From 32cdd8ec294f70506a8716ad9b1f69738ab262d8 Mon Sep 17 00:00:00 2001 From: "pyup.io vuln bot" Date: Sat, 30 Nov 2024 22:00:55 -0800 Subject: [PATCH] december update --- data/insecure_full.json | 57519 +++++++++++++++++++------------------- 1 file changed, 29112 insertions(+), 28407 deletions(-) diff --git a/data/insecure_full.json b/data/insecure_full.json index 99d41666..505bb898 100644 --- a/data/insecure_full.json +++ b/data/insecure_full.json @@ -2,7 +2,7 @@ "$meta": { "advisory": "PyUp.io metadata", "base_domain": "https://pyup.io", - "timestamp": 1730440847 + "timestamp": 1733032854 }, "10cent10": [ { @@ -291,6 +291,16 @@ ], "v": "<5.3.1" }, + { + "advisory": "Affected versions of Accesscontrol are vulnerable to untrusted access to AccessControl.userfolder.UserFolder.data.", + "cve": "PVE-2024-74011", + "id": "pyup.io-74011", + "more_info_path": "/vulnerabilities/PVE-2024-74011/74011", + "specs": [ + "<7.2" + ], + "v": "<7.2" + }, { "advisory": "Accesscontrol 4.3 and 5.3 include a fix for CVE-2021-32807: Remote Code Execution via unsafe classes in otherwise permitted modules .\r\nhttps://github.com/advisories/GHSA-qcx9-j53g-ccgf", "cve": "CVE-2021-32807", @@ -318,10 +328,10 @@ ], "acryl-datahub": [ { - "advisory": "DataHub's AuthServiceClient, particularly versions below 0.8.45, creates JSON strings using format strings with user-controlled data. This approach lets potential attackers alter these JSON strings and forward them to the backend, causing potential misuse and authentication bypasses. This could lead to the creation of system accounts, potentially resulting in full system compromise. This vulnerability was discovered and reported by the GitHub Security lab and is being tracked under GHSL-2022-081.\r\nhttps://github.com/datahub-project/datahub/security/advisories/GHSA-7wc6-p6c4-522c", - "cve": "CVE-2023-25561", - "id": "pyup.io-63339", - "more_info_path": "/vulnerabilities/CVE-2023-25561/63339", + "advisory": "DataHub under 0.8.45 frontend, acting as a proxy, is found to have a vulnerability where it doesn't properly construct URLs when forwarding data to the DataHub Metadata Store (GMS), potentially allowing external users to reroute requests from the DataHub Frontend to any host. This could enable attackers to reroute a request from the frontend proxy to any other server and return the result. The vulnerability was discovered and reported by the GitHub Security lab and is tracked as GHSL-2022-076.\r\nhttps://github.com/datahub-project/datahub/security/advisories/GHSA-5w2h-q83m-65xg", + "cve": "CVE-2023-25557", + "id": "pyup.io-63341", + "more_info_path": "/vulnerabilities/CVE-2023-25557/63341", "specs": [ "<0.8.45" ], @@ -338,10 +348,10 @@ "v": "<0.8.45" }, { - "advisory": "DataHub under 0.8.45 frontend, acting as a proxy, is found to have a vulnerability where it doesn't properly construct URLs when forwarding data to the DataHub Metadata Store (GMS), potentially allowing external users to reroute requests from the DataHub Frontend to any host. This could enable attackers to reroute a request from the frontend proxy to any other server and return the result. The vulnerability was discovered and reported by the GitHub Security lab and is tracked as GHSL-2022-076.\r\nhttps://github.com/datahub-project/datahub/security/advisories/GHSA-5w2h-q83m-65xg", - "cve": "CVE-2023-25557", - "id": "pyup.io-63341", - "more_info_path": "/vulnerabilities/CVE-2023-25557/63341", + "advisory": "DataHub's AuthServiceClient, particularly versions below 0.8.45, creates JSON strings using format strings with user-controlled data. This approach lets potential attackers alter these JSON strings and forward them to the backend, causing potential misuse and authentication bypasses. This could lead to the creation of system accounts, potentially resulting in full system compromise. This vulnerability was discovered and reported by the GitHub Security lab and is being tracked under GHSL-2022-081.\r\nhttps://github.com/datahub-project/datahub/security/advisories/GHSA-7wc6-p6c4-522c", + "cve": "CVE-2023-25561", + "id": "pyup.io-63339", + "more_info_path": "/vulnerabilities/CVE-2023-25561/63339", "specs": [ "<0.8.45" ], @@ -576,6 +586,19 @@ "v": "<3.0.0" } ], + "affinequant": [ + { + "advisory": "The OpenSSF Package Analysis project has identified the PyPI package 'affinequant' version 99.6 as malicious because it communicates with a domain associated with malicious activity, indicating potential security risks.", + "cve": "PVE-2024-74233", + "id": "pyup.io-74233", + "more_info_path": "/vulnerabilities/PVE-2024-74233/74233", + "specs": [ + ">=0", + "<=0" + ], + "v": ">=0,<=0" + } + ], "agentscope": [ { "advisory": "Affected versions of Agentscope are vulnerable to Code Injection. Agentscope does not implement security measures to isolate the execution of user-provided code, which could lead to the takeover of the server running the code.", @@ -765,9 +788,9 @@ "ai-python": [ { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41213", - "id": "pyup.io-43062", - "more_info_path": "/vulnerabilities/CVE-2021-41213/43062", + "cve": "CVE-2021-41220", + "id": "pyup.io-43070", + "more_info_path": "/vulnerabilities/CVE-2021-41220/43070", "specs": [ "<0.8.1" ], @@ -775,9 +798,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41208", - "id": "pyup.io-43071", - "more_info_path": "/vulnerabilities/CVE-2021-41208/43071", + "cve": "CVE-2021-41214", + "id": "pyup.io-43055", + "more_info_path": "/vulnerabilities/CVE-2021-41214/43055", "specs": [ "<0.8.1" ], @@ -785,9 +808,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41211", - "id": "pyup.io-43053", - "more_info_path": "/vulnerabilities/CVE-2021-41211/43053", + "cve": "CVE-2021-41222", + "id": "pyup.io-43065", + "more_info_path": "/vulnerabilities/CVE-2021-41222/43065", "specs": [ "<0.8.1" ], @@ -795,9 +818,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41209", - "id": "pyup.io-43061", - "more_info_path": "/vulnerabilities/CVE-2021-41209/43061", + "cve": "CVE-2021-41199", + "id": "pyup.io-43002", + "more_info_path": "/vulnerabilities/CVE-2021-41199/43002", "specs": [ "<0.8.1" ], @@ -805,9 +828,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41228", - "id": "pyup.io-43064", - "more_info_path": "/vulnerabilities/CVE-2021-41228/43064", + "cve": "CVE-2021-41207", + "id": "pyup.io-43075", + "more_info_path": "/vulnerabilities/CVE-2021-41207/43075", "specs": [ "<0.8.1" ], @@ -815,9 +838,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41217", - "id": "pyup.io-43054", - "more_info_path": "/vulnerabilities/CVE-2021-41217/43054", + "cve": "CVE-2021-41226", + "id": "pyup.io-43057", + "more_info_path": "/vulnerabilities/CVE-2021-41226/43057", "specs": [ "<0.8.1" ], @@ -825,9 +848,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41214", - "id": "pyup.io-43055", - "more_info_path": "/vulnerabilities/CVE-2021-41214/43055", + "cve": "CVE-2021-41212", + "id": "pyup.io-43074", + "more_info_path": "/vulnerabilities/CVE-2021-41212/43074", "specs": [ "<0.8.1" ], @@ -835,9 +858,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41219", - "id": "pyup.io-43056", - "more_info_path": "/vulnerabilities/CVE-2021-41219/43056", + "cve": "CVE-2021-41211", + "id": "pyup.io-43053", + "more_info_path": "/vulnerabilities/CVE-2021-41211/43053", "specs": [ "<0.8.1" ], @@ -845,9 +868,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41218", - "id": "pyup.io-43067", - "more_info_path": "/vulnerabilities/CVE-2021-41218/43067", + "cve": "CVE-2021-41198", + "id": "pyup.io-43080", + "more_info_path": "/vulnerabilities/CVE-2021-41198/43080", "specs": [ "<0.8.1" ], @@ -855,9 +878,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41216", - "id": "pyup.io-43068", - "more_info_path": "/vulnerabilities/CVE-2021-41216/43068", + "cve": "CVE-2021-41200", + "id": "pyup.io-43052", + "more_info_path": "/vulnerabilities/CVE-2021-41200/43052", "specs": [ "<0.8.1" ], @@ -865,9 +888,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41206", - "id": "pyup.io-43072", - "more_info_path": "/vulnerabilities/CVE-2021-41206/43072", + "cve": "CVE-2021-41216", + "id": "pyup.io-43068", + "more_info_path": "/vulnerabilities/CVE-2021-41216/43068", "specs": [ "<0.8.1" ], @@ -875,9 +898,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41201", - "id": "pyup.io-43077", - "more_info_path": "/vulnerabilities/CVE-2021-41201/43077", + "cve": "CVE-2021-41209", + "id": "pyup.io-43061", + "more_info_path": "/vulnerabilities/CVE-2021-41209/43061", "specs": [ "<0.8.1" ], @@ -885,19 +908,19 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41197", - "id": "pyup.io-43078", - "more_info_path": "/vulnerabilities/CVE-2021-41197/43078", + "cve": "CVE-2021-41224", + "id": "pyup.io-43066", + "more_info_path": "/vulnerabilities/CVE-2021-41224/43066", "specs": [ "<0.8.1" ], "v": "<0.8.1" }, { - "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41204", - "id": "pyup.io-43063", - "more_info_path": "/vulnerabilities/CVE-2021-41204/43063", + "advisory": "Ai-python 0.8.1 updates its dependency 'pillow' to v8.3.2 to include security fixes.", + "cve": "CVE-2021-34552", + "id": "pyup.io-43082", + "more_info_path": "/vulnerabilities/CVE-2021-34552/43082", "specs": [ "<0.8.1" ], @@ -905,9 +928,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41222", - "id": "pyup.io-43065", - "more_info_path": "/vulnerabilities/CVE-2021-41222/43065", + "cve": "CVE-2021-41203", + "id": "pyup.io-43051", + "more_info_path": "/vulnerabilities/CVE-2021-41203/43051", "specs": [ "<0.8.1" ], @@ -915,9 +938,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41224", - "id": "pyup.io-43066", - "more_info_path": "/vulnerabilities/CVE-2021-41224/43066", + "cve": "CVE-2021-41201", + "id": "pyup.io-43077", + "more_info_path": "/vulnerabilities/CVE-2021-41201/43077", "specs": [ "<0.8.1" ], @@ -925,9 +948,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41220", - "id": "pyup.io-43070", - "more_info_path": "/vulnerabilities/CVE-2021-41220/43070", + "cve": "CVE-2021-41197", + "id": "pyup.io-43078", + "more_info_path": "/vulnerabilities/CVE-2021-41197/43078", "specs": [ "<0.8.1" ], @@ -935,9 +958,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41207", - "id": "pyup.io-43075", - "more_info_path": "/vulnerabilities/CVE-2021-41207/43075", + "cve": "CVE-2021-41227", + "id": "pyup.io-43058", + "more_info_path": "/vulnerabilities/CVE-2021-41227/43058", "specs": [ "<0.8.1" ], @@ -945,19 +968,19 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41202", - "id": "pyup.io-43076", - "more_info_path": "/vulnerabilities/CVE-2021-41202/43076", + "cve": "CVE-2021-41225", + "id": "pyup.io-43059", + "more_info_path": "/vulnerabilities/CVE-2021-41225/43059", "specs": [ "<0.8.1" ], "v": "<0.8.1" }, { - "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41198", - "id": "pyup.io-43080", - "more_info_path": "/vulnerabilities/CVE-2021-41198/43080", + "advisory": "Ai-python 0.8.1 updates its dependency 'pillow' to v8.3.2 to include security fixes.", + "cve": "CVE-2021-23437", + "id": "pyup.io-43083", + "more_info_path": "/vulnerabilities/CVE-2021-23437/43083", "specs": [ "<0.8.1" ], @@ -965,9 +988,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41215", - "id": "pyup.io-43069", - "more_info_path": "/vulnerabilities/CVE-2021-41215/43069", + "cve": "CVE-2021-41221", + "id": "pyup.io-43060", + "more_info_path": "/vulnerabilities/CVE-2021-41221/43060", "specs": [ "<0.8.1" ], @@ -975,9 +998,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41210", - "id": "pyup.io-43081", - "more_info_path": "/vulnerabilities/CVE-2021-41210/43081", + "cve": "CVE-2021-41213", + "id": "pyup.io-43062", + "more_info_path": "/vulnerabilities/CVE-2021-41213/43062", "specs": [ "<0.8.1" ], @@ -985,9 +1008,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41196", - "id": "pyup.io-43050", - "more_info_path": "/vulnerabilities/CVE-2021-41196/43050", + "cve": "CVE-2021-41218", + "id": "pyup.io-43067", + "more_info_path": "/vulnerabilities/CVE-2021-41218/43067", "specs": [ "<0.8.1" ], @@ -995,9 +1018,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41225", - "id": "pyup.io-43059", - "more_info_path": "/vulnerabilities/CVE-2021-41225/43059", + "cve": "CVE-2021-41204", + "id": "pyup.io-43063", + "more_info_path": "/vulnerabilities/CVE-2021-41204/43063", "specs": [ "<0.8.1" ], @@ -1005,9 +1028,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41203", - "id": "pyup.io-43051", - "more_info_path": "/vulnerabilities/CVE-2021-41203/43051", + "cve": "CVE-2021-41206", + "id": "pyup.io-43072", + "more_info_path": "/vulnerabilities/CVE-2021-41206/43072", "specs": [ "<0.8.1" ], @@ -1015,9 +1038,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41212", - "id": "pyup.io-43074", - "more_info_path": "/vulnerabilities/CVE-2021-41212/43074", + "cve": "CVE-2021-41196", + "id": "pyup.io-43050", + "more_info_path": "/vulnerabilities/CVE-2021-41196/43050", "specs": [ "<0.8.1" ], @@ -1025,9 +1048,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41195", - "id": "pyup.io-43079", - "more_info_path": "/vulnerabilities/CVE-2021-41195/43079", + "cve": "CVE-2021-41228", + "id": "pyup.io-43064", + "more_info_path": "/vulnerabilities/CVE-2021-41228/43064", "specs": [ "<0.8.1" ], @@ -1035,9 +1058,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41200", - "id": "pyup.io-43052", - "more_info_path": "/vulnerabilities/CVE-2021-41200/43052", + "cve": "CVE-2021-41219", + "id": "pyup.io-43056", + "more_info_path": "/vulnerabilities/CVE-2021-41219/43056", "specs": [ "<0.8.1" ], @@ -1045,9 +1068,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41226", - "id": "pyup.io-43057", - "more_info_path": "/vulnerabilities/CVE-2021-41226/43057", + "cve": "CVE-2021-41208", + "id": "pyup.io-43071", + "more_info_path": "/vulnerabilities/CVE-2021-41208/43071", "specs": [ "<0.8.1" ], @@ -1055,9 +1078,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41227", - "id": "pyup.io-43058", - "more_info_path": "/vulnerabilities/CVE-2021-41227/43058", + "cve": "CVE-2021-41202", + "id": "pyup.io-43076", + "more_info_path": "/vulnerabilities/CVE-2021-41202/43076", "specs": [ "<0.8.1" ], @@ -1065,9 +1088,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41221", - "id": "pyup.io-43060", - "more_info_path": "/vulnerabilities/CVE-2021-41221/43060", + "cve": "CVE-2021-41217", + "id": "pyup.io-43054", + "more_info_path": "/vulnerabilities/CVE-2021-41217/43054", "specs": [ "<0.8.1" ], @@ -1075,9 +1098,9 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41205", - "id": "pyup.io-43073", - "more_info_path": "/vulnerabilities/CVE-2021-41205/43073", + "cve": "CVE-2021-41215", + "id": "pyup.io-43069", + "more_info_path": "/vulnerabilities/CVE-2021-41215/43069", "specs": [ "<0.8.1" ], @@ -1085,29 +1108,29 @@ }, { "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41199", - "id": "pyup.io-43002", - "more_info_path": "/vulnerabilities/CVE-2021-41199/43002", + "cve": "CVE-2021-41210", + "id": "pyup.io-43081", + "more_info_path": "/vulnerabilities/CVE-2021-41210/43081", "specs": [ "<0.8.1" ], "v": "<0.8.1" }, { - "advisory": "Ai-python 0.8.1 updates its dependency 'pillow' to v8.3.2 to include security fixes.", - "cve": "CVE-2021-34552", - "id": "pyup.io-43082", - "more_info_path": "/vulnerabilities/CVE-2021-34552/43082", + "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", + "cve": "CVE-2021-41195", + "id": "pyup.io-43079", + "more_info_path": "/vulnerabilities/CVE-2021-41195/43079", "specs": [ "<0.8.1" ], "v": "<0.8.1" }, { - "advisory": "Ai-python 0.8.1 updates its dependency 'pillow' to v8.3.2 to include security fixes.", - "cve": "CVE-2021-23437", - "id": "pyup.io-43083", - "more_info_path": "/vulnerabilities/CVE-2021-23437/43083", + "advisory": "Ai-python 0.8.1 updates its dependency 'tensorflow' to v2.6.1 to include security fixes.", + "cve": "CVE-2021-41205", + "id": "pyup.io-43073", + "more_info_path": "/vulnerabilities/CVE-2021-41205/43073", "specs": [ "<0.8.1" ], @@ -1178,20 +1201,20 @@ "v": "<1.6.5" }, { - "advisory": "Aiida-core 1.6.5 updates 'PyYAML' to v5.4 to fix critical security issues.", - "cve": "CVE-2020-14343", - "id": "pyup.io-43458", - "more_info_path": "/vulnerabilities/CVE-2020-14343/43458", + "advisory": "Aiida-core 1.6.5 updates its dependency 'pyyaml' to v5.4 to include security fixes.", + "cve": "CVE-2019-20477", + "id": "pyup.io-41169", + "more_info_path": "/vulnerabilities/CVE-2019-20477/41169", "specs": [ "<1.6.5" ], "v": "<1.6.5" }, { - "advisory": "Aiida-core 1.6.5 updates its dependency 'pyyaml' to v5.4 to include security fixes.", - "cve": "CVE-2019-20477", - "id": "pyup.io-41169", - "more_info_path": "/vulnerabilities/CVE-2019-20477/41169", + "advisory": "Aiida-core 1.6.5 updates 'PyYAML' to v5.4 to fix critical security issues.", + "cve": "CVE-2020-14343", + "id": "pyup.io-43458", + "more_info_path": "/vulnerabilities/CVE-2020-14343/43458", "specs": [ "<1.6.5" ], @@ -1213,9 +1236,9 @@ "aim": [ { "advisory": "Aim 1.2.13 updates its dependency 'pillow' to v6.2.2 to include security fixes.", - "cve": "CVE-2020-5311", - "id": "pyup.io-48613", - "more_info_path": "/vulnerabilities/CVE-2020-5311/48613", + "cve": "CVE-2020-5310", + "id": "pyup.io-48607", + "more_info_path": "/vulnerabilities/CVE-2020-5310/48607", "specs": [ "<1.2.13" ], @@ -1223,9 +1246,9 @@ }, { "advisory": "Aim 1.2.13 updates its dependency 'pillow' to v6.2.2 to include security fixes.", - "cve": "CVE-2020-5310", - "id": "pyup.io-48607", - "more_info_path": "/vulnerabilities/CVE-2020-5310/48607", + "cve": "CVE-2020-5312", + "id": "pyup.io-48614", + "more_info_path": "/vulnerabilities/CVE-2020-5312/48614", "specs": [ "<1.2.13" ], @@ -1233,9 +1256,9 @@ }, { "advisory": "Aim 1.2.13 updates its dependency 'pillow' to v6.2.2 to include security fixes.", - "cve": "CVE-2020-5312", - "id": "pyup.io-48614", - "more_info_path": "/vulnerabilities/CVE-2020-5312/48614", + "cve": "CVE-2020-5311", + "id": "pyup.io-48613", + "more_info_path": "/vulnerabilities/CVE-2020-5311/48613", "specs": [ "<1.2.13" ], @@ -1407,6 +1430,26 @@ ], "v": "<0.22.0" }, + { + "advisory": "Affected versions of aiohttp are vulnerable to HTTP Request Smuggling (CWE-444). This vulnerability allows attackers to inject malicious HTTP messages by including line feeds (LF) in chunk extensions, potentially bypassing security controls and executing unauthorized actions. The attack vector involves sending specially crafted chunked HTTP requests to exploit the improper parsing in the HttpPayloadParser class. To mitigate, upgrade to aiohttp version which validates chunk extensions by rejecting any containing unexpected LFs, thereby preventing request smuggling attacks.", + "cve": "CVE-2024-52304", + "id": "pyup.io-74251", + "more_info_path": "/vulnerabilities/CVE-2024-52304/74251", + "specs": [ + "<3.10.11" + ], + "v": "<3.10.11" + }, + { + "advisory": "Affected versions of aiohttp are vulnerable to Middleware Cache Pollution. This vulnerability allows attackers to potentially interfere with middleware handling by exploiting cached middleware associated with system routes. The impact includes possible bypassing of security middleware or unintended access to internal routes. The attack vector involves crafting requests that target system routes, causing the middleware cache to store and reuse inappropriate middleware configurations. The vulnerable methods are _build_middlewares and the middleware caching mechanism in web_app.py. To mitigate, upgrade to aiohttp version, which prevents system routes from polluting the middleware cache by excluding SystemRoute instances from caching.", + "cve": "CVE-2024-52303", + "id": "pyup.io-74252", + "more_info_path": "/vulnerabilities/CVE-2024-52303/74252", + "specs": [ + "<3.10.11" + ], + "v": "<3.10.11" + }, { "advisory": "Aiohttp 3.7.4 includes a fix for CVE-2021-21330: In aiohttp before version 3.7.4 there is an open redirect vulnerability. A maliciously crafted link to an aiohttp-based web-server could redirect the browser to a different website. It is caused by a bug in the 'aiohttp.web_middlewares.normalize_path_middleware' middleware. A workaround can be to avoid using 'aiohttp.web_middlewares.normalize_path_middleware' in your applications.\r\nhttps://github.com/aio-libs/aiohttp/security/advisories/GHSA-v6wp-4m6f-gcjg", "cve": "CVE-2021-21330", @@ -1438,20 +1481,20 @@ "v": "<3.8.0" }, { - "advisory": "Aiohttp 3.8.6 includes a fix for CVE-2023-47627: The HTTP parser in AIOHTTP has numerous problems with header parsing, which could lead to request smuggling. This parser is only used when AIOHTTP_NO_EXTENSIONS is enabled (or not using a prebuilt wheel).\r\nhttps://github.com/aio-libs/aiohttp/security/advisories/GHSA-gfw2-4jvh-wgfg", - "cve": "CVE-2023-47627", - "id": "pyup.io-62326", - "more_info_path": "/vulnerabilities/CVE-2023-47627/62326", + "advisory": "Aiohttp 3.8.6 updates vendored copy of 'llhttp' to v9.1.3 to include a security fix.\r\nhttps://github.com/aio-libs/aiohttp/security/advisories/GHSA-pjjw-qhg8-p2p9", + "cve": "PVE-2023-61657", + "id": "pyup.io-61657", + "more_info_path": "/vulnerabilities/PVE-2023-61657/61657", "specs": [ "<3.8.6" ], "v": "<3.8.6" }, { - "advisory": "Aiohttp 3.8.6 updates vendored copy of 'llhttp' to v9.1.3 to include a security fix.\r\nhttps://github.com/aio-libs/aiohttp/security/advisories/GHSA-pjjw-qhg8-p2p9", - "cve": "PVE-2023-61657", - "id": "pyup.io-61657", - "more_info_path": "/vulnerabilities/PVE-2023-61657/61657", + "advisory": "Aiohttp 3.8.6 includes a fix for CVE-2023-47627: The HTTP parser in AIOHTTP has numerous problems with header parsing, which could lead to request smuggling. This parser is only used when AIOHTTP_NO_EXTENSIONS is enabled (or not using a prebuilt wheel).\r\nhttps://github.com/aio-libs/aiohttp/security/advisories/GHSA-gfw2-4jvh-wgfg", + "cve": "CVE-2023-47627", + "id": "pyup.io-62326", + "more_info_path": "/vulnerabilities/CVE-2023-47627/62326", "specs": [ "<3.8.6" ], @@ -2003,20 +2046,20 @@ "v": "<=2.5.2" }, { - "advisory": "An issue in the YAML Python library of NASA AIT-Core allows attackers to execute arbitrary commands via supplying a crafted YAML file.", - "cve": "CVE-2024-35060", - "id": "pyup.io-71244", - "more_info_path": "/vulnerabilities/CVE-2024-35060/71244", + "advisory": "An issue in the Pickle Python library of NASA AIT-Core allows attackers to execute arbitrary commands.", + "cve": "CVE-2024-35059", + "id": "pyup.io-71243", + "more_info_path": "/vulnerabilities/CVE-2024-35059/71243", "specs": [ "<=2.5.2" ], "v": "<=2.5.2" }, { - "advisory": "An issue in the Pickle Python library of NASA AIT-Core allows attackers to execute arbitrary commands.", - "cve": "CVE-2024-35059", - "id": "pyup.io-71243", - "more_info_path": "/vulnerabilities/CVE-2024-35059/71243", + "advisory": "An issue in the YAML Python library of NASA AIT-Core allows attackers to execute arbitrary commands via supplying a crafted YAML file.", + "cve": "CVE-2024-35060", + "id": "pyup.io-71244", + "more_info_path": "/vulnerabilities/CVE-2024-35060/71244", "specs": [ "<=2.5.2" ], @@ -2234,9 +2277,9 @@ }, { "advisory": "Aldryn-django 3.2.12.0 updates its dependency 'django' to v3.2.12 to include security fixes.", - "cve": "CVE-2022-22818", - "id": "pyup.io-45167", - "more_info_path": "/vulnerabilities/CVE-2022-22818/45167", + "cve": "CVE-2022-23833", + "id": "pyup.io-45351", + "more_info_path": "/vulnerabilities/CVE-2022-23833/45351", "specs": [ "<3.2.12.0" ], @@ -2244,9 +2287,9 @@ }, { "advisory": "Aldryn-django 3.2.12.0 updates its dependency 'django' to v3.2.12 to include security fixes.", - "cve": "CVE-2022-23833", - "id": "pyup.io-45351", - "more_info_path": "/vulnerabilities/CVE-2022-23833/45351", + "cve": "CVE-2022-22818", + "id": "pyup.io-45167", + "more_info_path": "/vulnerabilities/CVE-2022-22818/45167", "specs": [ "<3.2.12.0" ], @@ -2921,20 +2964,20 @@ ], "anaplan-api": [ { - "advisory": "Anaplan-api 0.2.13 updates its cryptography dependency from version 42.0.6 to 42.0.8 to include a security fix for CVE-2024-4603.", - "cve": "CVE-2024-4603", - "id": "pyup.io-71674", - "more_info_path": "/vulnerabilities/CVE-2024-4603/71674", + "advisory": "Anaplan-api 0.2.13 updates its idna dependency from version 3.6 to 3.7 to address CVE-2024-3651.", + "cve": "CVE-2024-3651", + "id": "pyup.io-71679", + "more_info_path": "/vulnerabilities/CVE-2024-3651/71679", "specs": [ "<0.2.13" ], "v": "<0.2.13" }, { - "advisory": "Anaplan-api 0.2.13 updates its idna dependency from version 3.6 to 3.7 to address CVE-2024-3651.", - "cve": "CVE-2024-3651", - "id": "pyup.io-71679", - "more_info_path": "/vulnerabilities/CVE-2024-3651/71679", + "advisory": "Anaplan-api 0.2.13 updates its cryptography dependency from version 42.0.6 to 42.0.8 to include a security fix for CVE-2024-4603.", + "cve": "CVE-2024-4603", + "id": "pyup.io-71674", + "more_info_path": "/vulnerabilities/CVE-2024-4603/71674", "specs": [ "<0.2.13" ], @@ -3302,10 +3345,10 @@ "v": "<2.7.17,>=2.8.0a0,<2.8.11,>=2.9.0a0,<2.9.7" }, { - "advisory": "Ansible versions 2.7.17, 2.8.9 and 2.9.6 include a fix for CVE-2020-1735: A flaw was found in the Ansible Engine when the fetch module is used. An attacker could intercept the module, inject a new path, and then choose a new destination path on the controller node. All versions in 2.7.x, 2.8.x and 2.9.x branches are believed to be vulnerable.\r\nhttps://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-1735", - "cve": "CVE-2020-1735", - "id": "pyup.io-42877", - "more_info_path": "/vulnerabilities/CVE-2020-1735/42877", + "advisory": "Ansible versions 2.7.17, 2.8.9 and 2.9.6 include a fix for CVE-2020-1739: A flaw was found in Ansible 2.7.16 and prior, 2.8.8 and prior, and 2.9.5 and prior. When a password is set with the argument \"password\" of svn module, it is used on svn command line, disclosing to other users within the same node. An attacker could take advantage by reading the cmdline file from that particular PID on the procfs.\r\nhttps://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-1739", + "cve": "CVE-2020-1739", + "id": "pyup.io-42871", + "more_info_path": "/vulnerabilities/CVE-2020-1739/42871", "specs": [ "<2.7.17", ">=2.8.0a0,<2.8.9", @@ -3314,10 +3357,10 @@ "v": "<2.7.17,>=2.8.0a0,<2.8.9,>=2.9.0a0,<2.9.6" }, { - "advisory": "Ansible versions 2.7.17, 2.8.9 and 2.9.6 include a fix for CVE-2020-1739: A flaw was found in Ansible 2.7.16 and prior, 2.8.8 and prior, and 2.9.5 and prior. When a password is set with the argument \"password\" of svn module, it is used on svn command line, disclosing to other users within the same node. An attacker could take advantage by reading the cmdline file from that particular PID on the procfs.\r\nhttps://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-1739", - "cve": "CVE-2020-1739", - "id": "pyup.io-42871", - "more_info_path": "/vulnerabilities/CVE-2020-1739/42871", + "advisory": "Ansible versions 2.7.17, 2.8.9 and 2.9.6 include a fix for CVE-2020-1735: A flaw was found in the Ansible Engine when the fetch module is used. An attacker could intercept the module, inject a new path, and then choose a new destination path on the controller node. All versions in 2.7.x, 2.8.x and 2.9.x branches are believed to be vulnerable.\r\nhttps://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-1735", + "cve": "CVE-2020-1735", + "id": "pyup.io-42877", + "more_info_path": "/vulnerabilities/CVE-2020-1735/42877", "specs": [ "<2.7.17", ">=2.8.0a0,<2.8.9", @@ -3610,10 +3653,10 @@ "v": ">=2.7.0a0,<2.7.15,>=2.8.0a0,<2.8.7,>=2.9.0a0,<2.9.1" }, { - "advisory": "Ansible versions 2.7.17, 2.8.9 and 2.9.6 include a fix for CVE-2020-1736: A flaw was found in Ansible Engine when a file is moved using atomic_move primitive as the file mode cannot be specified. This sets the destination files world-readable if the destination file does not exist and if the file exists, the file could be changed to have less restrictive permissions before the move. This could lead to the disclosure of sensitive data. All versions in 2.7.x, 2.8.x and 2.9.x branches are believed to be vulnerable.\r\nhttps://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-1736", - "cve": "CVE-2020-1736", - "id": "pyup.io-42875", - "more_info_path": "/vulnerabilities/CVE-2020-1736/42875", + "advisory": "Ansible versions 2.7.17, 2.8.9 and 2.9.6 include a fix for CVE-2020-1738: A flaw was found in Ansible Engine when the module package or service is used and the parameter 'use' is not specified. If a previous task is executed with a malicious user, the module sent can be selected by the attacker using the ansible facts file. All versions in 2.7.x, 2.8.x and 2.9.x branches are believed to be vulnerable.\r\nhttps://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-1738", + "cve": "CVE-2020-1738", + "id": "pyup.io-42873", + "more_info_path": "/vulnerabilities/CVE-2020-1738/42873", "specs": [ ">=2.7.0a0,<2.7.17", ">=2.8.0a0,<2.8.9", @@ -3622,10 +3665,10 @@ "v": ">=2.7.0a0,<2.7.17,>=2.8.0a0,<2.8.9,>=2.9.0a0,<2.9.6" }, { - "advisory": "Ansible versions 2.7.17, 2.8.9 and 2.9.6 include a fix for CVE-2020-10684: A flaw was found in Ansible Engine, all versions 2.7.x, 2.8.x and 2.9.x prior to 2.7.17, 2.8.9 and 2.9.6 respectively, when using ansible_facts as a subkey of itself and promoting it to a variable when inject is enabled, overwriting the ansible_facts after the clean. An attacker could take advantage of this by altering the ansible_facts, such as ansible_hosts, users and any other key data which would lead into privilege escalation or code injection.\r\nhttps://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-10684", - "cve": "CVE-2020-10684", - "id": "pyup.io-42864", - "more_info_path": "/vulnerabilities/CVE-2020-10684/42864", + "advisory": "Ansible versions 2.7.17, 2.8.9 and 2.9.6 include a fix for CVE-2020-1736: A flaw was found in Ansible Engine when a file is moved using atomic_move primitive as the file mode cannot be specified. This sets the destination files world-readable if the destination file does not exist and if the file exists, the file could be changed to have less restrictive permissions before the move. This could lead to the disclosure of sensitive data. All versions in 2.7.x, 2.8.x and 2.9.x branches are believed to be vulnerable.\r\nhttps://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-1736", + "cve": "CVE-2020-1736", + "id": "pyup.io-42875", + "more_info_path": "/vulnerabilities/CVE-2020-1736/42875", "specs": [ ">=2.7.0a0,<2.7.17", ">=2.8.0a0,<2.8.9", @@ -3634,10 +3677,10 @@ "v": ">=2.7.0a0,<2.7.17,>=2.8.0a0,<2.8.9,>=2.9.0a0,<2.9.6" }, { - "advisory": "Ansible versions 2.7.17, 2.8.9 and 2.9.6 include a fix for CVE-2020-1738: A flaw was found in Ansible Engine when the module package or service is used and the parameter 'use' is not specified. If a previous task is executed with a malicious user, the module sent can be selected by the attacker using the ansible facts file. All versions in 2.7.x, 2.8.x and 2.9.x branches are believed to be vulnerable.\r\nhttps://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-1738", - "cve": "CVE-2020-1738", - "id": "pyup.io-42873", - "more_info_path": "/vulnerabilities/CVE-2020-1738/42873", + "advisory": "Ansible versions 2.7.17, 2.8.9 and 2.9.6 include a fix for CVE-2020-10684: A flaw was found in Ansible Engine, all versions 2.7.x, 2.8.x and 2.9.x prior to 2.7.17, 2.8.9 and 2.9.6 respectively, when using ansible_facts as a subkey of itself and promoting it to a variable when inject is enabled, overwriting the ansible_facts after the clean. An attacker could take advantage of this by altering the ansible_facts, such as ansible_hosts, users and any other key data which would lead into privilege escalation or code injection.\r\nhttps://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-10684", + "cve": "CVE-2020-10684", + "id": "pyup.io-42864", + "more_info_path": "/vulnerabilities/CVE-2020-10684/42864", "specs": [ ">=2.7.0a0,<2.7.17", ">=2.8.0a0,<2.8.9", @@ -3748,6 +3791,20 @@ ], "v": "<2.14.14,>=2.15.0b1,<2.15.9,>=2.16.0b1,<2.16.3" }, + { + "advisory": "Affected versions of Ansible are vulnerable to Incorrect Authorization (CWE-863). This flaw allows unprivileged users to silently create or replace any file on the system and assume ownership when a privileged user executes the user module against the unprivileged user's home directory. The attack requires the attacker to have traversal permissions on the directory containing the target file. To exploit, an attacker leverages these permissions to manipulate file contents.", + "cve": "CVE-2024-9902", + "id": "pyup.io-74221", + "more_info_path": "/vulnerabilities/CVE-2024-9902/74221", + "specs": [ + "<2.14.18rc1", + ">= 2.15.0b1,<2.15.13rc1", + ">= 2.16.0b1,< 2.16.13rc1", + ">= 2.17.0b1,< 2.17.6rc1", + ">= 2.18.0b1,< 2.18.0rc2" + ], + "v": "<2.14.18rc1,>= 2.15.0b1,<2.15.13rc1,>= 2.16.0b1,< 2.16.13rc1,>= 2.17.0b1,< 2.17.6rc1,>= 2.18.0b1,< 2.18.0rc2" + }, { "advisory": "Ansible-core 2.15.8 includes a fix for CVE-2023-5764: A template injection flaw was found in Ansible where a user's controller internal templating operations may remove the unsafe designation from template data. This issue could allow an attacker to use a specially crafted file to introduce code injection when supplying templating data.", "cve": "CVE-2023-5764", @@ -3759,14 +3816,28 @@ "v": "<2.15.8" }, { - "advisory": "A critical security vulnerability affects Ansible, impacting the handling of sensitive information stored in Ansible Vault files. The vulnerability occurs during playbook execution when using tasks like include_vars to load vaulted variables without setting the no_log: true parameter. This flaw causes sensitive data, including passwords and API keys, to be exposed in plaintext within playbook outputs or logs. Attackers who gain access to these outputs could potentially acquire secrets, leading to unauthorized access or actions on affected systems. Users must immediately review and update their Ansible playbooks to ensure proper use of the no_log: true parameter when handling vaulted variables. Additionally, users should audit recent playbook outputs and logs for potential secret exposure.", + "advisory": "Affected versions of Ansible-Core before the fix are vulnerable to Improper Input Validation (CWE-20). This vulnerability allows attackers to bypass content protections by exploiting the hostvars object to execute templated content, potentially leading to arbitrary code execution within playbooks. The attack vector involves crafting malicious templates that reference and execute unsafe content through hostvars in hostvars.py. Vulnerable functions include the templating methods that do not properly manage serialization with native Jinja. To mitigate, upgrade to Ansible-Core version which implements proper handling and serialization of hostvars, preventing arbitrary code execution.", + "cve": "CVE-2024-11079", + "id": "pyup.io-74261", + "more_info_path": "/vulnerabilities/CVE-2024-11079/74261", + "specs": [ + "<2.18.0" + ], + "v": "<2.18.0" + }, + { + "advisory": "A security vulnerability affects Ansible, impacting the handling of sensitive information stored in Ansible Vault files. The vulnerability occurs during playbook execution when using tasks like include_vars to load vaulted variables without setting the no_log: true parameter. This flaw causes sensitive data, including passwords and API keys, to be exposed in plaintext within playbook outputs or logs. Attackers who gain access to these outputs could potentially acquire secrets, leading to unauthorized access or actions on affected systems. Users must immediately review and update their Ansible playbooks to ensure proper use of the no_log: true parameter when handling vaulted variables. Additionally, users should audit recent playbook outputs and logs for potential secret exposure.", "cve": "CVE-2024-8775", "id": "pyup.io-73302", "more_info_path": "/vulnerabilities/CVE-2024-8775/73302", "specs": [ - ">=0" + "<2.18.0,>=2.18.0b1", + "<2.17.6,>=2.17.0b1", + "<2.16.13,>=2.16.0b1", + "<2.15.13,>=2.15.0b1", + "<2.14.18" ], - "v": ">=0" + "v": "<2.18.0,>=2.18.0b1,<2.17.6,>=2.17.0b1,<2.16.13,>=2.16.0b1,<2.15.13,>=2.15.0b1,<2.14.18" }, { "advisory": "An absolute path traversal attack exists in the Ansible automation platform. This flaw allows an attacker to craft a malicious Ansible role and make the victim execute the role. A symlink can be used to overwrite a file outside of the extraction path.", @@ -3802,6 +3873,16 @@ "<4.0.0" ], "v": "<4.0.0" + }, + { + "advisory": "Ansible-doctor 7.0.0 updates its dependency 'ansible-core' to v2.14.18 to include a security fix.", + "cve": "CVE-2024-9902", + "id": "pyup.io-74092", + "more_info_path": "/vulnerabilities/CVE-2024-9902/74092", + "specs": [ + "<7.0.0" + ], + "v": "<7.0.0" } ], "ansible-runner": [ @@ -3858,80 +3939,80 @@ "v": "<3.2.0" }, { - "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2020-1738.", - "cve": "CVE-2020-1738", - "id": "pyup.io-42874", - "more_info_path": "/vulnerabilities/CVE-2020-1738/42874", + "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2021-3447.", + "cve": "CVE-2021-3447", + "id": "pyup.io-42861", + "more_info_path": "/vulnerabilities/CVE-2021-3447/42861", "specs": [ "<3.2.0" ], "v": "<3.2.0" }, { - "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2020-1739.", - "cve": "CVE-2020-1739", - "id": "pyup.io-42872", - "more_info_path": "/vulnerabilities/CVE-2020-1739/42872", + "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2021-3583.", + "cve": "CVE-2021-3583", + "id": "pyup.io-42925", + "more_info_path": "/vulnerabilities/CVE-2021-3583/42925", "specs": [ "<3.2.0" ], "v": "<3.2.0" }, { - "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2020-1736.", - "cve": "CVE-2020-1736", - "id": "pyup.io-42876", - "more_info_path": "/vulnerabilities/CVE-2020-1736/42876", + "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2020-1740.", + "cve": "CVE-2020-1740", + "id": "pyup.io-42870", + "more_info_path": "/vulnerabilities/CVE-2020-1740/42870", "specs": [ "<3.2.0" ], "v": "<3.2.0" }, { - "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2021-3447.", - "cve": "CVE-2021-3447", - "id": "pyup.io-42861", - "more_info_path": "/vulnerabilities/CVE-2021-3447/42861", + "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2020-1738.", + "cve": "CVE-2020-1738", + "id": "pyup.io-42874", + "more_info_path": "/vulnerabilities/CVE-2020-1738/42874", "specs": [ "<3.2.0" ], "v": "<3.2.0" }, { - "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2020-1740.", - "cve": "CVE-2020-1740", - "id": "pyup.io-42870", - "more_info_path": "/vulnerabilities/CVE-2020-1740/42870", + "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2020-1736.", + "cve": "CVE-2020-1736", + "id": "pyup.io-42876", + "more_info_path": "/vulnerabilities/CVE-2020-1736/42876", "specs": [ "<3.2.0" ], "v": "<3.2.0" }, { - "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2020-1733.", - "cve": "CVE-2020-1733", - "id": "pyup.io-42880", - "more_info_path": "/vulnerabilities/CVE-2020-1733/42880", + "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2020-1735.", + "cve": "CVE-2020-1735", + "id": "pyup.io-42878", + "more_info_path": "/vulnerabilities/CVE-2020-1735/42878", "specs": [ "<3.2.0" ], "v": "<3.2.0" }, { - "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2020-1735.", - "cve": "CVE-2020-1735", - "id": "pyup.io-42878", - "more_info_path": "/vulnerabilities/CVE-2020-1735/42878", + "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2020-1739.", + "cve": "CVE-2020-1739", + "id": "pyup.io-42872", + "more_info_path": "/vulnerabilities/CVE-2020-1739/42872", "specs": [ "<3.2.0" ], "v": "<3.2.0" }, { - "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2021-3583.", - "cve": "CVE-2021-3583", - "id": "pyup.io-42925", - "more_info_path": "/vulnerabilities/CVE-2021-3583/42925", + "advisory": "Ansible-tower-cli versions 3.1.x and before use API v1, what implies the use of ansible-tower version 3.2.x or earlier. These are affected by CVE-2020-1733.", + "cve": "CVE-2020-1733", + "id": "pyup.io-42880", + "more_info_path": "/vulnerabilities/CVE-2020-1733/42880", "specs": [ "<3.2.0" ], @@ -4072,9 +4153,9 @@ }, { "advisory": "Ansys-tools-repo-sync 0.1.17 updates its dependency 'urllib3' to v1.26.9 to include security fixes.", - "cve": "CVE-2018-20060", - "id": "pyup.io-51115", - "more_info_path": "/vulnerabilities/CVE-2018-20060/51115", + "cve": "CVE-2021-33503", + "id": "pyup.io-51024", + "more_info_path": "/vulnerabilities/CVE-2021-33503/51024", "specs": [ "<0.1.17" ], @@ -4082,9 +4163,9 @@ }, { "advisory": "Ansys-tools-repo-sync 0.1.17 updates its dependency 'urllib3' to v1.26.9 to include security fixes.", - "cve": "CVE-2019-11324", - "id": "pyup.io-51113", - "more_info_path": "/vulnerabilities/CVE-2019-11324/51113", + "cve": "CVE-2018-20060", + "id": "pyup.io-51115", + "more_info_path": "/vulnerabilities/CVE-2018-20060/51115", "specs": [ "<0.1.17" ], @@ -4092,9 +4173,9 @@ }, { "advisory": "Ansys-tools-repo-sync 0.1.17 updates its dependency 'urllib3' to v1.26.9 to include security fixes.", - "cve": "CVE-2021-33503", - "id": "pyup.io-51024", - "more_info_path": "/vulnerabilities/CVE-2021-33503/51024", + "cve": "CVE-2019-11324", + "id": "pyup.io-51113", + "more_info_path": "/vulnerabilities/CVE-2019-11324/51113", "specs": [ "<0.1.17" ], @@ -4160,26 +4241,6 @@ } ], "ao3-poster": [ - { - "advisory": "Ao3-poster version 0.0.7 updates its dependency 'httplib2' to v0.19.0 to include security fixes.", - "cve": "CVE-2021-21240", - "id": "pyup.io-49127", - "more_info_path": "/vulnerabilities/CVE-2021-21240/49127", - "specs": [ - "<0.0.7" - ], - "v": "<0.0.7" - }, - { - "advisory": "Ao3-poster version 0.0.7 updates its dependency 'pygments' to v2.7.4 to include security fixes.", - "cve": "CVE-2021-27291", - "id": "pyup.io-49124", - "more_info_path": "/vulnerabilities/CVE-2021-27291/49124", - "specs": [ - "<0.0.7" - ], - "v": "<0.0.7" - }, { "advisory": "Ao3-poster version 0.0.7 updates its dependency 'rsa' to v4.7 to include security fixes.", "cve": "CVE-2020-25658", @@ -4191,20 +4252,20 @@ "v": "<0.0.7" }, { - "advisory": "Ao3-poster version 0.0.7 updates its dependency 'pygments' to v2.7.4 to include security fixes.", - "cve": "CVE-2021-20270", - "id": "pyup.io-49123", - "more_info_path": "/vulnerabilities/CVE-2021-20270/49123", + "advisory": "Ao3-poster version 0.0.7 updates its dependency 'httplib2' to v0.19.0 to include security fixes.", + "cve": "CVE-2020-11078", + "id": "pyup.io-49128", + "more_info_path": "/vulnerabilities/CVE-2020-11078/49128", "specs": [ "<0.0.7" ], "v": "<0.0.7" }, { - "advisory": "Ao3-poster version 0.0.7 updates its dependency 'httplib2' to v0.19.0 to include security fixes.", - "cve": "CVE-2020-11078", - "id": "pyup.io-49128", - "more_info_path": "/vulnerabilities/CVE-2020-11078/49128", + "advisory": "Ao3-poster version 0.0.7 updates its dependency 'pygments' to v2.7.4 to include security fixes.", + "cve": "CVE-2021-20270", + "id": "pyup.io-49123", + "more_info_path": "/vulnerabilities/CVE-2021-20270/49123", "specs": [ "<0.0.7" ], @@ -4229,6 +4290,26 @@ "<0.0.7" ], "v": "<0.0.7" + }, + { + "advisory": "Ao3-poster version 0.0.7 updates its dependency 'httplib2' to v0.19.0 to include security fixes.", + "cve": "CVE-2021-21240", + "id": "pyup.io-49127", + "more_info_path": "/vulnerabilities/CVE-2021-21240/49127", + "specs": [ + "<0.0.7" + ], + "v": "<0.0.7" + }, + { + "advisory": "Ao3-poster version 0.0.7 updates its dependency 'pygments' to v2.7.4 to include security fixes.", + "cve": "CVE-2021-27291", + "id": "pyup.io-49124", + "more_info_path": "/vulnerabilities/CVE-2021-27291/49124", + "specs": [ + "<0.0.7" + ], + "v": "<0.0.7" } ], "apache-age-python": [ @@ -4265,20 +4346,20 @@ "v": "<1.10.12" }, { - "advisory": "In Apache Airflow versions prior to 1.10.13, the Charts and Query View of the old (Flask-admin based) UI were vulnerable for SSRF attack. See CVE-2020-17513.", - "cve": "CVE-2020-17513", - "id": "pyup.io-39282", - "more_info_path": "/vulnerabilities/CVE-2020-17513/39282", + "advisory": "The \"origin\" parameter passed to some of the endpoints like '/trigger' was vulnerable to XSS exploit. This issue affects Apache Airflow versions prior to 1.10.13. This is same as CVE-2020-13944 but the implemented fix in Airflow 1.10.13 did not fix the issue completely.", + "cve": "CVE-2020-17515", + "id": "pyup.io-42326", + "more_info_path": "/vulnerabilities/CVE-2020-17515/42326", "specs": [ "<1.10.13" ], "v": "<1.10.13" }, { - "advisory": "The \"origin\" parameter passed to some of the endpoints like '/trigger' was vulnerable to XSS exploit. This issue affects Apache Airflow versions prior to 1.10.13. This is same as CVE-2020-13944 but the implemented fix in Airflow 1.10.13 did not fix the issue completely.", - "cve": "CVE-2020-17515", - "id": "pyup.io-42326", - "more_info_path": "/vulnerabilities/CVE-2020-17515/42326", + "advisory": "In Apache Airflow versions prior to 1.10.13, the Charts and Query View of the old (Flask-admin based) UI were vulnerable for SSRF attack. See CVE-2020-17513.", + "cve": "CVE-2020-17513", + "id": "pyup.io-39282", + "more_info_path": "/vulnerabilities/CVE-2020-17513/39282", "specs": [ "<1.10.13" ], @@ -4354,6 +4435,16 @@ ], "v": "<2.0.0b1" }, + { + "advisory": "Apache-airflow before 2.0.0b1 bundles the code for the Apache Spark provider integration. Therefore, it is affected by CVE-2023-28710.", + "cve": "CVE-2023-28710", + "id": "pyup.io-63173", + "more_info_path": "/vulnerabilities/CVE-2023-28710/63173", + "specs": [ + "<2.0.0b1" + ], + "v": "<2.0.0b1" + }, { "advisory": "Apache-airflow before 2.0.0b1 bundles the code for the Apache Hive provider integration. Therefore, it is affected by CVE-2023-25696.", "cve": "CVE-2023-25696", @@ -4365,10 +4456,10 @@ "v": "<2.0.0b1" }, { - "advisory": "Apache-airflow before 2.0.0b1 bundles the code for the Apache Spark provider integration. Therefore, it is affected by CVE-2023-28710.", - "cve": "CVE-2023-28710", - "id": "pyup.io-63173", - "more_info_path": "/vulnerabilities/CVE-2023-28710/63173", + "advisory": "Apache-airflow before 2.0.0b1 bundles the code for the Google Cloud provider integration. Therefore, it is affected by CVE-2023-25691.", + "cve": "CVE-2023-25691", + "id": "pyup.io-63175", + "more_info_path": "/vulnerabilities/CVE-2023-25691/63175", "specs": [ "<2.0.0b1" ], @@ -4404,16 +4495,6 @@ ], "v": "<2.0.0b1" }, - { - "advisory": "Apache-airflow before 2.0.0b1 bundles the code for the Google Cloud provider integration. Therefore, it is affected by CVE-2023-25691.", - "cve": "CVE-2023-25691", - "id": "pyup.io-63175", - "more_info_path": "/vulnerabilities/CVE-2023-25691/63175", - "specs": [ - "<2.0.0b1" - ], - "v": "<2.0.0b1" - }, { "advisory": "Apache-airflow before 2.0.0b1 bundles the code for the Apache Hive provider integration. Therefore, it is affected by CVE-2022-46421.", "cve": "CVE-2022-46421", @@ -4495,7 +4576,7 @@ "v": "<2.10.0" }, { - "advisory": "Apache Airflow affected versions contain a critical vulnerability in the example DAG file \"example_inlet_event_extra.py\". This flaw allows authenticated attackers with only DAG trigger permission to execute arbitrary commands on the Airflow worker. Users who have based their DAGs on this example may be at risk. It is strongly recommended to avoid exposing example DAGs in production environments. If exposure is necessary, upgrade immediately to Airflow version 2.10.1 or later, which patches this vulnerability. Additionally, review all DAGs derived from this example for similar security issues.", + "advisory": "Affected versions of Apache Airflowcontain a critical vulnerability in the example DAG file \"example_inlet_event_extra.py\". This flaw allows authenticated attackers with only DAG trigger permission to execute arbitrary commands on the Airflow worker. Users who have based their DAGs on this example may be at risk. It is strongly recommended to avoid exposing example DAGs in production environments. Additionally, review all DAGs derived from this example for similar security issues.", "cve": "CVE-2024-45498", "id": "pyup.io-73187", "more_info_path": "/vulnerabilities/CVE-2024-45498/73187", @@ -4514,6 +4595,26 @@ ], "v": "<2.10.1" }, + { + "advisory": "Apache Airflow affected versions have a vulnerability that can expose sensitive configuration variables in task logs. This allows DAG authors to unintentionally or intentionally log such variables, enabling unauthorized users to access critical data and potentially compromising Airflow deployments. Secrets are now masked in task logs to prevent this exposure. Additionally, if secret values might have been logged and logs aren't protected, it is recommended to update those secrets.", + "cve": "CVE-2024-45784", + "id": "pyup.io-74259", + "more_info_path": "/vulnerabilities/CVE-2024-45784/74259", + "specs": [ + "<2.10.3" + ], + "v": "<2.10.3" + }, + { + "advisory": "Affected versions of Apache Airflow are vulnerable to Exposure of Sensitive Information (CWE-201). This vulnerability allows authenticated users with audit log access to view sensitive configuration variables in task logs by setting them via the CLI. The attack vector involves executing CLI commands that store sensitive variables unencrypted in audit logs within cli.py, enabling unauthorized access to critical data. To mitigate, upgrade to Airflow version which masks secrets in task logs, preventing the exposure of sensitive configuration data. Additionally, users should manually delete any previously logged secret variables from the log table.", + "cve": "CVE-2024-50378", + "id": "pyup.io-74262", + "more_info_path": "/vulnerabilities/CVE-2024-50378/74262", + "specs": [ + "<2.10.3" + ], + "v": "<2.10.3" + }, { "advisory": "Apache-airflow 2.2.5 includes a fix for a Race Condition vulnerability.\r\nhttps://github.com/apache/airflow/pull/20699", "cve": "PVE-2023-60199", @@ -4525,10 +4626,10 @@ "v": "<2.2.5" }, { - "advisory": "Apache-airflow 2.3.0 updates its NPM dependency 'datatables.net' to versions ^1.10.23 to include a security fix.", - "cve": "CVE-2021-23445", - "id": "pyup.io-48604", - "more_info_path": "/vulnerabilities/CVE-2021-23445/48604", + "advisory": "Apache-airflow 2.3.0 updates its NPM dependency 'tar' requirement to '>=6.1.9' to include security fixes.", + "cve": "CVE-2021-37713", + "id": "pyup.io-48618", + "more_info_path": "/vulnerabilities/CVE-2021-37713/48618", "specs": [ "<2.3.0" ], @@ -4545,10 +4646,10 @@ "v": "<2.3.0" }, { - "advisory": "Apache-airflow 2.3.0 updates its NPM dependency 'tar' requirement to '>=6.1.9' to include security fixes.", - "cve": "CVE-2021-37701", - "id": "pyup.io-48616", - "more_info_path": "/vulnerabilities/CVE-2021-37701/48616", + "advisory": "Apache-airflow 2.3.0 updates its NPM dependency 'datatables.net' to versions ^1.10.23 to include a security fix.", + "cve": "CVE-2021-23445", + "id": "pyup.io-48604", + "more_info_path": "/vulnerabilities/CVE-2021-23445/48604", "specs": [ "<2.3.0" ], @@ -4556,29 +4657,29 @@ }, { "advisory": "Apache-airflow 2.3.0 updates its NPM dependency 'tar' requirement to '>=6.1.9' to include security fixes.", - "cve": "CVE-2021-37713", - "id": "pyup.io-48618", - "more_info_path": "/vulnerabilities/CVE-2021-37713/48618", + "cve": "CVE-2021-37701", + "id": "pyup.io-48616", + "more_info_path": "/vulnerabilities/CVE-2021-37701/48616", "specs": [ "<2.3.0" ], "v": "<2.3.0" }, { - "advisory": "Affected version of Apache-airflow are vulnerable to Privilege Context Switching Error. File Task Handler should apply different permissions to log files generated by Airflow in order to handle impersonation.", - "cve": "CVE-2023-25754", - "id": "pyup.io-62916", - "more_info_path": "/vulnerabilities/CVE-2023-25754/62916", + "advisory": "The details page for task instances in the user interface is subject to a stored XSS vulnerability. This problem pertains to Apache Airflow versions prior to 2.6.0.\r\nhttps://github.com/apache/airflow/pull/30447/commits/3d894f7a643e9319f5c48e343ac39b248dedaa9b\r\nhttps://github.com/apache/airflow/pull/30779/commits/7c566f7ef4b33175aafc4f89f94fb2096b093940", + "cve": "CVE-2023-29247", + "id": "pyup.io-63344", + "more_info_path": "/vulnerabilities/CVE-2023-29247/63344", "specs": [ "<2.6.0" ], "v": "<2.6.0" }, { - "advisory": "The details page for task instances in the user interface is subject to a stored XSS vulnerability. This problem pertains to Apache Airflow versions prior to 2.6.0.\r\nhttps://github.com/apache/airflow/pull/30447/commits/3d894f7a643e9319f5c48e343ac39b248dedaa9b\r\nhttps://github.com/apache/airflow/pull/30779/commits/7c566f7ef4b33175aafc4f89f94fb2096b093940", - "cve": "CVE-2023-29247", - "id": "pyup.io-63344", - "more_info_path": "/vulnerabilities/CVE-2023-29247/63344", + "advisory": "Affected version of Apache-airflow are vulnerable to Privilege Context Switching Error. File Task Handler should apply different permissions to log files generated by Airflow in order to handle impersonation.", + "cve": "CVE-2023-25754", + "id": "pyup.io-62916", + "more_info_path": "/vulnerabilities/CVE-2023-25754/62916", "specs": [ "<2.6.0" ], @@ -4595,20 +4696,20 @@ "v": "<2.6.0" }, { - "advisory": "Apache Airflow, versions before 2.6.3, is affected by a vulnerability that allows an attacker to perform unauthorized file access outside the intended directory structure by manipulating the run_id parameter. This vulnerability is considered low since it requires an authenticated user to exploit it. It is recommended to upgrade to a version that is not affected", - "cve": "CVE-2023-22887", - "id": "pyup.io-62890", - "more_info_path": "/vulnerabilities/CVE-2023-22887/62890", + "advisory": "Apache Airflow affected versions have a vulnerability where an authenticated user can use crafted input to make the current request hang. It is recommended to upgrade to a version that is not affected", + "cve": "CVE-2023-36543", + "id": "pyup.io-71687", + "more_info_path": "/vulnerabilities/CVE-2023-36543/71687", "specs": [ "<2.6.3" ], "v": "<2.6.3" }, { - "advisory": "Apache Airflow affected versions are affected by a vulnerability that allows an unauthorized actor to gain access to sensitive information in Connection edit view. This vulnerability is considered low since it requires someone with access to Connection resources specifically updating the connection to exploit it.", - "cve": "CVE-2022-46651", - "id": "pyup.io-71689", - "more_info_path": "/vulnerabilities/CVE-2022-46651/71689", + "advisory": "Apache Airflow affected versions are affected by a vulnerability that allows unauthorized read access to a DAG through the URL.", + "cve": "CVE-2023-35908", + "id": "pyup.io-71688", + "more_info_path": "/vulnerabilities/CVE-2023-35908/71688", "specs": [ "<2.6.3" ], @@ -4625,20 +4726,30 @@ "v": "<2.6.3" }, { - "advisory": "Apache Airflow affected versions have a vulnerability where an authenticated user can use crafted input to make the current request hang. It is recommended to upgrade to a version that is not affected", - "cve": "CVE-2023-36543", - "id": "pyup.io-71687", - "more_info_path": "/vulnerabilities/CVE-2023-36543/71687", + "advisory": "Apache Airflow affected versions are affected by a vulnerability that allows an unauthorized actor to gain access to sensitive information in Connection edit view. This vulnerability is considered low since it requires someone with access to Connection resources specifically updating the connection to exploit it.", + "cve": "CVE-2022-46651", + "id": "pyup.io-71689", + "more_info_path": "/vulnerabilities/CVE-2022-46651/71689", "specs": [ "<2.6.3" ], "v": "<2.6.3" }, { - "advisory": "Apache Airflow affected versions are affected by a vulnerability that allows unauthorized read access to a DAG through the URL.", - "cve": "CVE-2023-35908", - "id": "pyup.io-71688", - "more_info_path": "/vulnerabilities/CVE-2023-35908/71688", + "advisory": "Apache Airflow, versions before 2.6.3, is affected by a vulnerability that allows an attacker to perform unauthorized file access outside the intended directory structure by manipulating the run_id parameter. This vulnerability is considered low since it requires an authenticated user to exploit it. It is recommended to upgrade to a version that is not affected", + "cve": "CVE-2023-22887", + "id": "pyup.io-62890", + "more_info_path": "/vulnerabilities/CVE-2023-22887/62890", + "specs": [ + "<2.6.3" + ], + "v": "<2.6.3" + }, + { + "advisory": "Apache Airflow, versions before 2.6.3, has a vulnerability where an authenticated user can use crafted input to make the current request hang. It is recommended to upgrade to a version that is not affected", + "cve": "PVE-2024-99900", + "id": "pyup.io-64989", + "more_info_path": "/vulnerabilities/PVE-2024-99900/64989", "specs": [ "<2.6.3" ], @@ -4655,14 +4766,14 @@ "v": "<2.6.3" }, { - "advisory": "Apache Airflow, versions before 2.6.3, has a vulnerability where an authenticated user can use crafted input to make the current request hang. It is recommended to upgrade to a version that is not affected", - "cve": "PVE-2024-99900", - "id": "pyup.io-64989", - "more_info_path": "/vulnerabilities/PVE-2024-99900/64989", + "advisory": "Apache-airflow 2.7.0 disables default allowing the testing of connections in UI, API and CLI for security reasons.\r\nhttps://github.com/apache/airflow/pull/32052", + "cve": "PVE-2023-60952", + "id": "pyup.io-60952", + "more_info_path": "/vulnerabilities/PVE-2023-60952/60952", "specs": [ - "<2.6.3" + "<2.7.0" ], - "v": "<2.6.3" + "v": "<2.7.0" }, { "advisory": "Apache-airflow 2.7.0 disables support for the deserialize flag by default in the 'xcomEntries' API. This was an unsafe default, as deserialization may instantiates arbitrary objects.\r\nhttps://github.com/apache/airflow/pull/32176", @@ -4704,16 +4815,6 @@ ], "v": "<2.7.0" }, - { - "advisory": "Apache-airflow 2.7.0 disables default allowing the testing of connections in UI, API and CLI for security reasons.\r\nhttps://github.com/apache/airflow/pull/32052", - "cve": "PVE-2023-60952", - "id": "pyup.io-60952", - "more_info_path": "/vulnerabilities/PVE-2023-60952/60952", - "specs": [ - "<2.7.0" - ], - "v": "<2.7.0" - }, { "advisory": "Versions of Apache Airflow are susceptible to a vulnerability permitting authenticated and DAG-view authorized users to manipulate certain DAG run detail values, like configuration parameters and start dates, through note submission.", "cve": "CVE-2023-40611", @@ -4734,16 +4835,6 @@ ], "v": "<2.7.1" }, - { - "advisory": "Apache Airflow contains a vulnerability where an authorized user with limited permissions can access task instance information across unintended DAGs, posing a risk to versions prior to 2.7.2. Users are encouraged to upgrade to mitigate this security risk.", - "cve": "CVE-2023-42663", - "id": "pyup.io-65393", - "more_info_path": "/vulnerabilities/CVE-2023-42663/65393", - "specs": [ - "<2.7.2" - ], - "v": "<2.7.2" - }, { "advisory": "A security vulnerability exists in versions of Apache Airflow that enables an authenticated user with limited permissions to potentially alter DAG resources they should not have access to, by crafting specific requests. This flaw could lead to unauthorized modification of DAGs, compromising the integrity of those processes.", "cve": "CVE-2023-42792", @@ -4765,14 +4856,14 @@ "v": "<2.7.2" }, { - "advisory": "Apache Airflow, versions before 2.7.3, has a vulnerability that allows an authorized user who has access to read specific DAGs only, to read information about task instances in other DAGs.\u00a0 This is a different issue than CVE-2023-42663 but leading to similar outcome.\r\nUsers of Apache Airflow are advised to upgrade to version 2.7.3 or newer to mitigate the risk associated with this vulnerability.", - "cve": "CVE-2023-42781", - "id": "pyup.io-65391", - "more_info_path": "/vulnerabilities/CVE-2023-42781/65391", + "advisory": "Apache Airflow contains a vulnerability where an authorized user with limited permissions can access task instance information across unintended DAGs, posing a risk to versions prior to 2.7.2. Users are encouraged to upgrade to mitigate this security risk.", + "cve": "CVE-2023-42663", + "id": "pyup.io-65393", + "more_info_path": "/vulnerabilities/CVE-2023-42663/65393", "specs": [ - "<2.7.3" + "<2.7.2" ], - "v": "<2.7.3" + "v": "<2.7.2" }, { "advisory": "Compromising versions of Apache Airflow allow authenticated and DAG-view authorized users to inappropriately modify DAG run detail values, including configuration parameters and start dates.", @@ -4784,6 +4875,26 @@ ], "v": "<2.7.3" }, + { + "advisory": "Apache Airflow, versions before 2.7.3, has a vulnerability that allows an authorized user who has access to read specific DAGs only, to read information about task instances in other DAGs.\u00a0 This is a different issue than CVE-2023-42663 but leading to similar outcome.\r\nUsers of Apache Airflow are advised to upgrade to version 2.7.3 or newer to mitigate the risk associated with this vulnerability.", + "cve": "CVE-2023-42781", + "id": "pyup.io-65391", + "more_info_path": "/vulnerabilities/CVE-2023-42781/65391", + "specs": [ + "<2.7.3" + ], + "v": "<2.7.3" + }, + { + "advisory": "Affected versions of Apache Airflow allow authenticated Ops and Viewers users to view all information on audit logs, including dag names and usernames they were not permitted to view.\u00a0In versions 2.8.2 and newer, Ops and Viewer users do not have audit log permission by default, they need to be explicitly granted permissions to see the logs. Only admin users have audit log permission by default.", + "cve": "CVE-2024-26280", + "id": "pyup.io-68489", + "more_info_path": "/vulnerabilities/CVE-2024-26280/68489", + "specs": [ + "<2.8.2rc1" + ], + "v": "<2.8.2rc1" + }, { "advisory": "Apache Airflow affected versions have a vulnerability related to improper preservation of permissions. The local file task handler incorrectly sets write permissions for all parent folders of the log folder, potentially adding write access to the Unix group. This is particularly problematic if Airflow is run as the root user, potentially impacting SSH operations if log files are stored in the home directory. This issue does not affect users of Official Airflow Docker images. Affected users should upgrade to version 2.8.4 or above, change the file task handler permissions, or ensure their umask is set to 002.", "cve": "CVE-2024-29735", @@ -4835,20 +4946,20 @@ "v": "<2.9.3" }, { - "advisory": "Apache-airflow 2.3.2 and prior versions ship with vulnerable dependencies (click == 7.1.2).", - "cve": "PVE-2022-47833", - "id": "pyup.io-49785", - "more_info_path": "/vulnerabilities/PVE-2022-47833/49785", + "advisory": "Apache-airflow 2.3.2 and prior versions ship with vulnerable dependencies (wtforms == 2.3.3).", + "cve": "PVE-2021-42852", + "id": "pyup.io-49787", + "more_info_path": "/vulnerabilities/PVE-2021-42852/49787", "specs": [ "<=2.3.2" ], "v": "<=2.3.2" }, { - "advisory": "Apache-airflow 2.3.2 and prior versions ship with vulnerable dependencies (wtforms == 2.3.3).", - "cve": "PVE-2021-42852", - "id": "pyup.io-49787", - "more_info_path": "/vulnerabilities/PVE-2021-42852/49787", + "advisory": "Apache-airflow 2.3.2 and prior versions ship with vulnerable dependencies (click == 7.1.2).", + "cve": "PVE-2022-47833", + "id": "pyup.io-49785", + "more_info_path": "/vulnerabilities/PVE-2022-47833/49785", "specs": [ "<=2.3.2" ], @@ -4884,16 +4995,6 @@ ], "v": ">=0,<1.10.11" }, - { - "advisory": "An issue was found in Apache Airflow versions 1.10.10 and below. When using CeleryExecutor, if an attacker can connect to the broker (Redis, RabbitMQ) directly, it is possible to inject commands, resulting in the celery worker running arbitrary commands.", - "cve": "CVE-2020-11981", - "id": "pyup.io-54177", - "more_info_path": "/vulnerabilities/CVE-2020-11981/54177", - "specs": [ - ">=0,<1.10.11rc1" - ], - "v": ">=0,<1.10.11rc1" - }, { "advisory": "An issue was found in Apache Airflow versions 1.10.10 and below. When using CeleryExecutor, if an attack can connect to the broker (Redis, RabbitMQ) directly, it was possible to insert a malicious payload directly to the broker which could lead to a deserialization attack (and thus remote code execution) on the Worker.", "cve": "CVE-2020-11982", @@ -4915,20 +5016,30 @@ "v": ">=0,<1.10.11rc1" }, { - "advisory": "Apache-airflow 1.10.11rc1 includes a fix for CVE-2020-11978: A remote code/command injection vulnerability was discovered in one of the example DAGs shipped with Airflow which would allow any authenticated user to run arbitrary commands as the user running airflow worker/scheduler (depending on the executor in use). If you already have examples disabled by setting load_examples=False in the config then you are not vulnerable.", - "cve": "CVE-2020-11978", - "id": "pyup.io-54349", - "more_info_path": "/vulnerabilities/CVE-2020-11978/54349", + "advisory": "An issue was found in Apache Airflow versions 1.10.10 and below. It was discovered that many of the admin management screens in the new/RBAC UI handled escaping incorrectly, allowing authenticated users with appropriate permissions to create stored XSS attacks.", + "cve": "CVE-2020-11983", + "id": "pyup.io-54181", + "more_info_path": "/vulnerabilities/CVE-2020-11983/54181", "specs": [ ">=0,<1.10.11rc1" ], "v": ">=0,<1.10.11rc1" }, { - "advisory": "An issue was found in Apache Airflow versions 1.10.10 and below. It was discovered that many of the admin management screens in the new/RBAC UI handled escaping incorrectly, allowing authenticated users with appropriate permissions to create stored XSS attacks.", - "cve": "CVE-2020-11983", - "id": "pyup.io-54181", - "more_info_path": "/vulnerabilities/CVE-2020-11983/54181", + "advisory": "An issue was found in Apache Airflow versions 1.10.10 and below. When using CeleryExecutor, if an attacker can connect to the broker (Redis, RabbitMQ) directly, it is possible to inject commands, resulting in the celery worker running arbitrary commands.", + "cve": "CVE-2020-11981", + "id": "pyup.io-54177", + "more_info_path": "/vulnerabilities/CVE-2020-11981/54177", + "specs": [ + ">=0,<1.10.11rc1" + ], + "v": ">=0,<1.10.11rc1" + }, + { + "advisory": "Apache-airflow 1.10.11rc1 includes a fix for CVE-2020-11978: A remote code/command injection vulnerability was discovered in one of the example DAGs shipped with Airflow which would allow any authenticated user to run arbitrary commands as the user running airflow worker/scheduler (depending on the executor in use). If you already have examples disabled by setting load_examples=False in the config then you are not vulnerable.", + "cve": "CVE-2020-11978", + "id": "pyup.io-54349", + "more_info_path": "/vulnerabilities/CVE-2020-11978/54349", "specs": [ ">=0,<1.10.11rc1" ], @@ -5035,20 +5146,20 @@ "v": ">=0,<1.9.0" }, { - "advisory": "In Apache Airflow 1.8.2 and earlier, an experimental Airflow feature displayed authenticated cookies, as well as passwords to databases used by Airflow. An attacker who has limited access to airflow, whether it be via XSS or by leaving a machine unlocked can exfiltrate all credentials from the system.", - "cve": "CVE-2017-17836", - "id": "pyup.io-53950", - "more_info_path": "/vulnerabilities/CVE-2017-17836/53950", + "advisory": "In Apache Airflow 1.8.2 and earlier, an authenticated user can execute code remotely on the Airflow webserver by creating a special object.\r\nhttps://github.com/apache/airflow/pull/2132", + "cve": "CVE-2017-15720", + "id": "pyup.io-53938", + "more_info_path": "/vulnerabilities/CVE-2017-15720/53938", "specs": [ ">=0,<1.9.0" ], "v": ">=0,<1.9.0" }, { - "advisory": "In Apache Airflow 1.8.2 and earlier, an authenticated user can execute code remotely on the Airflow webserver by creating a special object.\r\nhttps://github.com/apache/airflow/pull/2132", - "cve": "CVE-2017-15720", - "id": "pyup.io-53938", - "more_info_path": "/vulnerabilities/CVE-2017-15720/53938", + "advisory": "In Apache Airflow 1.8.2 and earlier, an experimental Airflow feature displayed authenticated cookies, as well as passwords to databases used by Airflow. An attacker who has limited access to airflow, whether it be via XSS or by leaving a machine unlocked can exfiltrate all credentials from the system.", + "cve": "CVE-2017-17836", + "id": "pyup.io-53950", + "more_info_path": "/vulnerabilities/CVE-2017-17836/53950", "specs": [ ">=0,<1.9.0" ], @@ -5075,40 +5186,40 @@ "v": ">=0,<2.2.4rc1" }, { - "advisory": "Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection') vulnerability in Apache Airflow Spark Provider, Apache Airflow allows an attacker to read arbtrary files in the task execution context, without write access to DAG files. This issue affects Spark Provider versions prior to 4.0.0. It also impacts any Apache Airflow versions prior to 2.3.0 in case Spark Provider is installed (Spark Provider 4.0.0 can only be installed for Airflow 2.3.0+). Note that you need to manually install the Spark Provider version 4.0.0 in order to get rid of the vulnerability on top of Airflow 2.3.0+ version that has lower version of the Spark Provider installed).", - "cve": "CVE-2022-40954", - "id": "pyup.io-54588", - "more_info_path": "/vulnerabilities/CVE-2022-40954/54588", + "advisory": "Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection') vulnerability in Apache Airflow Pinot Provider, Apache Airflow allows an attacker to control commands executed in the task execution context, without write access to DAG files. This issue affects Apache Airflow Pinot Provider versions prior to 4.0.0. It also impacts any Apache Airflow versions prior to 2.3.0 in case Apache Airflow Pinot Provider is installed (Apache Airflow Pinot Provider 4.0.0 can only be installed for Airflow 2.3.0+). Note that you need to manually install the Pinot Provider version 4.0.0 in order to get rid of the vulnerability on top of Airflow 2.3.0+ version.", + "cve": "CVE-2022-38649", + "id": "pyup.io-54586", + "more_info_path": "/vulnerabilities/CVE-2022-38649/54586", "specs": [ ">=0,<2.3.0" ], "v": ">=0,<2.3.0" }, { - "advisory": "Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection') vulnerability in Apache Airflow Pig Provider, Apache Airflow allows an attacker to control commands executed in the task execution context, without write access to DAG files. This issue affects Pig Provider versions prior to 4.0.0. It also impacts any Apache Airflow versions prior to 2.3.0 in case Pig Provider is installed (Pig Provider 4.0.0 can only be installed for Airflow 2.3.0+). Note that you need to manually install the Pig Provider version 4.0.0 in order to get rid of the vulnerability on top of Airflow 2.3.0+ version.", - "cve": "CVE-2022-40189", - "id": "pyup.io-54587", - "more_info_path": "/vulnerabilities/CVE-2022-40189/54587", + "advisory": "Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection') vulnerability in Apache Airflow Spark Provider, Apache Airflow allows an attacker to read arbtrary files in the task execution context, without write access to DAG files. This issue affects Spark Provider versions prior to 4.0.0. It also impacts any Apache Airflow versions prior to 2.3.0 in case Spark Provider is installed (Spark Provider 4.0.0 can only be installed for Airflow 2.3.0+). Note that you need to manually install the Spark Provider version 4.0.0 in order to get rid of the vulnerability on top of Airflow 2.3.0+ version that has lower version of the Spark Provider installed).", + "cve": "CVE-2022-40954", + "id": "pyup.io-54588", + "more_info_path": "/vulnerabilities/CVE-2022-40954/54588", "specs": [ ">=0,<2.3.0" ], "v": ">=0,<2.3.0" }, { - "advisory": "Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection') vulnerability in Apache Airflow Pinot Provider, Apache Airflow allows an attacker to control commands executed in the task execution context, without write access to DAG files. This issue affects Apache Airflow Pinot Provider versions prior to 4.0.0. It also impacts any Apache Airflow versions prior to 2.3.0 in case Apache Airflow Pinot Provider is installed (Apache Airflow Pinot Provider 4.0.0 can only be installed for Airflow 2.3.0+). Note that you need to manually install the Pinot Provider version 4.0.0 in order to get rid of the vulnerability on top of Airflow 2.3.0+ version.", - "cve": "CVE-2022-38649", - "id": "pyup.io-54586", - "more_info_path": "/vulnerabilities/CVE-2022-38649/54586", + "advisory": "Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection') vulnerability in Apache Airflow Hive Provider, Apache Airflow allows an attacker to execute arbtrary commands in the task execution context, without write access to DAG files. This issue affects Hive Provider versions prior to 4.1.0. It also impacts any Apache Airflow versions prior to 2.3.0 in case HIve Provider is installed (Hive Provider 4.1.0 can only be installed for Airflow 2.3.0+). Note that you need to manually install the HIve Provider version 4.1.0 in order to get rid of the vulnerability on top of Airflow 2.3.0+ version that has lower version of the Hive Provider installed).", + "cve": "CVE-2022-41131", + "id": "pyup.io-54592", + "more_info_path": "/vulnerabilities/CVE-2022-41131/54592", "specs": [ ">=0,<2.3.0" ], "v": ">=0,<2.3.0" }, { - "advisory": "Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection') vulnerability in Apache Airflow Hive Provider, Apache Airflow allows an attacker to execute arbtrary commands in the task execution context, without write access to DAG files. This issue affects Hive Provider versions prior to 4.1.0. It also impacts any Apache Airflow versions prior to 2.3.0 in case HIve Provider is installed (Hive Provider 4.1.0 can only be installed for Airflow 2.3.0+). Note that you need to manually install the HIve Provider version 4.1.0 in order to get rid of the vulnerability on top of Airflow 2.3.0+ version that has lower version of the Hive Provider installed).", - "cve": "CVE-2022-41131", - "id": "pyup.io-54592", - "more_info_path": "/vulnerabilities/CVE-2022-41131/54592", + "advisory": "Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection') vulnerability in Apache Airflow Pig Provider, Apache Airflow allows an attacker to control commands executed in the task execution context, without write access to DAG files. This issue affects Pig Provider versions prior to 4.0.0. It also impacts any Apache Airflow versions prior to 2.3.0 in case Pig Provider is installed (Pig Provider 4.0.0 can only be installed for Airflow 2.3.0+). Note that you need to manually install the Pig Provider version 4.0.0 in order to get rid of the vulnerability on top of Airflow 2.3.0+ version.", + "cve": "CVE-2022-40189", + "id": "pyup.io-54587", + "more_info_path": "/vulnerabilities/CVE-2022-40189/54587", "specs": [ ">=0,<2.3.0" ], @@ -5234,16 +5345,6 @@ ], "v": ">=0,<2.8.0b1" }, - { - "advisory": "Apache Airflow, versions before 2.8.1, have a vulnerability that allows a potential attacker to poison the XCom data by bypassing the protection of \"enable_xcom_pickling=False\" configuration setting resulting in poisoned data after XCom deserialization. This vulnerability is considered low since it requires a DAG author to exploit it. Users are recommended to upgrade to version 2.8.1 or later, which fixes this issue.", - "cve": "CVE-2023-50943", - "id": "pyup.io-65264", - "more_info_path": "/vulnerabilities/CVE-2023-50943/65264", - "specs": [ - ">=0,<2.8.1" - ], - "v": ">=0,<2.8.1" - }, { "advisory": "Apache Airflow, versions before 2.8.1, have a vulnerability that allows an authenticated user to access the source code of a DAG to which they don't have access.\u00a0This vulnerability is considered low since it requires an authenticated user to exploit it. Users are recommended to upgrade to version 2.8.1, which fixes this issue.", "cve": "CVE-2023-50944", @@ -5255,14 +5356,14 @@ "v": ">=0,<2.8.1" }, { - "advisory": "** DISPUTED ** Apache Airflow, versions before 2.8.2, has a vulnerability that allows authenticated Ops and Viewers users to view all information on audit logs, including dag names and usernames they were not permitted to view.\u00a0With 2.8.2 and newer, Ops and Viewer users do not have audit log permission by default, they need to be explicitly granted permissions to see the logs. Only admin users have audit log permission by default. Users of Apache Airflow are recommended to upgrade to version 2.8.2 or newer to mitigate the risk associated with this vulnerability.", - "cve": "CVE-2024-26280", - "id": "pyup.io-68489", - "more_info_path": "/vulnerabilities/CVE-2024-26280/68489", + "advisory": "Apache Airflow, versions before 2.8.1, have a vulnerability that allows a potential attacker to poison the XCom data by bypassing the protection of \"enable_xcom_pickling=False\" configuration setting resulting in poisoned data after XCom deserialization. This vulnerability is considered low since it requires a DAG author to exploit it. Users are recommended to upgrade to version 2.8.1 or later, which fixes this issue.", + "cve": "CVE-2023-50943", + "id": "pyup.io-65264", + "more_info_path": "/vulnerabilities/CVE-2023-50943/65264", "specs": [ - ">=0,<2.8.2" + ">=0,<2.8.1" ], - "v": ">=0,<2.8.2" + "v": ">=0,<2.8.1" }, { "advisory": "** DISPUTED ** Apache Airflow is affected by a vulnerability impacting versions before 2.8.2, where authenticated users can access DAG code and import errors for DAGs without required permissions via the API and UI. To mitigate this risk, upgrading to version 2.8.2 or newer is recommended.", @@ -5307,20 +5408,20 @@ "v": ">=1.10.0,<2.7.0" }, { - "advisory": "The lineage endpoint of the deprecated Experimental API was not protected by authentication in Airflow 2.0.0. This allowed unauthenticated users to hit that endpoint. This is low-severity issue as the attacker needs to be aware of certain parameters to pass to that endpoint and even after can just get some metadata about a DAG and a Task. This issue only affects Apache Airflow 2.0.0.", - "cve": "CVE-2021-26697", - "id": "pyup.io-54461", - "more_info_path": "/vulnerabilities/CVE-2021-26697/54461", + "advisory": "Improper Access Control on Configurations Endpoint for the Stable API of Apache Airflow allows users with Viewer or User role to get Airflow Configurations including sensitive information even when `[webserver] expose_config` is set to `False` in `airflow.cfg`. This allowed a privilege escalation attack. This issue affects Apache Airflow 2.0.0.", + "cve": "CVE-2021-26559", + "id": "pyup.io-54168", + "more_info_path": "/vulnerabilities/CVE-2021-26559/54168", "specs": [ ">=2.0.0,<2.0.1" ], "v": ">=2.0.0,<2.0.1" }, { - "advisory": "Improper Access Control on Configurations Endpoint for the Stable API of Apache Airflow allows users with Viewer or User role to get Airflow Configurations including sensitive information even when `[webserver] expose_config` is set to `False` in `airflow.cfg`. This allowed a privilege escalation attack. This issue affects Apache Airflow 2.0.0.", - "cve": "CVE-2021-26559", - "id": "pyup.io-54168", - "more_info_path": "/vulnerabilities/CVE-2021-26559/54168", + "advisory": "The lineage endpoint of the deprecated Experimental API was not protected by authentication in Airflow 2.0.0. This allowed unauthenticated users to hit that endpoint. This is low-severity issue as the attacker needs to be aware of certain parameters to pass to that endpoint and even after can just get some metadata about a DAG and a Task. This issue only affects Apache Airflow 2.0.0.", + "cve": "CVE-2021-26697", + "id": "pyup.io-54461", + "more_info_path": "/vulnerabilities/CVE-2021-26697/54461", "specs": [ ">=2.0.0,<2.0.1" ], @@ -5450,29 +5551,9 @@ "apache-airflow-backport-providers-amazon": [ { "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2020-7753", - "id": "pyup.io-49914", - "more_info_path": "/vulnerabilities/CVE-2020-7753/49914", - "specs": [ - "<=2021.3.3" - ], - "v": "<=2021.3.3" - }, - { - "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2022-24776", - "id": "pyup.io-49922", - "more_info_path": "/vulnerabilities/CVE-2022-24776/49922", - "specs": [ - "<=2021.3.3" - ], - "v": "<=2021.3.3" - }, - { - "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37701", - "id": "pyup.io-49915", - "more_info_path": "/vulnerabilities/CVE-2021-37701/49915", + "cve": "CVE-2023-25754", + "id": "pyup.io-62919", + "more_info_path": "/vulnerabilities/CVE-2023-25754/62919", "specs": [ "<=2021.3.3" ], @@ -5480,19 +5561,19 @@ }, { "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37712", - "id": "pyup.io-49916", - "more_info_path": "/vulnerabilities/CVE-2021-37712/49916", + "cve": "CVE-2021-35936", + "id": "pyup.io-49920", + "more_info_path": "/vulnerabilities/CVE-2021-35936/49920", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2021-32805", - "id": "pyup.io-49923", - "more_info_path": "/vulnerabilities/CVE-2021-32805/49923", + "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (urllib3 == 1.25.11).", + "cve": "CVE-2021-33503", + "id": "pyup.io-49927", + "more_info_path": "/vulnerabilities/CVE-2021-33503/49927", "specs": [ "<=2021.3.3" ], @@ -5509,20 +5590,20 @@ "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2022-21659", - "id": "pyup.io-49925", - "more_info_path": "/vulnerabilities/CVE-2022-21659/49925", + "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (click == 7.1.2).", + "cve": "PVE-2022-47833", + "id": "pyup.io-49928", + "more_info_path": "/vulnerabilities/PVE-2022-47833/49928", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (click == 7.1.2).", - "cve": "PVE-2022-47833", - "id": "pyup.io-49928", - "more_info_path": "/vulnerabilities/PVE-2022-47833/49928", + "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-23445", + "id": "pyup.io-49918", + "more_info_path": "/vulnerabilities/CVE-2021-23445/49918", "specs": [ "<=2021.3.3" ], @@ -5539,20 +5620,20 @@ "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-35936", - "id": "pyup.io-49920", - "more_info_path": "/vulnerabilities/CVE-2021-35936/49920", + "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", + "cve": "CVE-2022-29217", + "id": "pyup.io-49929", + "more_info_path": "/vulnerabilities/CVE-2022-29217/49929", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37713", - "id": "pyup.io-49917", - "more_info_path": "/vulnerabilities/CVE-2021-37713/49917", + "advisory": "Apache-airflow-backport-providers-amazon <=2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-33502", + "id": "pyup.io-49919", + "more_info_path": "/vulnerabilities/CVE-2021-33502/49919", "specs": [ "<=2021.3.3" ], @@ -5560,19 +5641,19 @@ }, { "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-23445", - "id": "pyup.io-49918", - "more_info_path": "/vulnerabilities/CVE-2021-23445/49918", + "cve": "CVE-2020-7753", + "id": "pyup.io-49914", + "more_info_path": "/vulnerabilities/CVE-2020-7753/49914", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2023-25754", - "id": "pyup.io-62919", - "more_info_path": "/vulnerabilities/CVE-2023-25754/62919", + "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", + "cve": "CVE-2021-32805", + "id": "pyup.io-49923", + "more_info_path": "/vulnerabilities/CVE-2021-32805/49923", "specs": [ "<=2021.3.3" ], @@ -5589,30 +5670,50 @@ "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", - "cve": "CVE-2022-29217", - "id": "pyup.io-49929", - "more_info_path": "/vulnerabilities/CVE-2022-29217/49929", + "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", + "cve": "CVE-2022-24776", + "id": "pyup.io-49922", + "more_info_path": "/vulnerabilities/CVE-2022-24776/49922", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-amazon <=2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-33502", - "id": "pyup.io-49919", - "more_info_path": "/vulnerabilities/CVE-2021-33502/49919", + "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-37713", + "id": "pyup.io-49917", + "more_info_path": "/vulnerabilities/CVE-2021-37713/49917", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (urllib3 == 1.25.11).", - "cve": "CVE-2021-33503", - "id": "pyup.io-49927", - "more_info_path": "/vulnerabilities/CVE-2021-33503/49927", + "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-37712", + "id": "pyup.io-49916", + "more_info_path": "/vulnerabilities/CVE-2021-37712/49916", + "specs": [ + "<=2021.3.3" + ], + "v": "<=2021.3.3" + }, + { + "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-37701", + "id": "pyup.io-49915", + "more_info_path": "/vulnerabilities/CVE-2021-37701/49915", + "specs": [ + "<=2021.3.3" + ], + "v": "<=2021.3.3" + }, + { + "advisory": "Apache-airflow-backport-providers-amazon 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", + "cve": "CVE-2022-21659", + "id": "pyup.io-49925", + "more_info_path": "/vulnerabilities/CVE-2022-21659/49925", "specs": [ "<=2021.3.3" ], @@ -5700,9 +5801,9 @@ "apache-airflow-backport-providers-cncf-kubernetes": [ { "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2021-41265", - "id": "pyup.io-49940", - "more_info_path": "/vulnerabilities/CVE-2021-41265/49940", + "cve": "CVE-2021-32805", + "id": "pyup.io-49939", + "more_info_path": "/vulnerabilities/CVE-2021-32805/49939", "specs": [ "<=2021.3.3" ], @@ -5719,20 +5820,10 @@ "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", - "cve": "CVE-2022-29217", - "id": "pyup.io-49944", - "more_info_path": "/vulnerabilities/CVE-2022-29217/49944", - "specs": [ - "<=2021.3.3" - ], - "v": "<=2021.3.3" - }, - { - "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37712", - "id": "pyup.io-49932", - "more_info_path": "/vulnerabilities/CVE-2021-37712/49932", + "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (urllib3 == 1.25.11).", + "cve": "CVE-2021-33503", + "id": "pyup.io-49943", + "more_info_path": "/vulnerabilities/CVE-2021-33503/49943", "specs": [ "<=2021.3.3" ], @@ -5760,39 +5851,39 @@ }, { "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2021-32805", - "id": "pyup.io-49939", - "more_info_path": "/vulnerabilities/CVE-2021-32805/49939", + "cve": "CVE-2022-24776", + "id": "pyup.io-49938", + "more_info_path": "/vulnerabilities/CVE-2022-24776/49938", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2022-24776", - "id": "pyup.io-49938", - "more_info_path": "/vulnerabilities/CVE-2022-24776/49938", + "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-33502", + "id": "pyup.io-49935", + "more_info_path": "/vulnerabilities/CVE-2021-33502/49935", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37701", - "id": "pyup.io-49931", - "more_info_path": "/vulnerabilities/CVE-2021-37701/49931", + "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (flask-caching == 1.3.3).", + "cve": "CVE-2021-33026", + "id": "pyup.io-49942", + "more_info_path": "/vulnerabilities/CVE-2021-33026/49942", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37713", - "id": "pyup.io-49933", - "more_info_path": "/vulnerabilities/CVE-2021-37713/49933", + "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", + "cve": "CVE-2022-29217", + "id": "pyup.io-49944", + "more_info_path": "/vulnerabilities/CVE-2022-29217/49944", "specs": [ "<=2021.3.3" ], @@ -5800,19 +5891,19 @@ }, { "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-33502", - "id": "pyup.io-49935", - "more_info_path": "/vulnerabilities/CVE-2021-33502/49935", + "cve": "CVE-2021-37712", + "id": "pyup.io-49932", + "more_info_path": "/vulnerabilities/CVE-2021-37712/49932", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (flask-caching == 1.3.3).", - "cve": "CVE-2021-33026", - "id": "pyup.io-49942", - "more_info_path": "/vulnerabilities/CVE-2021-33026/49942", + "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2020-7753", + "id": "pyup.io-49930", + "more_info_path": "/vulnerabilities/CVE-2020-7753/49930", "specs": [ "<=2021.3.3" ], @@ -5839,10 +5930,10 @@ "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2020-7753", - "id": "pyup.io-49930", - "more_info_path": "/vulnerabilities/CVE-2020-7753/49930", + "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", + "cve": "CVE-2021-41265", + "id": "pyup.io-49940", + "more_info_path": "/vulnerabilities/CVE-2021-41265/49940", "specs": [ "<=2021.3.3" ], @@ -5859,10 +5950,10 @@ "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (urllib3 == 1.25.11).", - "cve": "CVE-2021-33503", - "id": "pyup.io-49943", - "more_info_path": "/vulnerabilities/CVE-2021-33503/49943", + "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-23445", + "id": "pyup.io-49934", + "more_info_path": "/vulnerabilities/CVE-2021-23445/49934", "specs": [ "<=2021.3.3" ], @@ -5870,9 +5961,19 @@ }, { "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-23445", - "id": "pyup.io-49934", - "more_info_path": "/vulnerabilities/CVE-2021-23445/49934", + "cve": "CVE-2021-37713", + "id": "pyup.io-49933", + "more_info_path": "/vulnerabilities/CVE-2021-37713/49933", + "specs": [ + "<=2021.3.3" + ], + "v": "<=2021.3.3" + }, + { + "advisory": "Apache-airflow-backport-providers-cncf-kubernetes 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-37701", + "id": "pyup.io-49931", + "more_info_path": "/vulnerabilities/CVE-2021-37701/49931", "specs": [ "<=2021.3.3" ], @@ -5973,10 +6074,10 @@ "v": "<=2020.6.24" }, { - "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2021-41265", - "id": "pyup.io-49956", - "more_info_path": "/vulnerabilities/CVE-2021-41265/49956", + "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-37712", + "id": "pyup.io-49948", + "more_info_path": "/vulnerabilities/CVE-2021-37712/49948", "specs": [ "<=2020.6.24" ], @@ -5984,9 +6085,9 @@ }, { "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37712", - "id": "pyup.io-49948", - "more_info_path": "/vulnerabilities/CVE-2021-37712/49948", + "cve": "CVE-2021-37713", + "id": "pyup.io-49949", + "more_info_path": "/vulnerabilities/CVE-2021-37713/49949", "specs": [ "<=2020.6.24" ], @@ -6013,60 +6114,60 @@ "v": "<=2020.6.24" }, { - "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2020-7753", - "id": "pyup.io-49946", - "more_info_path": "/vulnerabilities/CVE-2020-7753/49946", + "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (flask-caching == 1.3.3).", + "cve": "CVE-2021-33026", + "id": "pyup.io-49958", + "more_info_path": "/vulnerabilities/CVE-2021-33026/49958", "specs": [ "<=2020.6.24" ], "v": "<=2020.6.24" }, { - "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2021-32805", - "id": "pyup.io-49955", - "more_info_path": "/vulnerabilities/CVE-2021-32805/49955", + "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-33502", + "id": "pyup.io-49951", + "more_info_path": "/vulnerabilities/CVE-2021-33502/49951", "specs": [ "<=2020.6.24" ], "v": "<=2020.6.24" }, { - "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2022-21659", - "id": "pyup.io-49957", - "more_info_path": "/vulnerabilities/CVE-2022-21659/49957", + "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (urllib3 == 1.25.11).", + "cve": "CVE-2021-33503", + "id": "pyup.io-49959", + "more_info_path": "/vulnerabilities/CVE-2021-33503/49959", "specs": [ "<=2020.6.24" ], "v": "<=2020.6.24" }, { - "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37713", - "id": "pyup.io-49949", - "more_info_path": "/vulnerabilities/CVE-2021-37713/49949", + "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", + "cve": "CVE-2021-32805", + "id": "pyup.io-49955", + "more_info_path": "/vulnerabilities/CVE-2021-32805/49955", "specs": [ "<=2020.6.24" ], "v": "<=2020.6.24" }, { - "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (flask-caching == 1.3.3).", - "cve": "CVE-2021-33026", - "id": "pyup.io-49958", - "more_info_path": "/vulnerabilities/CVE-2021-33026/49958", + "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", + "cve": "CVE-2022-29217", + "id": "pyup.io-49960", + "more_info_path": "/vulnerabilities/CVE-2022-29217/49960", "specs": [ "<=2020.6.24" ], "v": "<=2020.6.24" }, { - "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-23445", - "id": "pyup.io-49950", - "more_info_path": "/vulnerabilities/CVE-2021-23445/49950", + "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", + "cve": "CVE-2021-41265", + "id": "pyup.io-49956", + "more_info_path": "/vulnerabilities/CVE-2021-41265/49956", "specs": [ "<=2020.6.24" ], @@ -6083,30 +6184,30 @@ "v": "<=2020.6.24" }, { - "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-33502", - "id": "pyup.io-49951", - "more_info_path": "/vulnerabilities/CVE-2021-33502/49951", + "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", + "cve": "CVE-2022-21659", + "id": "pyup.io-49957", + "more_info_path": "/vulnerabilities/CVE-2022-21659/49957", "specs": [ "<=2020.6.24" ], "v": "<=2020.6.24" }, { - "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (urllib3 == 1.25.11).", - "cve": "CVE-2021-33503", - "id": "pyup.io-49959", - "more_info_path": "/vulnerabilities/CVE-2021-33503/49959", + "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2020-7753", + "id": "pyup.io-49946", + "more_info_path": "/vulnerabilities/CVE-2020-7753/49946", "specs": [ "<=2020.6.24" ], "v": "<=2020.6.24" }, { - "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", - "cve": "CVE-2022-29217", - "id": "pyup.io-49960", - "more_info_path": "/vulnerabilities/CVE-2022-29217/49960", + "advisory": "Apache-airflow-backport-providers-email 2020.6.24 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-23445", + "id": "pyup.io-49950", + "more_info_path": "/vulnerabilities/CVE-2021-23445/49950", "specs": [ "<=2020.6.24" ], @@ -6210,16 +6311,6 @@ } ], "apache-airflow-backport-providers-microsoft-azure": [ - { - "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.13 and prior versions ship with vulnerable dependencies (flask-caching == 1.3.3).", - "cve": "CVE-2021-33026", - "id": "pyup.io-49974", - "more_info_path": "/vulnerabilities/CVE-2021-33026/49974", - "specs": [ - "<=2021.3.13" - ], - "v": "<=2021.3.13" - }, { "advisory": "apache-airflow-backport-providers-microsoft-azure 2021.3.13 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", "cve": "CVE-2023-25754", @@ -6231,14 +6322,14 @@ "v": "<=2021.3.13" }, { - "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37701", - "id": "pyup.io-49963", - "more_info_path": "/vulnerabilities/CVE-2021-37701/49963", + "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.13 and prior versions ship with vulnerable dependencies (flask-caching == 1.3.3).", + "cve": "CVE-2021-33026", + "id": "pyup.io-49974", + "more_info_path": "/vulnerabilities/CVE-2021-33026/49974", "specs": [ - "<=2021.3.3" + "<=2021.3.13" ], - "v": "<=2021.3.3" + "v": "<=2021.3.13" }, { "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", @@ -6262,19 +6353,29 @@ }, { "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2021-32805", - "id": "pyup.io-49971", - "more_info_path": "/vulnerabilities/CVE-2021-32805/49971", + "cve": "CVE-2021-41265", + "id": "pyup.io-49972", + "more_info_path": "/vulnerabilities/CVE-2021-41265/49972", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2022-24776", - "id": "pyup.io-49970", - "more_info_path": "/vulnerabilities/CVE-2022-24776/49970", + "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-37712", + "id": "pyup.io-49964", + "more_info_path": "/vulnerabilities/CVE-2021-37712/49964", + "specs": [ + "<=2021.3.3" + ], + "v": "<=2021.3.3" + }, + { + "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-23445", + "id": "pyup.io-49966", + "more_info_path": "/vulnerabilities/CVE-2021-23445/49966", "specs": [ "<=2021.3.3" ], @@ -6282,9 +6383,9 @@ }, { "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2022-21659", - "id": "pyup.io-49973", - "more_info_path": "/vulnerabilities/CVE-2022-21659/49973", + "cve": "CVE-2021-29621", + "id": "pyup.io-49969", + "more_info_path": "/vulnerabilities/CVE-2021-29621/49969", "specs": [ "<=2021.3.3" ], @@ -6292,29 +6393,29 @@ }, { "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2021-41265", - "id": "pyup.io-49972", - "more_info_path": "/vulnerabilities/CVE-2021-41265/49972", + "cve": "CVE-2021-32805", + "id": "pyup.io-49971", + "more_info_path": "/vulnerabilities/CVE-2021-32805/49971", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-35936", - "id": "pyup.io-49968", - "more_info_path": "/vulnerabilities/CVE-2021-35936/49968", + "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", + "cve": "CVE-2022-29217", + "id": "pyup.io-49976", + "more_info_path": "/vulnerabilities/CVE-2022-29217/49976", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37712", - "id": "pyup.io-49964", - "more_info_path": "/vulnerabilities/CVE-2021-37712/49964", + "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (urllib3 == 1.25.11).", + "cve": "CVE-2021-33503", + "id": "pyup.io-49975", + "more_info_path": "/vulnerabilities/CVE-2021-33503/49975", "specs": [ "<=2021.3.3" ], @@ -6322,9 +6423,9 @@ }, { "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37713", - "id": "pyup.io-49965", - "more_info_path": "/vulnerabilities/CVE-2021-37713/49965", + "cve": "CVE-2021-35936", + "id": "pyup.io-49968", + "more_info_path": "/vulnerabilities/CVE-2021-35936/49968", "specs": [ "<=2021.3.3" ], @@ -6332,9 +6433,9 @@ }, { "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-23445", - "id": "pyup.io-49966", - "more_info_path": "/vulnerabilities/CVE-2021-23445/49966", + "cve": "CVE-2020-7753", + "id": "pyup.io-49962", + "more_info_path": "/vulnerabilities/CVE-2020-7753/49962", "specs": [ "<=2021.3.3" ], @@ -6342,39 +6443,39 @@ }, { "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2021-29621", - "id": "pyup.io-49969", - "more_info_path": "/vulnerabilities/CVE-2021-29621/49969", + "cve": "CVE-2022-24776", + "id": "pyup.io-49970", + "more_info_path": "/vulnerabilities/CVE-2022-24776/49970", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", - "cve": "CVE-2022-29217", - "id": "pyup.io-49976", - "more_info_path": "/vulnerabilities/CVE-2022-29217/49976", + "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-37713", + "id": "pyup.io-49965", + "more_info_path": "/vulnerabilities/CVE-2021-37713/49965", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (urllib3 == 1.25.11).", - "cve": "CVE-2021-33503", - "id": "pyup.io-49975", - "more_info_path": "/vulnerabilities/CVE-2021-33503/49975", + "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-37701", + "id": "pyup.io-49963", + "more_info_path": "/vulnerabilities/CVE-2021-37701/49963", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2020-7753", - "id": "pyup.io-49962", - "more_info_path": "/vulnerabilities/CVE-2020-7753/49962", + "advisory": "Apache-airflow-backport-providers-microsoft-azure 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", + "cve": "CVE-2022-21659", + "id": "pyup.io-49973", + "more_info_path": "/vulnerabilities/CVE-2022-21659/49973", "specs": [ "<=2021.3.3" ], @@ -6634,16 +6735,6 @@ } ], "apache-airflow-backport-providers-slack": [ - { - "advisory": "Apache-airflow-backport-providers-slack 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2021-41265", - "id": "pyup.io-49988", - "more_info_path": "/vulnerabilities/CVE-2021-41265/49988", - "specs": [ - "<=2021.3.3" - ], - "v": "<=2021.3.3" - }, { "advisory": "Apache-airflow-backport-providers-slack 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", "cve": "CVE-2021-37701", @@ -6674,16 +6765,6 @@ ], "v": "<=2021.3.3" }, - { - "advisory": "Apache-airflow-backport-providers-slack 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2022-21659", - "id": "pyup.io-49989", - "more_info_path": "/vulnerabilities/CVE-2022-21659/49989", - "specs": [ - "<=2021.3.3" - ], - "v": "<=2021.3.3" - }, { "advisory": "Apache-airflow-backport-providers-slack 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", "cve": "CVE-2022-24776", @@ -6695,10 +6776,10 @@ "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-slack 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37713", - "id": "pyup.io-49981", - "more_info_path": "/vulnerabilities/CVE-2021-37713/49981", + "advisory": "Apache-airflow-backport-providers-slack 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", + "cve": "CVE-2022-21659", + "id": "pyup.io-49989", + "more_info_path": "/vulnerabilities/CVE-2022-21659/49989", "specs": [ "<=2021.3.3" ], @@ -6724,16 +6805,6 @@ ], "v": "<=2021.3.3" }, - { - "advisory": "Apache-airflow-backport-providers-slack 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37712", - "id": "pyup.io-49980", - "more_info_path": "/vulnerabilities/CVE-2021-37712/49980", - "specs": [ - "<=2021.3.3" - ], - "v": "<=2021.3.3" - }, { "advisory": "Apache-airflow-backport-providers-slack 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", "cve": "CVE-2021-33502", @@ -6765,10 +6836,10 @@ "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-slack 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2021-29621", - "id": "pyup.io-49985", - "more_info_path": "/vulnerabilities/CVE-2021-29621/49985", + "advisory": "Apache-airflow-backport-providers-slack 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2020-7753", + "id": "pyup.io-49978", + "more_info_path": "/vulnerabilities/CVE-2020-7753/49978", "specs": [ "<=2021.3.3" ], @@ -6784,11 +6855,41 @@ ], "v": "<=2021.3.3" }, + { + "advisory": "Apache-airflow-backport-providers-slack 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", + "cve": "CVE-2021-29621", + "id": "pyup.io-49985", + "more_info_path": "/vulnerabilities/CVE-2021-29621/49985", + "specs": [ + "<=2021.3.3" + ], + "v": "<=2021.3.3" + }, + { + "advisory": "Apache-airflow-backport-providers-slack 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", + "cve": "CVE-2021-41265", + "id": "pyup.io-49988", + "more_info_path": "/vulnerabilities/CVE-2021-41265/49988", + "specs": [ + "<=2021.3.3" + ], + "v": "<=2021.3.3" + }, { "advisory": "Apache-airflow-backport-providers-slack 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2020-7753", - "id": "pyup.io-49978", - "more_info_path": "/vulnerabilities/CVE-2020-7753/49978", + "cve": "CVE-2021-37713", + "id": "pyup.io-49981", + "more_info_path": "/vulnerabilities/CVE-2021-37713/49981", + "specs": [ + "<=2021.3.3" + ], + "v": "<=2021.3.3" + }, + { + "advisory": "Apache-airflow-backport-providers-slack 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-37712", + "id": "pyup.io-49980", + "more_info_path": "/vulnerabilities/CVE-2021-37712/49980", "specs": [ "<=2021.3.3" ], @@ -6831,20 +6932,10 @@ ], "apache-airflow-backport-providers-ssh": [ { - "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37701", - "id": "pyup.io-49995", - "more_info_path": "/vulnerabilities/CVE-2021-37701/49995", - "specs": [ - "<=2021.3.3" - ], - "v": "<=2021.3.3" - }, - { - "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37713", - "id": "pyup.io-49997", - "more_info_path": "/vulnerabilities/CVE-2021-37713/49997", + "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", + "cve": "CVE-2021-32805", + "id": "pyup.io-50003", + "more_info_path": "/vulnerabilities/CVE-2021-32805/50003", "specs": [ "<=2021.3.3" ], @@ -6861,30 +6952,30 @@ "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-35936", - "id": "pyup.io-50000", - "more_info_path": "/vulnerabilities/CVE-2021-35936/50000", + "advisory": "apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2023-25754", + "id": "pyup.io-62966", + "more_info_path": "/vulnerabilities/CVE-2023-25754/62966", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2022-21659", - "id": "pyup.io-50005", - "more_info_path": "/vulnerabilities/CVE-2022-21659/50005", + "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-33502", + "id": "pyup.io-49999", + "more_info_path": "/vulnerabilities/CVE-2021-33502/49999", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2021-32805", - "id": "pyup.io-50003", - "more_info_path": "/vulnerabilities/CVE-2021-32805/50003", + "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (urllib3 == 1.25.11).", + "cve": "CVE-2021-33503", + "id": "pyup.io-50007", + "more_info_path": "/vulnerabilities/CVE-2021-33503/50007", "specs": [ "<=2021.3.3" ], @@ -6902,9 +6993,9 @@ }, { "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2022-24776", - "id": "pyup.io-50002", - "more_info_path": "/vulnerabilities/CVE-2022-24776/50002", + "cve": "CVE-2022-21659", + "id": "pyup.io-50005", + "more_info_path": "/vulnerabilities/CVE-2022-21659/50005", "specs": [ "<=2021.3.3" ], @@ -6912,49 +7003,49 @@ }, { "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-37712", - "id": "pyup.io-49996", - "more_info_path": "/vulnerabilities/CVE-2021-37712/49996", + "cve": "CVE-2020-7753", + "id": "pyup.io-49994", + "more_info_path": "/vulnerabilities/CVE-2020-7753/49994", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2023-25754", - "id": "pyup.io-62966", - "more_info_path": "/vulnerabilities/CVE-2023-25754/62966", + "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", + "cve": "CVE-2021-29621", + "id": "pyup.io-50001", + "more_info_path": "/vulnerabilities/CVE-2021-29621/50001", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2020-7753", - "id": "pyup.io-49994", - "more_info_path": "/vulnerabilities/CVE-2020-7753/49994", + "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", + "cve": "CVE-2022-29217", + "id": "pyup.io-50008", + "more_info_path": "/vulnerabilities/CVE-2022-29217/50008", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", - "cve": "CVE-2021-29621", - "id": "pyup.io-50001", - "more_info_path": "/vulnerabilities/CVE-2021-29621/50001", + "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-37713", + "id": "pyup.io-49997", + "more_info_path": "/vulnerabilities/CVE-2021-37713/49997", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", - "cve": "CVE-2022-29217", - "id": "pyup.io-50008", - "more_info_path": "/vulnerabilities/CVE-2022-29217/50008", + "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (flask-caching == 1.3.3).", + "cve": "CVE-2021-33026", + "id": "pyup.io-50006", + "more_info_path": "/vulnerabilities/CVE-2021-33026/50006", "specs": [ "<=2021.3.3" ], @@ -6962,19 +7053,19 @@ }, { "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-33502", - "id": "pyup.io-49999", - "more_info_path": "/vulnerabilities/CVE-2021-33502/49999", + "cve": "CVE-2021-35936", + "id": "pyup.io-50000", + "more_info_path": "/vulnerabilities/CVE-2021-35936/50000", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (urllib3 == 1.25.11).", - "cve": "CVE-2021-33503", - "id": "pyup.io-50007", - "more_info_path": "/vulnerabilities/CVE-2021-33503/50007", + "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (flask-appbuilder == 2.3.4).", + "cve": "CVE-2022-24776", + "id": "pyup.io-50002", + "more_info_path": "/vulnerabilities/CVE-2022-24776/50002", "specs": [ "<=2021.3.3" ], @@ -6982,19 +7073,29 @@ }, { "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", - "cve": "CVE-2021-23445", - "id": "pyup.io-49998", - "more_info_path": "/vulnerabilities/CVE-2021-23445/49998", + "cve": "CVE-2021-37712", + "id": "pyup.io-49996", + "more_info_path": "/vulnerabilities/CVE-2021-37712/49996", "specs": [ "<=2021.3.3" ], "v": "<=2021.3.3" }, { - "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (flask-caching == 1.3.3).", - "cve": "CVE-2021-33026", - "id": "pyup.io-50006", - "more_info_path": "/vulnerabilities/CVE-2021-33026/50006", + "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-37701", + "id": "pyup.io-49995", + "more_info_path": "/vulnerabilities/CVE-2021-37701/49995", + "specs": [ + "<=2021.3.3" + ], + "v": "<=2021.3.3" + }, + { + "advisory": "Apache-airflow-backport-providers-ssh 2021.3.3 and prior versions ship with vulnerable dependencies (apache-airflow == 1.10.15).", + "cve": "CVE-2021-23445", + "id": "pyup.io-49998", + "more_info_path": "/vulnerabilities/CVE-2021-23445/49998", "specs": [ "<=2021.3.3" ], @@ -7063,20 +7164,20 @@ ], "apache-airflow-providers-airbyte": [ { - "advisory": "Apache-airflow-providers-airbyte 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", - "cve": "CVE-2022-29217", - "id": "pyup.io-49837", - "more_info_path": "/vulnerabilities/CVE-2022-29217/49837", + "advisory": "Apache-airflow-providers-airbyte 3.0.0 and prior versions ship with vulnerable dependencies (click == 7.1.2).", + "cve": "PVE-2022-47833", + "id": "pyup.io-49836", + "more_info_path": "/vulnerabilities/PVE-2022-47833/49836", "specs": [ "<=3.0.0" ], "v": "<=3.0.0" }, { - "advisory": "Apache-airflow-providers-airbyte 3.0.0 and prior versions ship with vulnerable dependencies (click == 7.1.2).", - "cve": "PVE-2022-47833", - "id": "pyup.io-49836", - "more_info_path": "/vulnerabilities/PVE-2022-47833/49836", + "advisory": "Apache-airflow-providers-airbyte 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", + "cve": "CVE-2022-29217", + "id": "pyup.io-49837", + "more_info_path": "/vulnerabilities/CVE-2022-29217/49837", "specs": [ "<=3.0.0" ], @@ -7094,6 +7195,16 @@ } ], "apache-airflow-providers-amazon": [ + { + "advisory": "Apache-airflow-providers-amazon 4.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", + "cve": "CVE-2022-29217", + "id": "pyup.io-49834", + "more_info_path": "/vulnerabilities/CVE-2022-29217/49834", + "specs": [ + "<=4.0.0" + ], + "v": "<=4.0.0" + }, { "advisory": "Apache-airflow-providers-amazon 4.0.0 and prior versions ship with vulnerable dependencies (wtforms == 2.3.3).", "cve": "PVE-2021-42852", @@ -7114,16 +7225,6 @@ ], "v": "<=4.0.0" }, - { - "advisory": "Apache-airflow-providers-amazon 4.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", - "cve": "CVE-2022-29217", - "id": "pyup.io-49834", - "more_info_path": "/vulnerabilities/CVE-2022-29217/49834", - "specs": [ - "<=4.0.0" - ], - "v": "<=4.0.0" - }, { "advisory": "Generation of Error Message Containing Sensitive Information vulnerability in the Apache Airflow AWS Provider. This issue affects Apache Airflow AWS Provider versions before 7.2.1.", "cve": "CVE-2023-25956", @@ -7354,6 +7455,16 @@ ], "v": "<4.1.3" }, + { + "advisory": "Apache-airflow-providers-apache-spark 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", + "cve": "CVE-2022-29217", + "id": "pyup.io-49846", + "more_info_path": "/vulnerabilities/CVE-2022-29217/49846", + "specs": [ + "<=3.0.0" + ], + "v": "<=3.0.0" + }, { "advisory": "Apache-airflow-providers-apache-spark 3.0.0 and prior versions ship with vulnerable dependencies (wtforms == 2.3.3).", "cve": "PVE-2021-42852", @@ -7374,16 +7485,6 @@ ], "v": "<=3.0.0" }, - { - "advisory": "Apache-airflow-providers-apache-spark 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", - "cve": "CVE-2022-29217", - "id": "pyup.io-49846", - "more_info_path": "/vulnerabilities/CVE-2022-29217/49846", - "specs": [ - "<=3.0.0" - ], - "v": "<=3.0.0" - }, { "advisory": "Apache-airflow-providers-apache-spark is affected by CVE-2023-40195: Deserialization of Untrusted Data, Inclusion of Functionality from Untrusted Control Sphere vulnerability in Apache Software Foundation Apache Airflow Spark Provider.\r\nWhen the Apache Spark provider is installed on an Airflow deployment, an Airflow user that is authorized to configure Spark hooks can effectively run arbitrary code on the Airflow node by pointing it at a malicious Spark server. Prior to version 4.1.3, this was not called out in the documentation explicitly, so it is possible that administrators provided authorizations to configure Spark hooks without taking this into account. We recommend administrators to review their configurations to make sure the authorization to configure Spark hooks is only provided to fully trusted users.\r\nhttps://airflow.apache.org/docs/apache-airflow-providers-apache-spark/4.1.3/connections/spark.html", "cve": "CVE-2023-40195", @@ -7663,20 +7764,20 @@ "v": "<=8.1.0" }, { - "advisory": "Improper Input Validation vulnerability in the Apache Airflow Google Provider. This issue affects Apache Airflow Google Provider versions before 8.10.0.\r\nhttps://github.com/apache/airflow/pull/29499", - "cve": "CVE-2023-25692", - "id": "pyup.io-54664", - "more_info_path": "/vulnerabilities/CVE-2023-25692/54664", + "advisory": "Improper Input Validation vulnerability in the Apache Airflow Google Provider. This issue affects Apache Airflow Google Provider versions before 8.10.0.\r\nhttps://github.com/apache/airflow/pull/29497", + "cve": "CVE-2023-25691", + "id": "pyup.io-54665", + "more_info_path": "/vulnerabilities/CVE-2023-25691/54665", "specs": [ ">=0,<8.10.0" ], "v": ">=0,<8.10.0" }, { - "advisory": "Improper Input Validation vulnerability in the Apache Airflow Google Provider. This issue affects Apache Airflow Google Provider versions before 8.10.0.\r\nhttps://github.com/apache/airflow/pull/29497", - "cve": "CVE-2023-25691", - "id": "pyup.io-54665", - "more_info_path": "/vulnerabilities/CVE-2023-25691/54665", + "advisory": "Improper Input Validation vulnerability in the Apache Airflow Google Provider. This issue affects Apache Airflow Google Provider versions before 8.10.0.\r\nhttps://github.com/apache/airflow/pull/29499", + "cve": "CVE-2023-25692", + "id": "pyup.io-54664", + "more_info_path": "/vulnerabilities/CVE-2023-25692/54664", "specs": [ ">=0,<8.10.0" ], @@ -7833,20 +7934,20 @@ "v": "<3.4.1" }, { - "advisory": "Apache-airflow-providers-microsoft-mssql 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", - "cve": "CVE-2022-29217", - "id": "pyup.io-49828", - "more_info_path": "/vulnerabilities/CVE-2022-29217/49828", + "advisory": "Apache-airflow-providers-microsoft-mssql 3.0.0 and prior versions ship with vulnerable dependencies (wtforms == 2.3.3).", + "cve": "PVE-2021-42852", + "id": "pyup.io-49829", + "more_info_path": "/vulnerabilities/PVE-2021-42852/49829", "specs": [ "<=3.0.0" ], "v": "<=3.0.0" }, { - "advisory": "Apache-airflow-providers-microsoft-mssql 3.0.0 and prior versions ship with vulnerable dependencies (wtforms == 2.3.3).", - "cve": "PVE-2021-42852", - "id": "pyup.io-49829", - "more_info_path": "/vulnerabilities/PVE-2021-42852/49829", + "advisory": "Apache-airflow-providers-microsoft-mssql 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", + "cve": "CVE-2022-29217", + "id": "pyup.io-49828", + "more_info_path": "/vulnerabilities/CVE-2022-29217/49828", "specs": [ "<=3.0.0" ], @@ -7917,20 +8018,20 @@ "v": "<=3.0.0" }, { - "advisory": "Apache-airflow-providers-mysql 3.0.0 and prior versions ship with vulnerable dependencies (wtforms == 2.3.3).", - "cve": "PVE-2021-42852", - "id": "pyup.io-49832", - "more_info_path": "/vulnerabilities/PVE-2021-42852/49832", + "advisory": "Apache-airflow-providers-mysql 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", + "cve": "CVE-2022-29217", + "id": "pyup.io-49831", + "more_info_path": "/vulnerabilities/CVE-2022-29217/49831", "specs": [ "<=3.0.0" ], "v": "<=3.0.0" }, { - "advisory": "Apache-airflow-providers-mysql 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", - "cve": "CVE-2022-29217", - "id": "pyup.io-49831", - "more_info_path": "/vulnerabilities/CVE-2022-29217/49831", + "advisory": "Apache-airflow-providers-mysql 3.0.0 and prior versions ship with vulnerable dependencies (wtforms == 2.3.3).", + "cve": "PVE-2021-42852", + "id": "pyup.io-49832", + "more_info_path": "/vulnerabilities/PVE-2021-42852/49832", "specs": [ "<=3.0.0" ], @@ -7979,20 +8080,20 @@ "v": "<=3.0.0" }, { - "advisory": "Apache-airflow-providers-odbc 3.0.0 and prior versions ship with vulnerable dependencies (click == 7.1.2).", - "cve": "PVE-2022-47833", - "id": "pyup.io-49893", - "more_info_path": "/vulnerabilities/PVE-2022-47833/49893", + "advisory": "Apache-airflow-providers-odbc 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", + "cve": "CVE-2022-29217", + "id": "pyup.io-49894", + "more_info_path": "/vulnerabilities/CVE-2022-29217/49894", "specs": [ "<=3.0.0" ], "v": "<=3.0.0" }, { - "advisory": "Apache-airflow-providers-odbc 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", - "cve": "CVE-2022-29217", - "id": "pyup.io-49894", - "more_info_path": "/vulnerabilities/CVE-2022-29217/49894", + "advisory": "Apache-airflow-providers-odbc 3.0.0 and prior versions ship with vulnerable dependencies (click == 7.1.2).", + "cve": "PVE-2022-47833", + "id": "pyup.io-49893", + "more_info_path": "/vulnerabilities/PVE-2022-47833/49893", "specs": [ "<=3.0.0" ], @@ -8043,20 +8144,20 @@ "v": "<=3.0.0" }, { - "advisory": "Apache-airflow-providers-pagerduty 3.0.0 and prior versions ship with vulnerable dependencies (click == 7.1.2).", - "cve": "PVE-2022-47833", - "id": "pyup.io-49860", - "more_info_path": "/vulnerabilities/PVE-2022-47833/49860", + "advisory": "Apache-airflow-providers-pagerduty 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", + "cve": "CVE-2022-29217", + "id": "pyup.io-49861", + "more_info_path": "/vulnerabilities/CVE-2022-29217/49861", "specs": [ "<=3.0.0" ], "v": "<=3.0.0" }, { - "advisory": "Apache-airflow-providers-pagerduty 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", - "cve": "CVE-2022-29217", - "id": "pyup.io-49861", - "more_info_path": "/vulnerabilities/CVE-2022-29217/49861", + "advisory": "Apache-airflow-providers-pagerduty 3.0.0 and prior versions ship with vulnerable dependencies (click == 7.1.2).", + "cve": "PVE-2022-47833", + "id": "pyup.io-49860", + "more_info_path": "/vulnerabilities/PVE-2022-47833/49860", "specs": [ "<=3.0.0" ], @@ -8075,20 +8176,20 @@ "v": "<=3.0.0" }, { - "advisory": "Apache-airflow-providers-plexus 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", - "cve": "CVE-2022-29217", - "id": "pyup.io-49840", - "more_info_path": "/vulnerabilities/CVE-2022-29217/49840", + "advisory": "Apache-airflow-providers-plexus 3.0.0 and prior versions ship with vulnerable dependencies (click == 7.1.2).", + "cve": "PVE-2022-47833", + "id": "pyup.io-49839", + "more_info_path": "/vulnerabilities/PVE-2022-47833/49839", "specs": [ "<=3.0.0" ], "v": "<=3.0.0" }, { - "advisory": "Apache-airflow-providers-plexus 3.0.0 and prior versions ship with vulnerable dependencies (click == 7.1.2).", - "cve": "PVE-2022-47833", - "id": "pyup.io-49839", - "more_info_path": "/vulnerabilities/PVE-2022-47833/49839", + "advisory": "Apache-airflow-providers-plexus 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", + "cve": "CVE-2022-29217", + "id": "pyup.io-49840", + "more_info_path": "/vulnerabilities/CVE-2022-29217/49840", "specs": [ "<=3.0.0" ], @@ -8107,20 +8208,20 @@ "v": "<=5.0.0" }, { - "advisory": "Apache-airflow-providers-postgres 5.0.0 and prior versions ship with vulnerable dependencies (click == 7.1.2).", - "cve": "PVE-2022-47833", - "id": "pyup.io-49821", - "more_info_path": "/vulnerabilities/PVE-2022-47833/49821", + "advisory": "Apache-airflow-providers-postgres 5.0.0 and prior versions ship with vulnerable dependencies (wtforms == 2.3.3).", + "cve": "PVE-2021-42852", + "id": "pyup.io-49823", + "more_info_path": "/vulnerabilities/PVE-2021-42852/49823", "specs": [ "<=5.0.0" ], "v": "<=5.0.0" }, { - "advisory": "Apache-airflow-providers-postgres 5.0.0 and prior versions ship with vulnerable dependencies (wtforms == 2.3.3).", - "cve": "PVE-2021-42852", - "id": "pyup.io-49823", - "more_info_path": "/vulnerabilities/PVE-2021-42852/49823", + "advisory": "Apache-airflow-providers-postgres 5.0.0 and prior versions ship with vulnerable dependencies (click == 7.1.2).", + "cve": "PVE-2022-47833", + "id": "pyup.io-49821", + "more_info_path": "/vulnerabilities/PVE-2022-47833/49821", "specs": [ "<=5.0.0" ], @@ -8161,20 +8262,20 @@ ], "apache-airflow-providers-redis": [ { - "advisory": "Apache-airflow-providers-redis 3.0.0 and prior versions ship with vulnerable dependencies (click == 7.1.2).", - "cve": "PVE-2022-47833", - "id": "pyup.io-49872", - "more_info_path": "/vulnerabilities/PVE-2022-47833/49872", + "advisory": "Apache-airflow-providers-redis 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", + "cve": "CVE-2022-29217", + "id": "pyup.io-49873", + "more_info_path": "/vulnerabilities/CVE-2022-29217/49873", "specs": [ "<=3.0.0" ], "v": "<=3.0.0" }, { - "advisory": "Apache-airflow-providers-redis 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", - "cve": "CVE-2022-29217", - "id": "pyup.io-49873", - "more_info_path": "/vulnerabilities/CVE-2022-29217/49873", + "advisory": "Apache-airflow-providers-redis 3.0.0 and prior versions ship with vulnerable dependencies (click == 7.1.2).", + "cve": "PVE-2022-47833", + "id": "pyup.io-49872", + "more_info_path": "/vulnerabilities/PVE-2022-47833/49872", "specs": [ "<=3.0.0" ], @@ -8288,6 +8389,16 @@ } ], "apache-airflow-providers-snowflake": [ + { + "advisory": "Apache-airflow-providers-snowflake 3.0.0 and prior versions ship with vulnerable dependencies (click == 7.1.2).", + "cve": "PVE-2022-47833", + "id": "pyup.io-49848", + "more_info_path": "/vulnerabilities/PVE-2022-47833/49848", + "specs": [ + "<=3.0.0" + ], + "v": "<=3.0.0" + }, { "advisory": "Apache-airflow-providers-snowflake 3.0.0 and prior versions ship with vulnerable dependencies (pyjwt == 1.7.1).", "cve": "CVE-2022-29217", @@ -8307,16 +8418,6 @@ "<=3.0.0" ], "v": "<=3.0.0" - }, - { - "advisory": "Apache-airflow-providers-snowflake 3.0.0 and prior versions ship with vulnerable dependencies (click == 7.1.2).", - "cve": "PVE-2022-47833", - "id": "pyup.io-49848", - "more_info_path": "/vulnerabilities/PVE-2022-47833/49848", - "specs": [ - "<=3.0.0" - ], - "v": "<=3.0.0" } ], "apache-airflow-providers-ssh": [ @@ -8548,16 +8649,6 @@ ], "v": "<3.1.0" }, - { - "advisory": "Apache-dolphinscheduler (Python API) 3.1.0 works together with apache-dolphinscheduler (core) 3.1.0, that updates its MAVEN dependency 'h2' to v2.1.210 to include security fixes.", - "cve": "CVE-2021-42392", - "id": "pyup.io-51309", - "more_info_path": "/vulnerabilities/CVE-2021-42392/51309", - "specs": [ - "<3.1.0" - ], - "v": "<3.1.0" - }, { "advisory": "Apache-dolphinscheduler (Python API) 3.1.0 works together with apache-dolphinscheduler (core) 3.1.0, that adds validations of possible malicious keys.\r\nhttps://github.com/apache/dolphinscheduler/commit/5811b84fcc7cc0ff354cf8e871f36aa3ae61aa2a", "cve": "PVE-2022-51304", @@ -8618,6 +8709,16 @@ ], "v": "<3.1.0" }, + { + "advisory": "Apache-dolphinscheduler (Python API) 3.1.0 works together with apache-dolphinscheduler (core) 3.1.0, that updates its MAVEN dependency 'h2' to v2.1.210 to include security fixes.", + "cve": "CVE-2021-42392", + "id": "pyup.io-51309", + "more_info_path": "/vulnerabilities/CVE-2021-42392/51309", + "specs": [ + "<3.1.0" + ], + "v": "<3.1.0" + }, { "advisory": "Apache-dolphinscheduler 2.0.5 (Python SDK) corresponds to DolphinScheduler version 2.0.5, that fixes CVE-2022-25598:\r\nApache DolphinScheduler user registration is vulnerable to Regular express Denial of Service (ReDoS) attacks.", "cve": "CVE-2022-25598", @@ -8631,20 +8732,20 @@ ], "apache-flink": [ { - "advisory": "Apache-flink 1.14.2 updates its dependency 'log4j' to v2.16.0 to include security fixes.\r\nhttps://github.com/apache/flink/commit/361ce6591069b2f7317f1c181cdaf7965615415c", - "cve": "CVE-2021-44228", - "id": "pyup.io-43416", - "more_info_path": "/vulnerabilities/CVE-2021-44228/43416", + "advisory": "Apache-flink 1.14.2 updates its dependency 'log4j' to v2.16.0 to include security fixes.\r\nhttps://github.com/apache/flink/commit/361ce6591069b2f7317f1c181cdaf7965615415c", + "cve": "CVE-2021-45046", + "id": "pyup.io-43417", + "more_info_path": "/vulnerabilities/CVE-2021-45046/43417", "specs": [ "<1.14.2" ], "v": "<1.14.2" }, { - "advisory": "Apache-flink 1.14.2 updates its dependency 'log4j' to v2.16.0 to include security fixes.\r\nhttps://github.com/apache/flink/commit/361ce6591069b2f7317f1c181cdaf7965615415c", - "cve": "CVE-2021-45046", - "id": "pyup.io-43417", - "more_info_path": "/vulnerabilities/CVE-2021-45046/43417", + "advisory": "Apache-flink 1.14.2 updates its dependency 'log4j' to v2.16.0 to include security fixes.\r\nhttps://github.com/apache/flink/commit/361ce6591069b2f7317f1c181cdaf7965615415c", + "cve": "CVE-2021-44228", + "id": "pyup.io-43416", + "more_info_path": "/vulnerabilities/CVE-2021-44228/43416", "specs": [ "<1.14.2" ], @@ -9033,9 +9134,9 @@ }, { "advisory": "Apache-superset 1.2.0 updates NPM packages for security fixes.\r\nhttps://github.com/apache/superset/pull/13367", - "cve": "CVE-2021-3807", - "id": "pyup.io-45803", - "more_info_path": "/vulnerabilities/CVE-2021-3807/45803", + "cve": "CVE-2020-28477", + "id": "pyup.io-41791", + "more_info_path": "/vulnerabilities/CVE-2020-28477/41791", "specs": [ "<1.2.0" ], @@ -9043,9 +9144,9 @@ }, { "advisory": "Apache-superset 1.2.0 updates NPM packages for security fixes.\r\nhttps://github.com/apache/superset/pull/13367", - "cve": "CVE-2020-28477", - "id": "pyup.io-41791", - "more_info_path": "/vulnerabilities/CVE-2020-28477/41791", + "cve": "CVE-2021-3807", + "id": "pyup.io-45803", + "more_info_path": "/vulnerabilities/CVE-2021-3807/45803", "specs": [ "<1.2.0" ], @@ -9142,20 +9243,20 @@ "v": "<3.0.0" }, { - "advisory": "An authenticated malicious user could initiate multiple concurrent requests, each requesting multiple dashboard exports, leading to a possible denial of service. This issue affects Apache Superset: before 3.0.0", - "cve": "CVE-2023-42504", - "id": "pyup.io-65228", - "more_info_path": "/vulnerabilities/CVE-2023-42504/65228", + "advisory": "Apache-superset 3.0.0 updates its NPM dependency 'ansi-regex' to include a security fix.", + "cve": "CVE-2021-3807", + "id": "pyup.io-61908", + "more_info_path": "/vulnerabilities/CVE-2021-3807/61908", "specs": [ "<3.0.0" ], "v": "<3.0.0" }, { - "advisory": "Apache-superset 3.0.0 updates its NPM dependency 'ansi-regex' to include a security fix.", - "cve": "CVE-2021-3807", - "id": "pyup.io-61908", - "more_info_path": "/vulnerabilities/CVE-2021-3807/61908", + "advisory": "An authenticated malicious user could initiate multiple concurrent requests, each requesting multiple dashboard exports, leading to a possible denial of service. This issue affects Apache Superset: before 3.0.0", + "cve": "CVE-2023-42504", + "id": "pyup.io-65228", + "more_info_path": "/vulnerabilities/CVE-2023-42504/65228", "specs": [ "<3.0.0" ], @@ -9203,10 +9304,10 @@ "v": "<4.0.2" }, { - "advisory": "When explicitly enabling the feature flag 'DASHBOARD_CACHE' (disabled by default), the system allowed for an unauthenticated user to access dashboard configuration metadata using a REST API Get endpoint. This issue affects Apache Superset version 1.5.2 and prior versions and version 2.0.0.", - "cve": "CVE-2022-45438", - "id": "pyup.io-54614", - "more_info_path": "/vulnerabilities/CVE-2022-45438/54614", + "advisory": "Dashboard rendering does not sufficiently sanitize the content of markdown components leading to possible XSS attack vectors that can be performed by authenticated users with create dashboard permissions. This issue affects Apache Superset version 1.5.2 and prior versions and version 2.0.0.", + "cve": "CVE-2022-43717", + "id": "pyup.io-54616", + "more_info_path": "/vulnerabilities/CVE-2022-43717/54616", "specs": [ "<=1.5.2", "==2.0.0" @@ -9225,10 +9326,10 @@ "v": "<=1.5.2,==2.0.0" }, { - "advisory": "Dashboard rendering does not sufficiently sanitize the content of markdown components leading to possible XSS attack vectors that can be performed by authenticated users with create dashboard permissions. This issue affects Apache Superset version 1.5.2 and prior versions and version 2.0.0.", - "cve": "CVE-2022-43717", - "id": "pyup.io-54616", - "more_info_path": "/vulnerabilities/CVE-2022-43717/54616", + "advisory": "An authenticated attacker with write CSS template permissions can create a record with specific HTML tags that will not get properly escaped by the toast message displayed when a user deletes that specific CSS template record. This issue affects Apache Superset version 1.5.2 and prior versions and version 2.0.0.", + "cve": "CVE-2022-43720", + "id": "pyup.io-54625", + "more_info_path": "/vulnerabilities/CVE-2022-43720/54625", "specs": [ "<=1.5.2", "==2.0.0" @@ -9236,10 +9337,10 @@ "v": "<=1.5.2,==2.0.0" }, { - "advisory": "An authenticated attacker with update datasets permission could change a dataset link to an untrusted site, users could be redirected to this site when clicking on that specific dataset. This issue affects Apache Superset version 1.5.2 and prior versions and version 2.0.0.", - "cve": "CVE-2022-43721", - "id": "pyup.io-54615", - "more_info_path": "/vulnerabilities/CVE-2022-43721/54615", + "advisory": "When explicitly enabling the feature flag 'DASHBOARD_CACHE' (disabled by default), the system allowed for an unauthenticated user to access dashboard configuration metadata using a REST API Get endpoint. This issue affects Apache Superset version 1.5.2 and prior versions and version 2.0.0.", + "cve": "CVE-2022-45438", + "id": "pyup.io-54614", + "more_info_path": "/vulnerabilities/CVE-2022-45438/54614", "specs": [ "<=1.5.2", "==2.0.0" @@ -9247,10 +9348,10 @@ "v": "<=1.5.2,==2.0.0" }, { - "advisory": "An authenticated attacker with write CSS template permissions can create a record with specific HTML tags that will not get properly escaped by the toast message displayed when a user deletes that specific CSS template record. This issue affects Apache Superset version 1.5.2 and prior versions and version 2.0.0.", - "cve": "CVE-2022-43720", - "id": "pyup.io-54625", - "more_info_path": "/vulnerabilities/CVE-2022-43720/54625", + "advisory": "An authenticated attacker with update datasets permission could change a dataset link to an untrusted site, users could be redirected to this site when clicking on that specific dataset. This issue affects Apache Superset version 1.5.2 and prior versions and version 2.0.0.", + "cve": "CVE-2022-43721", + "id": "pyup.io-54615", + "more_info_path": "/vulnerabilities/CVE-2022-43721/54615", "specs": [ "<=1.5.2", "==2.0.0" @@ -9280,10 +9381,10 @@ "v": "<=1.5.2,==2.0.0" }, { - "advisory": "A malicious actor who has been authenticated and granted specific permissions in Apache Superset may use the import dataset feature in order to conduct Server-Side Request Forgery\r\nattacks and query internal resources on behalf of the server where Superset\r\nis deployed. This vulnerability exists\u00a0in Apache Superset versions up to and including 2.0.1.", - "cve": "CVE-2023-25504", - "id": "pyup.io-62896", - "more_info_path": "/vulnerabilities/CVE-2023-25504/62896", + "advisory": "Session Validation attacks in Apache Superset versions up to and including 2.0.1. Installations that have not altered the default configured SECRET_KEY according to installation instructions allow for an attacker to authenticate and access unauthorized resources. This does not affect Superset administrators who have changed the default value for SECRET_KEY config.", + "cve": "CVE-2023-27524", + "id": "pyup.io-62900", + "more_info_path": "/vulnerabilities/CVE-2023-27524/62900", "specs": [ "<=2.0.1" ], @@ -9300,10 +9401,10 @@ "v": "<=2.0.1" }, { - "advisory": "Session Validation attacks in Apache Superset versions up to and including 2.0.1. Installations that have not altered the default configured SECRET_KEY according to installation instructions allow for an attacker to authenticate and access unauthorized resources. This does not affect Superset administrators who have changed the default value for SECRET_KEY config.", - "cve": "CVE-2023-27524", - "id": "pyup.io-62900", - "more_info_path": "/vulnerabilities/CVE-2023-27524/62900", + "advisory": "A malicious actor who has been authenticated and granted specific permissions in Apache Superset may use the import dataset feature in order to conduct Server-Side Request Forgery\r\nattacks and query internal resources on behalf of the server where Superset\r\nis deployed. This vulnerability exists\u00a0in Apache Superset versions up to and including 2.0.1.", + "cve": "CVE-2023-25504", + "id": "pyup.io-62896", + "more_info_path": "/vulnerabilities/CVE-2023-25504/62896", "specs": [ "<=2.0.1" ], @@ -9480,20 +9581,20 @@ "v": ">=0,<1.3.1" }, { - "advisory": "Improper output neutralization for Logs. A specific Apache Superset HTTP endpoint allowed for an authenticated user to forge log entries or inject malicious content into logs.", - "cve": "CVE-2021-42250", - "id": "pyup.io-54375", - "more_info_path": "/vulnerabilities/CVE-2021-42250/54375", + "advisory": "Apache Superset up to and including 1.3.1 allowed for database connections password leak for authenticated users. This information could be accessed in a non-trivial way.", + "cve": "CVE-2021-41972", + "id": "pyup.io-54371", + "more_info_path": "/vulnerabilities/CVE-2021-41972/54371", "specs": [ ">=0,<1.3.2" ], "v": ">=0,<1.3.2" }, { - "advisory": "Apache Superset up to and including 1.3.1 allowed for database connections password leak for authenticated users. This information could be accessed in a non-trivial way.", - "cve": "CVE-2021-41972", - "id": "pyup.io-54371", - "more_info_path": "/vulnerabilities/CVE-2021-41972/54371", + "advisory": "Improper output neutralization for Logs. A specific Apache Superset HTTP endpoint allowed for an authenticated user to forge log entries or inject malicious content into logs.", + "cve": "CVE-2021-42250", + "id": "pyup.io-54375", + "more_info_path": "/vulnerabilities/CVE-2021-42250/54375", "specs": [ ">=0,<1.3.2" ], @@ -9529,17 +9630,6 @@ ], "v": ">=0,<1.5.1" }, - { - "advisory": "Uncontrolled resource consumption can be triggered by authenticated attacker that uploads a malicious ZIP to import database, dashboards or datasets.\u00a0\u00a0\r\nThis vulnerability exists in Apache Superset versions up to and including 2.1.2 and versions 3.0.0, 3.0.1.", - "cve": "CVE-2023-46104", - "id": "pyup.io-65186", - "more_info_path": "/vulnerabilities/CVE-2023-46104/65186", - "specs": [ - ">=0,<2.1.3", - ">=3.0.0,<3.0.2" - ], - "v": ">=0,<2.1.3,>=3.0.0,<3.0.2" - }, { "advisory": "A where_in JINJA macro allows users to specify a quote, which combined with a carefully crafted statement\u00a0would allow for SQL injection\u00a0in Apache Superset. This issue affects Apache Superset: before 2.1.2, from 3.0.0 before 3.0.2. Users are recommended to upgrade to version 3.0.2, which fixes the issue.", "cve": "CVE-2023-49736", @@ -9563,26 +9653,15 @@ "v": ">=0,<2.1.3,>=3.0.0,<3.0.2" }, { - "advisory": "Apache Superset with custom roles that include `can write on dataset` and without all data access permissions, allows for users to create virtual datasets to data they don't have access to. These users could then use those virtual datasets to get access to unauthorized data. This issue affects Apache Superset: before 3.0.4, from 3.1.0 before 3.1.1. Users are recommended to upgrade to version 3.1.1 or 3.0.4, which fixes the issue.", - "cve": "CVE-2024-24779", - "id": "pyup.io-68494", - "more_info_path": "/vulnerabilities/CVE-2024-24779/68494", - "specs": [ - ">=0,<3.0.4", - ">=3.1.0,<3.1.1" - ], - "v": ">=0,<3.0.4,>=3.1.0,<3.1.1" - }, - { - "advisory": "A guest user could exploit a chart data REST API and send arbitrary SQL statements that on error could leak information from the underlying analytics database. This issue affects Apache Superset: before 3.0.4, from 3.1.0 before 3.1.1.", - "cve": "CVE-2024-24772", - "id": "pyup.io-68496", - "more_info_path": "/vulnerabilities/CVE-2024-24772/68496", + "advisory": "Uncontrolled resource consumption can be triggered by authenticated attacker that uploads a malicious ZIP to import database, dashboards or datasets.\u00a0\u00a0\r\nThis vulnerability exists in Apache Superset versions up to and including 2.1.2 and versions 3.0.0, 3.0.1.", + "cve": "CVE-2023-46104", + "id": "pyup.io-65186", + "more_info_path": "/vulnerabilities/CVE-2023-46104/65186", "specs": [ - ">=0,<3.0.4", - ">=3.1.0,<3.1.1" + ">=0,<2.1.3", + ">=3.0.0,<3.0.2" ], - "v": ">=0,<3.0.4,>=3.1.0,<3.1.1" + "v": ">=0,<2.1.3,>=3.0.0,<3.0.2" }, { "advisory": "Improper parsing of nested SQL statements on SQLLab would allow authenticated users to surpass their data authorization scope. This issue affects Apache Superset: before 3.0.4, from 3.1.0 before 3.1.1. Users are recommended to upgrade to version 3.1.1, which fixes the issue.", @@ -9606,6 +9685,28 @@ ], "v": ">=0,<3.0.4,>=3.1.0,<3.1.1" }, + { + "advisory": "Apache Superset with custom roles that include `can write on dataset` and without all data access permissions, allows for users to create virtual datasets to data they don't have access to. These users could then use those virtual datasets to get access to unauthorized data. This issue affects Apache Superset: before 3.0.4, from 3.1.0 before 3.1.1. Users are recommended to upgrade to version 3.1.1 or 3.0.4, which fixes the issue.", + "cve": "CVE-2024-24779", + "id": "pyup.io-68494", + "more_info_path": "/vulnerabilities/CVE-2024-24779/68494", + "specs": [ + ">=0,<3.0.4", + ">=3.1.0,<3.1.1" + ], + "v": ">=0,<3.0.4,>=3.1.0,<3.1.1" + }, + { + "advisory": "A guest user could exploit a chart data REST API and send arbitrary SQL statements that on error could leak information from the underlying analytics database. This issue affects Apache Superset: before 3.0.4, from 3.1.0 before 3.1.1.", + "cve": "CVE-2024-24772", + "id": "pyup.io-68496", + "more_info_path": "/vulnerabilities/CVE-2024-24772/68496", + "specs": [ + ">=0,<3.0.4", + ">=3.1.0,<3.1.1" + ], + "v": ">=0,<3.0.4,>=3.1.0,<3.1.1" + }, { "advisory": "A vulnerability in various versions of Apache Superset allows authenticated users with alert creation privileges to execute a specially crafted SQL statement, leading to a database error. This error, improperly handled, could expose sensitive information in the alert's error log.", "cve": "CVE-2024-27315", @@ -10011,10 +10112,10 @@ "v": "<2.1.0" }, { - "advisory": "Aqtinstall 2.1.0rc2 uses 'defusedxml' instead of 'xml.etree.ElementTree' to avoid XXE attacks.\r\nhttps://github.com/miurahr/aqtinstall/commit/745e6a25e46411ff526387615a1db51a6ba968e0", - "cve": "CVE-2013-1664", - "id": "pyup.io-47852", - "more_info_path": "/vulnerabilities/CVE-2013-1664/47852", + "advisory": "Aqtinstall 2.1.0rc2 uses 'secrets' instead of 'random' module to generate cryptographically safe numbers.\r\nhttps://github.com/miurahr/aqtinstall/commit/745e6a25e46411ff526387615a1db51a6ba968e0", + "cve": "PVE-2022-47013", + "id": "pyup.io-47013", + "more_info_path": "/vulnerabilities/PVE-2022-47013/47013", "specs": [ "<2.1.0rc2" ], @@ -10031,10 +10132,10 @@ "v": "<2.1.0rc2" }, { - "advisory": "Aqtinstall 2.1.0rc2 uses 'secrets' instead of 'random' module to generate cryptographically safe numbers.\r\nhttps://github.com/miurahr/aqtinstall/commit/745e6a25e46411ff526387615a1db51a6ba968e0", - "cve": "PVE-2022-47013", - "id": "pyup.io-47013", - "more_info_path": "/vulnerabilities/PVE-2022-47013/47013", + "advisory": "Aqtinstall 2.1.0rc2 uses 'defusedxml' instead of 'xml.etree.ElementTree' to avoid XXE attacks.\r\nhttps://github.com/miurahr/aqtinstall/commit/745e6a25e46411ff526387615a1db51a6ba968e0", + "cve": "CVE-2013-1664", + "id": "pyup.io-47852", + "more_info_path": "/vulnerabilities/CVE-2013-1664/47852", "specs": [ "<2.1.0rc2" ], @@ -10192,9 +10293,9 @@ }, { "advisory": "Argilla 0.13.0 stops requiring its NPM dependency 'node-sass' to avoid security issues.", - "cve": "CVE-2019-18799", - "id": "pyup.io-52809", - "more_info_path": "/vulnerabilities/CVE-2019-18799/52809", + "cve": "CVE-2019-18797", + "id": "pyup.io-52811", + "more_info_path": "/vulnerabilities/CVE-2019-18797/52811", "specs": [ "<0.13.0" ], @@ -10202,9 +10303,9 @@ }, { "advisory": "Argilla 0.13.0 stops requiring its NPM dependency 'node-sass' to avoid security issues.", - "cve": "CVE-2019-18797", - "id": "pyup.io-52811", - "more_info_path": "/vulnerabilities/CVE-2019-18797/52811", + "cve": "CVE-2019-18799", + "id": "pyup.io-52809", + "more_info_path": "/vulnerabilities/CVE-2019-18799/52809", "specs": [ "<0.13.0" ], @@ -10273,40 +10374,40 @@ ], "argo-workflows": [ { - "advisory": "Argo-workflows 5.0.0 (Python SDK) is compatible with Argo-workflows core v3.0.0, which updates its NPM dependency 'swagger-ui-react' to v3.29.0, that includes a version of 'lodash' that fixes a vulnerability.", - "cve": "CVE-2020-8203", - "id": "pyup.io-46474", - "more_info_path": "/vulnerabilities/CVE-2020-8203/46474", + "advisory": "Argo-workflows 5.0.0 (Python SDK) is compatible with Argo-workflows core v3.0.0, which improves cookie security.\r\nhttps://github.com/argoproj/argo-workflows/issues/2759", + "cve": "PVE-2022-46476", + "id": "pyup.io-46476", + "more_info_path": "/vulnerabilities/PVE-2022-46476/46476", "specs": [ "<5.0.0" ], "v": "<5.0.0" }, { - "advisory": "Argo-workflows 5.0.0 (Python SDK) is compatible with Argo-workflows core v3.0.0, which includes a fix for an issue that allowed to list archived workflows that shouldn't be accessible.\r\nhttps://github.com/argoproj/argo-workflows/blob/7e9fc374a22c63fd5e09c322b37bd810f5d57a0e/sdks/python/README.md\r\nhttps://github.com/argoproj/argo-workflows/pull/2079", - "cve": "PVE-2022-46479", - "id": "pyup.io-46479", - "more_info_path": "/vulnerabilities/PVE-2022-46479/46479", + "advisory": "Argo-workflows 5.0.0 (Python SDK) is compatible with Argo-workflows core v3.0.0, which fixes a XSS vulnerability.\r\nhttps://github.com/argoproj/argo-workflows/pull/3975", + "cve": "PVE-2022-46473", + "id": "pyup.io-46473", + "more_info_path": "/vulnerabilities/PVE-2022-46473/46473", "specs": [ "<5.0.0" ], "v": "<5.0.0" }, { - "advisory": "Argo-workflows 5.0.0 (Python SDK) is compatible with Argo-workflows core v3.0.0, which improves cookie security.\r\nhttps://github.com/argoproj/argo-workflows/issues/2759", - "cve": "PVE-2022-46476", - "id": "pyup.io-46476", - "more_info_path": "/vulnerabilities/PVE-2022-46476/46476", + "advisory": "Argo-workflows 5.0.0 (Python SDK) is compatible with Argo-workflows core v3.0.0, which includes a fix for an issue that allowed to list archived workflows that shouldn't be accessible.\r\nhttps://github.com/argoproj/argo-workflows/blob/7e9fc374a22c63fd5e09c322b37bd810f5d57a0e/sdks/python/README.md\r\nhttps://github.com/argoproj/argo-workflows/pull/2079", + "cve": "PVE-2022-46479", + "id": "pyup.io-46479", + "more_info_path": "/vulnerabilities/PVE-2022-46479/46479", "specs": [ "<5.0.0" ], "v": "<5.0.0" }, { - "advisory": "Argo-workflows 5.0.0 (Python SDK) is compatible with Argo-workflows core v3.0.0, which fixes a XSS vulnerability.\r\nhttps://github.com/argoproj/argo-workflows/pull/3975", - "cve": "PVE-2022-46473", - "id": "pyup.io-46473", - "more_info_path": "/vulnerabilities/PVE-2022-46473/46473", + "advisory": "Argo-workflows 5.0.0 (Python SDK) is compatible with Argo-workflows core v3.0.0, which updates its NPM dependency 'swagger-ui-react' to v3.29.0, that includes a version of 'lodash' that fixes a vulnerability.", + "cve": "CVE-2020-8203", + "id": "pyup.io-46474", + "more_info_path": "/vulnerabilities/CVE-2020-8203/46474", "specs": [ "<5.0.0" ], @@ -10396,9 +10497,9 @@ }, { "advisory": "Argo-workflows 6.3.9 (Python SDK) is compatible with Argo-workflows core v3.3.9, that updates Maven dependencies to include security fixes.\r\nhttps://github.com/argoproj/argo-workflows/commit/481137c259b05c6a5b3c0e3adab1649c2b512364", - "cve": "CVE-2021-22569", - "id": "pyup.io-50686", - "more_info_path": "/vulnerabilities/CVE-2021-22569/50686", + "cve": "CVE-2020-28052", + "id": "pyup.io-50691", + "more_info_path": "/vulnerabilities/CVE-2020-28052/50691", "specs": [ "<6.3.9" ], @@ -10416,9 +10517,9 @@ }, { "advisory": "Argo-workflows 6.3.9 (Python SDK) is compatible with Argo-workflows core v3.3.9, that updates Maven dependencies to include security fixes.\r\nhttps://github.com/argoproj/argo-workflows/commit/481137c259b05c6a5b3c0e3adab1649c2b512364", - "cve": "CVE-2020-28052", - "id": "pyup.io-50691", - "more_info_path": "/vulnerabilities/CVE-2020-28052/50691", + "cve": "CVE-2021-22569", + "id": "pyup.io-50686", + "more_info_path": "/vulnerabilities/CVE-2021-22569/50686", "specs": [ "<6.3.9" ], @@ -10446,9 +10547,9 @@ }, { "advisory": "Argo-workflows 6.4.7 (Python SDK) is compatible with Argo-workflows core v3.4.7, which upgrades docker to v20.10.24 to include security fixes.\r\nhttps://github.com/argoproj/argo-workflows/pull/10868", - "cve": "CVE-2023-28842", - "id": "pyup.io-54996", - "more_info_path": "/vulnerabilities/CVE-2023-28842/54996", + "cve": "CVE-2023-28840", + "id": "pyup.io-54979", + "more_info_path": "/vulnerabilities/CVE-2023-28840/54979", "specs": [ "<6.4.7" ], @@ -10466,9 +10567,9 @@ }, { "advisory": "Argo-workflows 6.4.7 (Python SDK) is compatible with Argo-workflows core v3.4.7, which upgrades docker to v20.10.24 to include security fixes.\r\nhttps://github.com/argoproj/argo-workflows/pull/10868", - "cve": "CVE-2023-28841", - "id": "pyup.io-54995", - "more_info_path": "/vulnerabilities/CVE-2023-28841/54995", + "cve": "CVE-2023-28842", + "id": "pyup.io-54996", + "more_info_path": "/vulnerabilities/CVE-2023-28842/54996", "specs": [ "<6.4.7" ], @@ -10476,9 +10577,9 @@ }, { "advisory": "Argo-workflows 6.4.7 (Python SDK) is compatible with Argo-workflows core v3.4.7, which upgrades docker to v20.10.24 to include security fixes.\r\nhttps://github.com/argoproj/argo-workflows/pull/10868", - "cve": "CVE-2023-28840", - "id": "pyup.io-54979", - "more_info_path": "/vulnerabilities/CVE-2023-28840/54979", + "cve": "CVE-2023-28841", + "id": "pyup.io-54995", + "more_info_path": "/vulnerabilities/CVE-2023-28841/54995", "specs": [ "<6.4.7" ], @@ -10836,20 +10937,20 @@ "v": "<3.0.1" }, { - "advisory": "Astropy 3.0.1 updates the bundled CFITSIO library to 3.430. This is to remedy a critical security vulnerability that was identified by NASA.", - "cve": "CVE-2018-3849", - "id": "pyup.io-48548", - "more_info_path": "/vulnerabilities/CVE-2018-3849/48548", + "advisory": "Astropy 3.0.1 updates cfitsio to v3.43: NASA CFITSIO prior to 3.43 is affected by: Buffer Overflow. The impact is: arbitrary code execution. The component is: over 40 source code files were changed. The attack vector is: remote unauthenticated attacker. The fixed version is: 3.43. NOTE: this CVE refers to the issues not covered by CVE-2018-3846, CVE-2018-3847, CVE-2018-3848, and CVE-2018-3849. One example is ftp_status in drvrnet.c mishandling a long string beginning with a '4' character.", + "cve": "CVE-2019-1010060", + "id": "pyup.io-70530", + "more_info_path": "/vulnerabilities/CVE-2019-1010060/70530", "specs": [ "<3.0.1" ], "v": "<3.0.1" }, { - "advisory": "Astropy 3.0.1 updates cfitsio to v3.43: NASA CFITSIO prior to 3.43 is affected by: Buffer Overflow. The impact is: arbitrary code execution. The component is: over 40 source code files were changed. The attack vector is: remote unauthenticated attacker. The fixed version is: 3.43. NOTE: this CVE refers to the issues not covered by CVE-2018-3846, CVE-2018-3847, CVE-2018-3848, and CVE-2018-3849. One example is ftp_status in drvrnet.c mishandling a long string beginning with a '4' character.", - "cve": "CVE-2019-1010060", - "id": "pyup.io-70530", - "more_info_path": "/vulnerabilities/CVE-2019-1010060/70530", + "advisory": "Astropy 3.0.1 updates the bundled CFITSIO library to 3.430. This is to remedy a critical security vulnerability that was identified by NASA.", + "cve": "CVE-2018-3849", + "id": "pyup.io-48548", + "more_info_path": "/vulnerabilities/CVE-2018-3849/48548", "specs": [ "<3.0.1" ], @@ -10877,9 +10978,9 @@ }, { "advisory": "Astropy 5.1.1 and 5.0.5 update its JS dependency 'jquery' to v3.6.0 to include security fixes.", - "cve": "CVE-2020-11022", - "id": "pyup.io-52131", - "more_info_path": "/vulnerabilities/CVE-2020-11022/52131", + "cve": "CVE-2020-11023", + "id": "pyup.io-52172", + "more_info_path": "/vulnerabilities/CVE-2020-11023/52172", "specs": [ ">=5.1rc1,<5.1.1", "<5.0.5" @@ -10888,9 +10989,9 @@ }, { "advisory": "Astropy 5.1.1 and 5.0.5 update its JS dependency 'jquery' to v3.6.0 to include security fixes.", - "cve": "CVE-2020-11023", - "id": "pyup.io-52172", - "more_info_path": "/vulnerabilities/CVE-2020-11023/52172", + "cve": "CVE-2020-11022", + "id": "pyup.io-52131", + "more_info_path": "/vulnerabilities/CVE-2020-11022/52131", "specs": [ ">=5.1rc1,<5.1.1", "<5.0.5" @@ -10900,20 +11001,10 @@ ], "async-batcher": [ { - "advisory": "Async-batcher's update to a newer version of scikit-learn addresses CVE-2024-5206.", - "cve": "CVE-2024-5206", - "id": "pyup.io-73033", - "more_info_path": "/vulnerabilities/CVE-2024-5206/73033", - "specs": [ - "<0.2.1" - ], - "v": "<0.2.1" - }, - { - "advisory": "Async-batcher's update to a newer version of idna addresses CVE-2024-3651.", - "cve": "CVE-2024-3651", - "id": "pyup.io-73013", - "more_info_path": "/vulnerabilities/CVE-2024-3651/73013", + "advisory": "Async-batcher's update to a newer version of setuptools addresses CVE-2024-6345.", + "cve": "CVE-2024-6345", + "id": "pyup.io-73034", + "more_info_path": "/vulnerabilities/CVE-2024-6345/73034", "specs": [ "<0.2.1" ], @@ -10930,10 +11021,10 @@ "v": "<0.2.1" }, { - "advisory": "Async-batcher's update to a newer version of setuptools addresses CVE-2024-6345.", - "cve": "CVE-2024-6345", - "id": "pyup.io-73034", - "more_info_path": "/vulnerabilities/CVE-2024-6345/73034", + "advisory": "Async-batcher's update to a newer version of idna addresses CVE-2024-3651.", + "cve": "CVE-2024-3651", + "id": "pyup.io-73013", + "more_info_path": "/vulnerabilities/CVE-2024-3651/73013", "specs": [ "<0.2.1" ], @@ -10948,6 +11039,16 @@ "<0.2.1" ], "v": "<0.2.1" + }, + { + "advisory": "Async-batcher's update to a newer version of scikit-learn addresses CVE-2024-5206.", + "cve": "CVE-2024-5206", + "id": "pyup.io-73033", + "more_info_path": "/vulnerabilities/CVE-2024-5206/73033", + "specs": [ + "<0.2.1" + ], + "v": "<0.2.1" } ], "async-firebase": [ @@ -11042,20 +11143,20 @@ ], "asyncssh": [ { - "advisory": "An issue in AsyncSSH v2.14.0 and earlier allows attackers to control the remote end of an SSH client session via packet injection/removal and shell emulation.", - "cve": "CVE-2023-46446", - "id": "pyup.io-65384", - "more_info_path": "/vulnerabilities/CVE-2023-46446/65384", + "advisory": "An issue in AsyncSSH v2.14.0 and earlier allows attackers to control the extension info message (RFC 8308) via a man-in-the-middle attack.", + "cve": "CVE-2023-46445", + "id": "pyup.io-65385", + "more_info_path": "/vulnerabilities/CVE-2023-46445/65385", "specs": [ "<2.14.1" ], "v": "<2.14.1" }, { - "advisory": "An issue in AsyncSSH v2.14.0 and earlier allows attackers to control the extension info message (RFC 8308) via a man-in-the-middle attack.", - "cve": "CVE-2023-46445", - "id": "pyup.io-65385", - "more_info_path": "/vulnerabilities/CVE-2023-46445/65385", + "advisory": "An issue in AsyncSSH v2.14.0 and earlier allows attackers to control the remote end of an SSH client session via packet injection/removal and shell emulation.", + "cve": "CVE-2023-46446", + "id": "pyup.io-65384", + "more_info_path": "/vulnerabilities/CVE-2023-46446/65384", "specs": [ "<2.14.1" ], @@ -11093,6 +11194,16 @@ } ], "asyncua": [ + { + "advisory": "Asyncua 0.9.96 includes a fix for CVE-2022-25304: Denial of Service (DoS) due to a missing limitation on the number of received chunks - per single session or in total for all concurrent sessions. An attacker can exploit this vulnerability by sending an unlimited number of huge chunks (e.g. 2GB each) without sending the Final closing chunk.\r\nhttps://github.com/FreeOpcUa/opcua-asyncio/commit/01c7acf047887b62d979cd4373d370e72a4b9057", + "cve": "CVE-2022-25304", + "id": "pyup.io-50830", + "more_info_path": "/vulnerabilities/CVE-2022-25304/50830", + "specs": [ + "<0.9.96" + ], + "v": "<0.9.96" + }, { "advisory": "Asyncua 0.9.96 includes a fix for CVE-2023-26150: Improper Authentication such that it is possible to access Address Space without encryption and authentication. **Note:** This issue is a result of missing checks for services that require an active session.\r\nhttps://github.com/FreeOpcUa/opcua-asyncio/issues/1014", "cve": "CVE-2023-26150", @@ -11112,16 +11223,6 @@ "<0.9.96" ], "v": "<0.9.96" - }, - { - "advisory": "Asyncua 0.9.96 includes a fix for CVE-2022-25304: Denial of Service (DoS) due to a missing limitation on the number of received chunks - per single session or in total for all concurrent sessions. An attacker can exploit this vulnerability by sending an unlimited number of huge chunks (e.g. 2GB each) without sending the Final closing chunk.\r\nhttps://github.com/FreeOpcUa/opcua-asyncio/commit/01c7acf047887b62d979cd4373d370e72a4b9057", - "cve": "CVE-2022-25304", - "id": "pyup.io-50830", - "more_info_path": "/vulnerabilities/CVE-2022-25304/50830", - "specs": [ - "<0.9.96" - ], - "v": "<0.9.96" } ], "atlasapi": [ @@ -11135,6 +11236,16 @@ ], "v": "<2.0.5" }, + { + "advisory": "Atlasapi 2.0.5 updates its dependency 'pygments' to v2.7.4 to include security fixes.", + "cve": "CVE-2021-27291", + "id": "pyup.io-51566", + "more_info_path": "/vulnerabilities/CVE-2021-27291/51566", + "specs": [ + "<2.0.5" + ], + "v": "<2.0.5" + }, { "advisory": "Atlasapi 2.0.5 updates its dependency 'sphinx' to v3.0.4 to include security fixes.", "cve": "CVE-2020-11023", @@ -11154,16 +11265,6 @@ "<2.0.5" ], "v": "<2.0.5" - }, - { - "advisory": "Atlasapi 2.0.5 updates its dependency 'pygments' to v2.7.4 to include security fixes.", - "cve": "CVE-2021-27291", - "id": "pyup.io-51566", - "more_info_path": "/vulnerabilities/CVE-2021-27291/51566", - "specs": [ - "<2.0.5" - ], - "v": "<2.0.5" } ], "atproto": [ @@ -11498,10 +11599,10 @@ ], "auto-surprise": [ { - "advisory": "Auto-surprise 0.1.7 updates its dependency 'pygments' to v2.7.4 to include security fixes.", - "cve": "CVE-2021-20270", - "id": "pyup.io-44807", - "more_info_path": "/vulnerabilities/CVE-2021-20270/44807", + "advisory": "Auto-surprise 0.1.7 updates its dependency 'jinja2' to v2.11.3 to include a security fix.", + "cve": "CVE-2020-28493", + "id": "pyup.io-40146", + "more_info_path": "/vulnerabilities/CVE-2020-28493/40146", "specs": [ "<0.1.7" ], @@ -11518,10 +11619,10 @@ "v": "<0.1.7" }, { - "advisory": "Auto-surprise 0.1.7 updates its dependency 'jinja2' to v2.11.3 to include a security fix.", - "cve": "CVE-2020-28493", - "id": "pyup.io-40146", - "more_info_path": "/vulnerabilities/CVE-2020-28493/40146", + "advisory": "Auto-surprise 0.1.7 updates its dependency 'pygments' to v2.7.4 to include security fixes.", + "cve": "CVE-2021-20270", + "id": "pyup.io-44807", + "more_info_path": "/vulnerabilities/CVE-2021-20270/44807", "specs": [ "<0.1.7" ], @@ -11573,9 +11674,9 @@ "autocrop": [ { "advisory": "Autocrop 1.1.1 updates the minimum requirement of its dependency 'pillow' to v8.1.0 to include security fixes.", - "cve": "CVE-2020-5313", - "id": "pyup.io-42933", - "more_info_path": "/vulnerabilities/CVE-2020-5313/42933", + "cve": "CVE-2020-35653", + "id": "pyup.io-42939", + "more_info_path": "/vulnerabilities/CVE-2020-35653/42939", "specs": [ "<1.1.1" ], @@ -11593,9 +11694,9 @@ }, { "advisory": "Autocrop 1.1.1 updates the minimum requirement of its dependency 'pillow' to v8.1.0 to include security fixes.", - "cve": "CVE-2020-11538", - "id": "pyup.io-42934", - "more_info_path": "/vulnerabilities/CVE-2020-11538/42934", + "cve": "CVE-2020-35655", + "id": "pyup.io-42940", + "more_info_path": "/vulnerabilities/CVE-2020-35655/42940", "specs": [ "<1.1.1" ], @@ -11603,9 +11704,9 @@ }, { "advisory": "Autocrop 1.1.1 updates the minimum requirement of its dependency 'pillow' to v8.1.0 to include security fixes.", - "cve": "CVE-2020-35655", - "id": "pyup.io-42940", - "more_info_path": "/vulnerabilities/CVE-2020-35655/42940", + "cve": "CVE-2020-5310", + "id": "pyup.io-42932", + "more_info_path": "/vulnerabilities/CVE-2020-5310/42932", "specs": [ "<1.1.1" ], @@ -11613,9 +11714,9 @@ }, { "advisory": "Autocrop 1.1.1 updates the minimum requirement of its dependency 'pillow' to v8.1.0 to include security fixes.", - "cve": "CVE-2020-35654", - "id": "pyup.io-42938", - "more_info_path": "/vulnerabilities/CVE-2020-35654/42938", + "cve": "CVE-2020-11538", + "id": "pyup.io-42934", + "more_info_path": "/vulnerabilities/CVE-2020-11538/42934", "specs": [ "<1.1.1" ], @@ -11623,9 +11724,9 @@ }, { "advisory": "Autocrop 1.1.1 updates the minimum requirement of its dependency 'pillow' to v8.1.0 to include security fixes.", - "cve": "CVE-2020-5310", - "id": "pyup.io-42932", - "more_info_path": "/vulnerabilities/CVE-2020-5310/42932", + "cve": "CVE-2020-35654", + "id": "pyup.io-42938", + "more_info_path": "/vulnerabilities/CVE-2020-35654/42938", "specs": [ "<1.1.1" ], @@ -11663,9 +11764,9 @@ }, { "advisory": "Autocrop 1.1.1 updates the minimum requirement of its dependency 'pillow' to v8.1.0 to include security fixes.", - "cve": "CVE-2020-35653", - "id": "pyup.io-42939", - "more_info_path": "/vulnerabilities/CVE-2020-35653/42939", + "cve": "CVE-2020-5313", + "id": "pyup.io-42933", + "more_info_path": "/vulnerabilities/CVE-2020-5313/42933", "specs": [ "<1.1.1" ], @@ -11707,9 +11808,9 @@ }, { "advisory": "Autogluon 0.5.3 updates its dependency 'transformers' requirement to \">=4.23.0,<4.24.0\" to include security fixes.", - "cve": "PVE-2022-51450", - "id": "pyup.io-51940", - "more_info_path": "/vulnerabilities/PVE-2022-51450/51940", + "cve": "CVE-2022-1941", + "id": "pyup.io-51994", + "more_info_path": "/vulnerabilities/CVE-2022-1941/51994", "specs": [ "<0.5.3" ], @@ -11717,9 +11818,9 @@ }, { "advisory": "Autogluon 0.5.3 updates its dependency 'transformers' requirement to \">=4.23.0,<4.24.0\" to include security fixes.", - "cve": "CVE-2022-1941", - "id": "pyup.io-51994", - "more_info_path": "/vulnerabilities/CVE-2022-1941/51994", + "cve": "PVE-2022-51450", + "id": "pyup.io-51940", + "more_info_path": "/vulnerabilities/PVE-2022-51450/51940", "specs": [ "<0.5.3" ], @@ -11767,9 +11868,9 @@ }, { "advisory": "Autogluon 0.4.1 updates its dependency 'ray' minimum requirement to v1.10.0 to include security fixes.", - "cve": "CVE-2021-45046", - "id": "pyup.io-48622", - "more_info_path": "/vulnerabilities/CVE-2021-45046/48622", + "cve": "CVE-2021-45105", + "id": "pyup.io-48623", + "more_info_path": "/vulnerabilities/CVE-2021-45105/48623", "specs": [ ">=0.4.0,<0.4.1" ], @@ -11777,9 +11878,9 @@ }, { "advisory": "Autogluon 0.4.1 updates its dependency 'ray' minimum requirement to v1.10.0 to include security fixes.", - "cve": "CVE-2021-44832", - "id": "pyup.io-48624", - "more_info_path": "/vulnerabilities/CVE-2021-44832/48624", + "cve": "CVE-2021-44228", + "id": "pyup.io-48621", + "more_info_path": "/vulnerabilities/CVE-2021-44228/48621", "specs": [ ">=0.4.0,<0.4.1" ], @@ -11787,9 +11888,9 @@ }, { "advisory": "Autogluon 0.4.1 updates its dependency 'ray' minimum requirement to v1.10.0 to include security fixes.", - "cve": "PVE-2021-42426", - "id": "pyup.io-48620", - "more_info_path": "/vulnerabilities/PVE-2021-42426/48620", + "cve": "CVE-2021-44832", + "id": "pyup.io-48624", + "more_info_path": "/vulnerabilities/CVE-2021-44832/48624", "specs": [ ">=0.4.0,<0.4.1" ], @@ -11797,9 +11898,9 @@ }, { "advisory": "Autogluon 0.4.1 updates its dependency 'ray' minimum requirement to v1.10.0 to include security fixes.", - "cve": "CVE-2021-45105", - "id": "pyup.io-48623", - "more_info_path": "/vulnerabilities/CVE-2021-45105/48623", + "cve": "CVE-2021-45046", + "id": "pyup.io-48622", + "more_info_path": "/vulnerabilities/CVE-2021-45046/48622", "specs": [ ">=0.4.0,<0.4.1" ], @@ -11807,9 +11908,9 @@ }, { "advisory": "Autogluon 0.4.1 updates its dependency 'ray' minimum requirement to v1.10.0 to include security fixes.", - "cve": "CVE-2021-44228", - "id": "pyup.io-48621", - "more_info_path": "/vulnerabilities/CVE-2021-44228/48621", + "cve": "PVE-2021-42426", + "id": "pyup.io-48620", + "more_info_path": "/vulnerabilities/PVE-2021-42426/48620", "specs": [ ">=0.4.0,<0.4.1" ], @@ -11903,9 +12004,9 @@ "av": [ { "advisory": "Av 9.0.1 updates wheel components to include security fixes [openjpeg].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", - "cve": "CVE-2020-27841", - "id": "pyup.io-45831", - "more_info_path": "/vulnerabilities/CVE-2020-27841/45831", + "cve": "CVE-2019-12973", + "id": "pyup.io-45830", + "more_info_path": "/vulnerabilities/CVE-2019-12973/45830", "specs": [ "<9.0.1" ], @@ -11913,9 +12014,9 @@ }, { "advisory": "Av 9.0.1 updates wheel components to include security fixes [openjpeg].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", - "cve": "CVE-2019-12973", - "id": "pyup.io-45830", - "more_info_path": "/vulnerabilities/CVE-2019-12973/45830", + "cve": "CVE-2020-6851", + "id": "pyup.io-45827", + "more_info_path": "/vulnerabilities/CVE-2020-6851/45827", "specs": [ "<9.0.1" ], @@ -11923,9 +12024,19 @@ }, { "advisory": "Av 9.0.1 updates wheel components to include security fixes [openjpeg].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", - "cve": "CVE-2020-27824", - "id": "pyup.io-45832", - "more_info_path": "/vulnerabilities/CVE-2020-27824/45832", + "cve": "CVE-2020-27823", + "id": "pyup.io-45825", + "more_info_path": "/vulnerabilities/CVE-2020-27823/45825", + "specs": [ + "<9.0.1" + ], + "v": "<9.0.1" + }, + { + "advisory": "Av 9.0.1 updates wheel components to include security fixes [openjpeg].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", + "cve": "CVE-2020-27844", + "id": "pyup.io-45824", + "more_info_path": "/vulnerabilities/CVE-2020-27844/45824", "specs": [ "<9.0.1" ], @@ -11951,16 +12062,6 @@ ], "v": "<9.0.1" }, - { - "advisory": "Av 9.0.1 updates wheel components to include security fixes [openjpeg].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", - "cve": "CVE-2020-8112", - "id": "pyup.io-45822", - "more_info_path": "/vulnerabilities/CVE-2020-8112/45822", - "specs": [ - "<9.0.1" - ], - "v": "<9.0.1" - }, { "advisory": "Av 9.0.1 updates wheel components to include security fixes [wavpack].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", "cve": "CVE-2020-35738", @@ -11971,16 +12072,6 @@ ], "v": "<9.0.1" }, - { - "advisory": "Av 9.0.1 updates wheel components to include security fixes [openjpeg].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", - "cve": "CVE-2020-27814", - "id": "pyup.io-45826", - "more_info_path": "/vulnerabilities/CVE-2020-27814/45826", - "specs": [ - "<9.0.1" - ], - "v": "<9.0.1" - }, { "advisory": "Av 9.0.1 updates wheel components to include security fixes [openjpeg].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", "cve": "CVE-2020-15389", @@ -11993,19 +12084,19 @@ }, { "advisory": "Av 9.0.1 updates wheel components to include security fixes [openjpeg].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", - "cve": "CVE-2020-6851", - "id": "pyup.io-45827", - "more_info_path": "/vulnerabilities/CVE-2020-6851/45827", + "cve": "CVE-2020-27824", + "id": "pyup.io-45832", + "more_info_path": "/vulnerabilities/CVE-2020-27824/45832", "specs": [ "<9.0.1" ], "v": "<9.0.1" }, { - "advisory": "Av 9.0.1 updates wheel components to include security fixes [gmp].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", - "cve": "CVE-2021-43618", - "id": "pyup.io-45837", - "more_info_path": "/vulnerabilities/CVE-2021-43618/45837", + "advisory": "Av 9.0.1 updates wheel components to include security fixes [openjpeg].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", + "cve": "CVE-2020-27814", + "id": "pyup.io-45826", + "more_info_path": "/vulnerabilities/CVE-2020-27814/45826", "specs": [ "<9.0.1" ], @@ -12023,19 +12114,19 @@ }, { "advisory": "Av 9.0.1 updates wheel components to include security fixes [openjpeg].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", - "cve": "CVE-2020-27842", - "id": "pyup.io-45834", - "more_info_path": "/vulnerabilities/CVE-2020-27842/45834", + "cve": "CVE-2020-27841", + "id": "pyup.io-45831", + "more_info_path": "/vulnerabilities/CVE-2020-27841/45831", "specs": [ "<9.0.1" ], "v": "<9.0.1" }, { - "advisory": "Av 9.0.1 updates wheel components to include security fixes [openjpeg].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", - "cve": "CVE-2020-27823", - "id": "pyup.io-45825", - "more_info_path": "/vulnerabilities/CVE-2020-27823/45825", + "advisory": "Av 9.0.1 updates wheel components to include security fixes [gnutls].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", + "cve": "CVE-2021-20231", + "id": "pyup.io-45835", + "more_info_path": "/vulnerabilities/CVE-2021-20231/45835", "specs": [ "<9.0.1" ], @@ -12043,19 +12134,29 @@ }, { "advisory": "Av 9.0.1 updates wheel components to include security fixes [openjpeg].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", - "cve": "CVE-2020-27844", - "id": "pyup.io-45824", - "more_info_path": "/vulnerabilities/CVE-2020-27844/45824", + "cve": "CVE-2020-27842", + "id": "pyup.io-45834", + "more_info_path": "/vulnerabilities/CVE-2020-27842/45834", "specs": [ "<9.0.1" ], "v": "<9.0.1" }, { - "advisory": "Av 9.0.1 updates wheel components to include security fixes [gnutls].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", - "cve": "CVE-2021-20231", - "id": "pyup.io-45835", - "more_info_path": "/vulnerabilities/CVE-2021-20231/45835", + "advisory": "Av 9.0.1 updates wheel components to include security fixes [gmp].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", + "cve": "CVE-2021-43618", + "id": "pyup.io-45837", + "more_info_path": "/vulnerabilities/CVE-2021-43618/45837", + "specs": [ + "<9.0.1" + ], + "v": "<9.0.1" + }, + { + "advisory": "Av 9.0.1 updates wheel components to include security fixes [openjpeg].\r\nhttps://github.com/PyAV-Org/PyAV/issues/901", + "cve": "CVE-2020-8112", + "id": "pyup.io-45822", + "more_info_path": "/vulnerabilities/CVE-2020-8112/45822", "specs": [ "<9.0.1" ], @@ -12063,9 +12164,9 @@ }, { "advisory": "Av 9.1.0 updates 'FFmpeg' binary wheels to fix security vulnerabilities.\r\nhttps://github.com/PyAV-Org/PyAV/commit/9cbe441d637be15d5b4b57211a7df3958c3c0a02", - "cve": "CVE-2022-23308", - "id": "pyup.io-47836", - "more_info_path": "/vulnerabilities/CVE-2022-23308/47836", + "cve": "CVE-2020-26682", + "id": "pyup.io-47837", + "more_info_path": "/vulnerabilities/CVE-2020-26682/47837", "specs": [ "<9.1.0" ], @@ -12083,9 +12184,9 @@ }, { "advisory": "Av 9.1.0 updates 'FFmpeg' binary wheels to fix security vulnerabilities.\r\nhttps://github.com/PyAV-Org/PyAV/commit/9cbe441d637be15d5b4b57211a7df3958c3c0a02", - "cve": "CVE-2020-26682", - "id": "pyup.io-47837", - "more_info_path": "/vulnerabilities/CVE-2020-26682/47837", + "cve": "CVE-2018-10392", + "id": "pyup.io-47802", + "more_info_path": "/vulnerabilities/CVE-2018-10392/47802", "specs": [ "<9.1.0" ], @@ -12093,9 +12194,9 @@ }, { "advisory": "Av 9.1.0 updates 'FFmpeg' binary wheels to fix security vulnerabilities.\r\nhttps://github.com/PyAV-Org/PyAV/commit/9cbe441d637be15d5b4b57211a7df3958c3c0a02", - "cve": "CVE-2018-10392", - "id": "pyup.io-47802", - "more_info_path": "/vulnerabilities/CVE-2018-10392/47802", + "cve": "CVE-2022-23308", + "id": "pyup.io-47836", + "more_info_path": "/vulnerabilities/CVE-2022-23308/47836", "specs": [ "<9.1.0" ], @@ -12391,16 +12492,6 @@ } ], "aws-v4signer": [ - { - "advisory": "Aws-v4signer version 0.6 updates its dependency 'pyyaml' to v5.4 to include security fixes.", - "cve": "CVE-2020-14343", - "id": "pyup.io-49033", - "more_info_path": "/vulnerabilities/CVE-2020-14343/49033", - "specs": [ - "<0.6" - ], - "v": "<0.6" - }, { "advisory": "Aws-v4signer version 0.6 updates its dependency 'pyyaml' to v5.4 to include security fixes.", "cve": "CVE-2020-1747", @@ -12421,6 +12512,16 @@ ], "v": "<0.6" }, + { + "advisory": "Aws-v4signer version 0.6 updates its dependency 'pyyaml' to v5.4 to include security fixes.", + "cve": "CVE-2020-14343", + "id": "pyup.io-49033", + "more_info_path": "/vulnerabilities/CVE-2020-14343/49033", + "specs": [ + "<0.6" + ], + "v": "<0.6" + }, { "advisory": "Aws-v4signer version 0.6 updates its dependency 'pyyaml' to v5.4 to include security fixes.", "cve": "CVE-2019-20477", @@ -13097,20 +13198,20 @@ "v": "<0.4.1" }, { - "advisory": "Baybe 0.8.2 has updated its onnx dependency to version 1.16.0 or newer to address the security issue CVE-2024-27319.", + "advisory": "Baybe 0.8.2 has updated its onnx dependency to version 1.16.0 or newer to address the security issue CVE-2024-27318.", "cve": "CVE-2024-27318", - "id": "pyup.io-66984", - "more_info_path": "/vulnerabilities/CVE-2024-27318/66984", + "id": "pyup.io-66978", + "more_info_path": "/vulnerabilities/CVE-2024-27318/66978", "specs": [ "<0.8.2" ], "v": "<0.8.2" }, { - "advisory": "Baybe 0.8.2 has updated its onnx dependency to version 1.16.0 or newer to address the security issue CVE-2024-27318.", + "advisory": "Baybe 0.8.2 has updated its onnx dependency to version 1.16.0 or newer to address the security issue CVE-2024-27319.", "cve": "CVE-2024-27318", - "id": "pyup.io-66978", - "more_info_path": "/vulnerabilities/CVE-2024-27318/66978", + "id": "pyup.io-66984", + "more_info_path": "/vulnerabilities/CVE-2024-27318/66984", "specs": [ "<0.8.2" ], @@ -13411,6 +13512,16 @@ ], "v": "<2.2" }, + { + "advisory": "Affected versions of BenchExec are vulnerable to a Race Condition (CWE-362). An attacker could manipulate the timing of transient unit creation, leading to inaccurate benchmarking results or denial of service. The vulnerability exists in the asynchronous StartTransientUnit method within cgroupsv2.py. Exploiting this requires precise control over systemd interactions. Mitigation involves upgrading BenchExec to the version which implements synchronous handling to eliminate the race condition.", + "cve": "PVE-2024-74038", + "id": "pyup.io-74038", + "more_info_path": "/vulnerabilities/PVE-2024-74038/74038", + "specs": [ + "<3.26" + ], + "v": "<3.26" + }, { "advisory": "Benchexec 2.2 fixes a security issue. Since BenchExec 2.1, the setup of the container for the tool-info module (which was added in BenchExec 1.20) could silently fail, for example if user namespaces are disabled on the system. In this case the tool-info module would be executed outside of the container. Run execution was not affected.\r\nhttps://github.com/sosy-lab/benchexec/commit/dea58cac6e066d89e3ab3e374c6472d575493d07", "cve": "PVE-2021-37510", @@ -13652,20 +13763,20 @@ "v": "<0.8.0" }, { - "advisory": "Bigdl 2.0.0 updates its Maven dependency 'protobuf-java' to v3.19.2 to include a security fix.", - "cve": "CVE-2021-22569", - "id": "pyup.io-45818", - "more_info_path": "/vulnerabilities/CVE-2021-22569/45818", + "advisory": "Bigdl 2.0.0 updates its Maven dependency 'http.version' to v10.1.15 to include security fixes.", + "cve": "CVE-2021-23339", + "id": "pyup.io-45840", + "more_info_path": "/vulnerabilities/CVE-2021-23339/45840", "specs": [ "<2.0.0" ], "v": "<2.0.0" }, { - "advisory": "Bigdl 2.0.0 updates its Maven dependency 'http.version' to v10.1.15 to include security fixes.", - "cve": "CVE-2021-23339", - "id": "pyup.io-45840", - "more_info_path": "/vulnerabilities/CVE-2021-23339/45840", + "advisory": "Bigdl 2.0.0 updates its Maven dependency 'protobuf-java' to v3.19.2 to include a security fix.", + "cve": "CVE-2021-22569", + "id": "pyup.io-45818", + "more_info_path": "/vulnerabilities/CVE-2021-22569/45818", "specs": [ "<2.0.0" ], @@ -13848,20 +13959,20 @@ ], "bin-collect": [ { - "advisory": "The bin-collect package in PyPI before v0.1 included a code execution backdoor inserted by a third party.", - "cve": "CVE-2022-34500", - "id": "pyup.io-70768", - "more_info_path": "/vulnerabilities/CVE-2022-34500/70768", + "advisory": "The bin-collection package in PyPI before v0.1 included a code execution backdoor inserted by a third party.", + "cve": "CVE-2022-34501", + "id": "pyup.io-70770", + "more_info_path": "/vulnerabilities/CVE-2022-34501/70770", "specs": [ "<0.1" ], "v": "<0.1" }, { - "advisory": "The bin-collection package in PyPI before v0.1 included a code execution backdoor inserted by a third party.", - "cve": "CVE-2022-34501", - "id": "pyup.io-70770", - "more_info_path": "/vulnerabilities/CVE-2022-34501/70770", + "advisory": "The bin-collect package in PyPI before v0.1 included a code execution backdoor inserted by a third party.", + "cve": "CVE-2022-34500", + "id": "pyup.io-70768", + "more_info_path": "/vulnerabilities/CVE-2022-34500/70768", "specs": [ "<0.1" ], @@ -14061,6 +14172,16 @@ ], "v": "<5.3.1" }, + { + "advisory": "Bittensor 5.3.1 updates its dependency 'cryptography' to version '41.0.0' to fix a Use After Free vulnerability.\r\nhttps://github.com/opentensor/bittensor/commit/91d13b0fa711621cbf823708d4368b1b387e42c4", + "cve": "CVE-2023-0215", + "id": "pyup.io-59610", + "more_info_path": "/vulnerabilities/CVE-2023-0215/59610", + "specs": [ + "<5.3.1" + ], + "v": "<5.3.1" + }, { "advisory": "Bittensor 5.3.1 updates its dependency 'cryptography' to version '41.0.0' to fix a Timing Attack vulnerability.\r\nhttps://github.com/opentensor/bittensor/commit/91d13b0fa711621cbf823708d4368b1b387e42c4", "cve": "CVE-2022-4304", @@ -14072,10 +14193,20 @@ "v": "<5.3.1" }, { - "advisory": "Bittensor 5.3.1 updates its dependency 'cryptography' to version '41.0.0' to fix a Use After Free vulnerability.\r\nhttps://github.com/opentensor/bittensor/commit/91d13b0fa711621cbf823708d4368b1b387e42c4", - "cve": "CVE-2023-0215", - "id": "pyup.io-59610", - "more_info_path": "/vulnerabilities/CVE-2023-0215/59610", + "advisory": "Bittensor 5.3.1 updates its dependency 'cryptography' to version '41.0.0' to fix a Denial of Service vulnerability.\r\nhttps://github.com/opentensor/bittensor/commit/91d13b0fa711621cbf823708d4368b1b387e42c4", + "cve": "CVE-2022-4450", + "id": "pyup.io-59615", + "more_info_path": "/vulnerabilities/CVE-2022-4450/59615", + "specs": [ + "<5.3.1" + ], + "v": "<5.3.1" + }, + { + "advisory": "Bittensor 5.3.1 updates its dependency 'cryptography' to version '41.0.0' to fix an Expected Behavior Violation vulnerability.\r\nhttps://github.com/opentensor/bittensor/commit/91d13b0fa711621cbf823708d4368b1b387e42c4", + "cve": "CVE-2023-23931", + "id": "pyup.io-59616", + "more_info_path": "/vulnerabilities/CVE-2023-23931/59616", "specs": [ "<5.3.1" ], @@ -14083,9 +14214,19 @@ }, { "advisory": "Bittensor 5.3.1 updates its dependency 'cryptography' to version '41.0.0' to fix a Denial of Service vulnerability.\r\nhttps://github.com/opentensor/bittensor/commit/91d13b0fa711621cbf823708d4368b1b387e42c4", - "cve": "CVE-2023-2650", - "id": "pyup.io-59533", - "more_info_path": "/vulnerabilities/CVE-2023-2650/59533", + "cve": "CVE-2023-0401", + "id": "pyup.io-59608", + "more_info_path": "/vulnerabilities/CVE-2023-0401/59608", + "specs": [ + "<5.3.1" + ], + "v": "<5.3.1" + }, + { + "advisory": "Bittensor 5.3.1 updates its dependency 'cryptography' to version '41.0.0' to fix a Denial of Service vulnerability.\r\nhttps://github.com/opentensor/bittensor/commit/91d13b0fa711621cbf823708d4368b1b387e42c4", + "cve": "CVE-2023-0216", + "id": "pyup.io-59613", + "more_info_path": "/vulnerabilities/CVE-2023-0216/59613", "specs": [ "<5.3.1" ], @@ -14103,9 +14244,9 @@ }, { "advisory": "Bittensor 5.3.1 updates its dependency 'cryptography' to version '41.0.0' to fix a Denial of Service vulnerability.\r\nhttps://github.com/opentensor/bittensor/commit/91d13b0fa711621cbf823708d4368b1b387e42c4", - "cve": "CVE-2023-0401", - "id": "pyup.io-59608", - "more_info_path": "/vulnerabilities/CVE-2023-0401/59608", + "cve": "CVE-2023-2650", + "id": "pyup.io-59533", + "more_info_path": "/vulnerabilities/CVE-2023-2650/59533", "specs": [ "<5.3.1" ], @@ -14132,34 +14273,14 @@ "v": "<5.3.1" }, { - "advisory": "Bittensor 5.3.1 updates its dependency 'cryptography' to version '41.0.0' to fix a Denial of Service vulnerability.\r\nhttps://github.com/opentensor/bittensor/commit/91d13b0fa711621cbf823708d4368b1b387e42c4", - "cve": "CVE-2023-0216", - "id": "pyup.io-59613", - "more_info_path": "/vulnerabilities/CVE-2023-0216/59613", - "specs": [ - "<5.3.1" - ], - "v": "<5.3.1" - }, - { - "advisory": "Bittensor 5.3.1 updates its dependency 'cryptography' to version '41.0.0' to fix a Denial of Service vulnerability.\r\nhttps://github.com/opentensor/bittensor/commit/91d13b0fa711621cbf823708d4368b1b387e42c4", - "cve": "CVE-2022-4450", - "id": "pyup.io-59615", - "more_info_path": "/vulnerabilities/CVE-2022-4450/59615", - "specs": [ - "<5.3.1" - ], - "v": "<5.3.1" - }, - { - "advisory": "Bittensor 5.3.1 updates its dependency 'cryptography' to version '41.0.0' to fix an Expected Behavior Violation vulnerability.\r\nhttps://github.com/opentensor/bittensor/commit/91d13b0fa711621cbf823708d4368b1b387e42c4", - "cve": "CVE-2023-23931", - "id": "pyup.io-59616", - "more_info_path": "/vulnerabilities/CVE-2023-23931/59616", + "advisory": "Bittensor version 6.12.0 updates its cryptography library to versions 42.0.0 and 42.0.5 to address the security vulnerabilities outlined in CVE-2024-26130.", + "cve": "CVE-2023-5363", + "id": "pyup.io-70793", + "more_info_path": "/vulnerabilities/CVE-2023-5363/70793", "specs": [ - "<5.3.1" + "<6.12.0" ], - "v": "<5.3.1" + "v": "<6.12.0" }, { "advisory": "Bittensor version 6.12.0 updates FastAPI to versions 0.99.1 and 0.110.1 to address security issues highlighted in CVE-2024-24762.", @@ -14171,16 +14292,6 @@ ], "v": "<6.12.0" }, - { - "advisory": "Bittensor version 6.12.0 updates its cryptography library to versions 42.0.0 and 42.0.5 to address the security vulnerabilities outlined in CVE-2024-26130.", - "cve": "CVE-2023-5363", - "id": "pyup.io-70793", - "more_info_path": "/vulnerabilities/CVE-2023-5363/70793", - "specs": [ - "<6.12.0" - ], - "v": "<6.12.0" - }, { "advisory": "Bittensor version 6.12.0 updates its `certifi` package to versions 2023.7.22 and 2024.2.2 to address the security issues identified in CVE-2023-37920.", "cve": "CVE-2023-37920", @@ -14467,9 +14578,9 @@ "blendernc": [ { "advisory": "Blendernc 0.6.0 updates its dependency 'pillow' to v9.0.0 to include security fixes.", - "cve": "CVE-2022-22816", - "id": "pyup.io-50126", - "more_info_path": "/vulnerabilities/CVE-2022-22816/50126", + "cve": "PVE-2021-44525", + "id": "pyup.io-50128", + "more_info_path": "/vulnerabilities/PVE-2021-44525/50128", "specs": [ "<0.6.0" ], @@ -14477,9 +14588,9 @@ }, { "advisory": "Blendernc 0.6.0 updates its dependency 'pillow' to v9.0.0 to include security fixes.", - "cve": "PVE-2021-44525", - "id": "pyup.io-50128", - "more_info_path": "/vulnerabilities/PVE-2021-44525/50128", + "cve": "PVE-2022-44524", + "id": "pyup.io-50127", + "more_info_path": "/vulnerabilities/PVE-2022-44524/50127", "specs": [ "<0.6.0" ], @@ -14487,9 +14598,9 @@ }, { "advisory": "Blendernc 0.6.0 updates its dependency 'pillow' to v9.0.0 to include security fixes.", - "cve": "CVE-2022-22815", - "id": "pyup.io-50111", - "more_info_path": "/vulnerabilities/CVE-2022-22815/50111", + "cve": "CVE-2022-22816", + "id": "pyup.io-50126", + "more_info_path": "/vulnerabilities/CVE-2022-22816/50126", "specs": [ "<0.6.0" ], @@ -14497,9 +14608,9 @@ }, { "advisory": "Blendernc 0.6.0 updates its dependency 'pillow' to v9.0.0 to include security fixes.", - "cve": "PVE-2022-44524", - "id": "pyup.io-50127", - "more_info_path": "/vulnerabilities/PVE-2022-44524/50127", + "cve": "CVE-2022-22815", + "id": "pyup.io-50111", + "more_info_path": "/vulnerabilities/CVE-2022-22815/50111", "specs": [ "<0.6.0" ], @@ -14599,16 +14710,6 @@ } ], "boaviztapi": [ - { - "advisory": "Boaviztapi bumped requests from 2.31.0 to 2.32.2 via Dependabot to address CVE-2024-35195.", - "cve": "CVE-2024-35195", - "id": "pyup.io-73400", - "more_info_path": "/vulnerabilities/CVE-2024-35195/73400", - "specs": [ - "<1.3" - ], - "v": "<1.3" - }, { "advisory": "Boaviztapi bumped idna from 3.6 to 3.7 via Dependabot to address CVE-2024-3651.", "cve": "CVE-2024-3651", @@ -14628,6 +14729,16 @@ "<1.3" ], "v": "<1.3" + }, + { + "advisory": "Boaviztapi bumped requests from 2.31.0 to 2.32.2 via Dependabot to address CVE-2024-35195.", + "cve": "CVE-2024-35195", + "id": "pyup.io-73400", + "more_info_path": "/vulnerabilities/CVE-2024-35195/73400", + "specs": [ + "<1.3" + ], + "v": "<1.3" } ], "bobocep": [ @@ -14865,16 +14976,6 @@ } ], "borgmatic": [ - { - "advisory": "Borgmatic is vulnerable to shell injection within the SQLite hook.\r\nhttps://github.com/borgmatic-collective/borgmatic/commit/3c22a8ec164087beb1d292dc114f78f8b6382ae2", - "cve": "PVE-2024-64393", - "id": "pyup.io-64393", - "more_info_path": "/vulnerabilities/PVE-2024-64393/64393", - "specs": [ - "<1.8.7" - ], - "v": "<1.8.7" - }, { "advisory": "Borgmatic is vulnerable to shell injection within the command hook variable/constant interpolation.\r\nhttps://github.com/borgmatic-collective/borgmatic/commit/3c22a8ec164087beb1d292dc114f78f8b6382ae2", "cve": "PVE-2024-64395", @@ -14886,10 +14987,10 @@ "v": "<1.8.7" }, { - "advisory": "Borgmatic is vulnerable to shell injection within the PostgreSQL hook.\r\nhttps://github.com/borgmatic-collective/borgmatic/commit/3c22a8ec164087beb1d292dc114f78f8b6382ae2", - "cve": "PVE-2024-64386", - "id": "pyup.io-64386", - "more_info_path": "/vulnerabilities/PVE-2024-64386/64386", + "advisory": "Borgmatic is vulnerable to shell injection within the MongoDB hook.\r\nhttps://github.com/borgmatic-collective/borgmatic/commit/3c22a8ec164087beb1d292dc114f78f8b6382ae2", + "cve": "PVE-2024-64392", + "id": "pyup.io-64392", + "more_info_path": "/vulnerabilities/PVE-2024-64392/64392", "specs": [ "<1.8.7" ], @@ -14906,10 +15007,20 @@ "v": "<1.8.7" }, { - "advisory": "Borgmatic is vulnerable to shell injection within the MongoDB hook.\r\nhttps://github.com/borgmatic-collective/borgmatic/commit/3c22a8ec164087beb1d292dc114f78f8b6382ae2", - "cve": "PVE-2024-64392", - "id": "pyup.io-64392", - "more_info_path": "/vulnerabilities/PVE-2024-64392/64392", + "advisory": "Borgmatic is vulnerable to shell injection within the PostgreSQL hook.\r\nhttps://github.com/borgmatic-collective/borgmatic/commit/3c22a8ec164087beb1d292dc114f78f8b6382ae2", + "cve": "PVE-2024-64386", + "id": "pyup.io-64386", + "more_info_path": "/vulnerabilities/PVE-2024-64386/64386", + "specs": [ + "<1.8.7" + ], + "v": "<1.8.7" + }, + { + "advisory": "Borgmatic is vulnerable to shell injection within the SQLite hook.\r\nhttps://github.com/borgmatic-collective/borgmatic/commit/3c22a8ec164087beb1d292dc114f78f8b6382ae2", + "cve": "PVE-2024-64393", + "id": "pyup.io-64393", + "more_info_path": "/vulnerabilities/PVE-2024-64393/64393", "specs": [ "<1.8.7" ], @@ -15363,16 +15474,6 @@ ], "v": "<2.0.0" }, - { - "advisory": "Burl 2.0.0 updates its dependency 'django' to v2.2.25 to include security fixes.", - "cve": "CVE-2021-44420", - "id": "pyup.io-46495", - "more_info_path": "/vulnerabilities/CVE-2021-44420/46495", - "specs": [ - "<2.0.0" - ], - "v": "<2.0.0" - }, { "advisory": "Burl 2.0.0 updates its dependency 'django' to v2.2.25 to include security fixes.", "cve": "CVE-2021-33571", @@ -15402,6 +15503,16 @@ "<2.0.0" ], "v": "<2.0.0" + }, + { + "advisory": "Burl 2.0.0 updates its dependency 'django' to v2.2.25 to include security fixes.", + "cve": "CVE-2021-44420", + "id": "pyup.io-46495", + "more_info_path": "/vulnerabilities/CVE-2021-44420/46495", + "specs": [ + "<2.0.0" + ], + "v": "<2.0.0" } ], "buttplug": [ @@ -15542,9 +15653,9 @@ }, { "advisory": "C2cwsgiutils 4.0.0 updates its dependency 'pipenv' to v2020.5.28 to include security fixes.", - "cve": "CVE-2019-11324", - "id": "pyup.io-53060", - "more_info_path": "/vulnerabilities/CVE-2019-11324/53060", + "cve": "CVE-2020-26137", + "id": "pyup.io-53015", + "more_info_path": "/vulnerabilities/CVE-2020-26137/53015", "specs": [ "<4.0.0" ], @@ -15552,29 +15663,29 @@ }, { "advisory": "C2cwsgiutils 4.0.0 updates its dependency 'pipenv' to v2020.5.28 to include security fixes.", - "cve": "CVE-2020-26137", - "id": "pyup.io-53015", - "more_info_path": "/vulnerabilities/CVE-2020-26137/53015", + "cve": "CVE-2019-11324", + "id": "pyup.io-53060", + "more_info_path": "/vulnerabilities/CVE-2019-11324/53060", "specs": [ "<4.0.0" ], "v": "<4.0.0" }, { - "advisory": "C2cwsgiutils 4.1.2 updates its dependency 'mako' to v1.2.2 to include a security fix.", - "cve": "CVE-2022-40023", - "id": "pyup.io-53014", - "more_info_path": "/vulnerabilities/CVE-2022-40023/53014", + "advisory": "C2cwsgiutils 4.1.2 updates its dependency 'lxml' to v4.6.3 to include a security fix.", + "cve": "CVE-2021-28957", + "id": "pyup.io-53061", + "more_info_path": "/vulnerabilities/CVE-2021-28957/53061", "specs": [ "<4.1.2" ], "v": "<4.1.2" }, { - "advisory": "C2cwsgiutils 4.1.2 updates its dependency 'lxml' to v4.6.3 to include a security fix.", - "cve": "CVE-2021-28957", - "id": "pyup.io-53061", - "more_info_path": "/vulnerabilities/CVE-2021-28957/53061", + "advisory": "C2cwsgiutils 4.1.2 updates its dependency 'mako' to v1.2.2 to include a security fix.", + "cve": "CVE-2022-40023", + "id": "pyup.io-53014", + "more_info_path": "/vulnerabilities/CVE-2022-40023/53014", "specs": [ "<4.1.2" ], @@ -15727,17 +15838,37 @@ ], "calibreweb": [ { - "advisory": "Improper Access Control in GitHub repository janeczku/calibre-web prior to 0.6.16.", - "cve": "CVE-2022-0405", - "id": "pyup.io-62586", - "more_info_path": "/vulnerabilities/CVE-2022-0405/62586", + "advisory": "Affected versions of cps in calibre-web are vulnerable to Generation of Error Message Containing Sensitive Information (CWE-209). This vulnerability allows attackers to obtain the names of private shelves through error messages when attempting unauthorized actions such as adding or removing books. The attack vector involves triggering these actions, resulting in logs or user-facing flash messages that include shelf.name in shelf.py. To mitigate, upgrade to the version that removes the exposure of shelf names from error messages, thereby preventing information leakage.", + "cve": "CVE-2021-3986", + "id": "pyup.io-74254", + "more_info_path": "/vulnerabilities/CVE-2021-3986/74254", "specs": [ - "<0.6.16" + "<0.6.15" ], - "v": "<0.6.16" + "v": "<0.6.15" }, { - "advisory": "Improper Authorization in GitHub repository janeczku/calibre-web prior to 0.6.16.", + "advisory": "Affected versions of calibre-web are vulnerable to Missing Authorization (CWE-862). This vulnerability allows unauthorized users to create public shelves, potentially leading to unauthorized data exposure or manipulation. The attack vector involves exploiting the create_shelf method in shelf.py, which fails to verify user permissions before allowing shelf creation. This lack of proper access control enables attackers to perform actions beyond their intended privileges. To mitigate, upgrade to the version that implements proper permission checks in the create_shelf method, ensuring only authorized users can create public shelves.", + "cve": "CVE-2021-3987", + "id": "pyup.io-74255", + "more_info_path": "/vulnerabilities/CVE-2021-3987/74255", + "specs": [ + "<0.6.15" + ], + "v": "<0.6.15" + }, + { + "advisory": "Affected versions of cps in calibre-web are vulnerable to Cross-Site Scripting (CWE-79). This vulnerability allows attackers to inject malicious scripts through upload filename fields by exploiting the use of the .html() method to display filenames, potentially compromising user sessions or executing unauthorized actions. The attack vector involves uploading filenames containing malicious HTML or JavaScript, which are rendered unsafely in the DOM. The vulnerable methods include jQuery\u2019s .html() in edit_books.js. To mitigate, upgrade to the version which replaces .html() with .text(), ensuring safe rendering of filenames.", + "cve": "CVE-2021-3988", + "id": "pyup.io-74257", + "more_info_path": "/vulnerabilities/CVE-2021-3988/74257", + "specs": [ + "<0.6.15" + ], + "v": "<0.6.15" + }, + { + "advisory": "Affected versions of Calibreweb are vulnerable to Improper Authorization.", "cve": "CVE-2022-0406", "id": "pyup.io-62587", "more_info_path": "/vulnerabilities/CVE-2022-0406/62587", @@ -15747,7 +15878,17 @@ "v": "<0.6.16" }, { - "advisory": "Server-Side Request Forgery (SSRF) in GitHub repository janeczku/calibre-web prior to 0.6.18.", + "advisory": "Affected versions of Calibreweb are vulnerable to Improper Access Control.", + "cve": "CVE-2022-0405", + "id": "pyup.io-62586", + "more_info_path": "/vulnerabilities/CVE-2022-0405/62586", + "specs": [ + "<0.6.16" + ], + "v": "<0.6.16" + }, + { + "advisory": "Affected versions of Calibreweb are vulnerable to Server-Side Request Forgery (SSRF).", "cve": "CVE-2022-0939", "id": "pyup.io-62588", "more_info_path": "/vulnerabilities/CVE-2022-0939/62588", @@ -15757,7 +15898,7 @@ "v": "<0.6.18" }, { - "advisory": "Server-Side Request Forgery (SSRF) in GitHub repository janeczku/calibre-web prior to 0.6.18.", + "advisory": "Affected versions of Calibreweb are vulnerable to Server-Side Request Forgery (SSRF).", "cve": "CVE-2022-0990", "id": "pyup.io-62589", "more_info_path": "/vulnerabilities/CVE-2022-0990/62589", @@ -15767,7 +15908,7 @@ "v": "<0.6.18" }, { - "advisory": "Weak Password Requirements in GitHub repository janeczku/calibre-web prior to 0.6.20.", + "advisory": "Affected versions of Calibrewebare are vulnerable to Weak Password Requirements.", "cve": "CVE-2023-2106", "id": "pyup.io-62874", "more_info_path": "/vulnerabilities/CVE-2023-2106/62874", @@ -15777,7 +15918,7 @@ "v": "<0.6.20" }, { - "advisory": "Improper Restriction of Excessive Authentication Attempts in GitHub repository janeczku/calibre-web prior to 0.6.20.\r\n\r\nAlias:\r\nGHSA-jg8w-wgx2-g7q4", + "advisory": "Affected versions of Calibreweb are vulnerable to Improper Restriction of Excessive Authentication Attempts.", "cve": "CVE-2022-2525", "id": "pyup.io-62623", "more_info_path": "/vulnerabilities/CVE-2022-2525/62623", @@ -15787,7 +15928,7 @@ "v": "<0.6.20" }, { - "advisory": "Calibre-Web 0.6.7 prevents authentication bypass. Prior versions had a hardcoded secret key.", + "advisory": "Affected versions of calibreweb are vulnerable to sensitive information disclosure. There was a hardcoded secret key that could lead to authentication bypass.", "cve": "CVE-2020-12627", "id": "pyup.io-42274", "more_info_path": "/vulnerabilities/CVE-2020-12627/42274", @@ -15797,17 +15938,7 @@ "v": "<0.6.7" }, { - "advisory": "calibre-web is vulnerable to Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')", - "cve": "CVE-2021-4170", - "id": "pyup.io-54406", - "more_info_path": "/vulnerabilities/CVE-2021-4170/54406", - "specs": [ - ">=0,<0.6.15" - ], - "v": ">=0,<0.6.15" - }, - { - "advisory": "calibre-web is vulnerable to Cross-Site Request Forgery (CSRF)", + "advisory": "Affected versions of Calibreweb are vulnerable to Cross-Site Request Forgery (CSRF).", "cve": "CVE-2021-4164", "id": "pyup.io-54147", "more_info_path": "/vulnerabilities/CVE-2021-4164/54147", @@ -15817,7 +15948,7 @@ "v": ">=0,<0.6.15" }, { - "advisory": "calibre-web is vulnerable to Business Logic Errors\n\nAffected functions:\ncalibreweb.cps.shelf.check_shelf_is_unique\ncalibreweb.cps.shelf.create_edit_shelf", + "advisory": "Affected versions of Calibreweb are vulnerable to Business Logic Errors.\r\nAffected functions: calibreweb.cps.shelf.check_shelf_is_unique, calibreweb.cps.shelf.create_edit_shelf.", "cve": "CVE-2021-4171", "id": "pyup.io-54146", "more_info_path": "/vulnerabilities/CVE-2021-4171/54146", @@ -15827,7 +15958,17 @@ "v": ">=0,<0.6.15" }, { - "advisory": "calibreweb prior to version 0.6.16 contains an Incorrect Authorization vulnerability.", + "advisory": "Affected versions of Calibreweb are vulnerable to Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting').", + "cve": "CVE-2021-4170", + "id": "pyup.io-54406", + "more_info_path": "/vulnerabilities/CVE-2021-4170/54406", + "specs": [ + ">=0,<0.6.15" + ], + "v": ">=0,<0.6.15" + }, + { + "advisory": "Affected versions of Calibreweb are vulnerable Incorrect Authorization.", "cve": "CVE-2022-0273", "id": "pyup.io-54235", "more_info_path": "/vulnerabilities/CVE-2022-0273/54235", @@ -15837,7 +15978,7 @@ "v": ">=0,<0.6.16" }, { - "advisory": "calibreweb prior to version 0.6.16 contains a Server-Side Request Forgery (SSRF) vulnerability.", + "advisory": "Affected versions of Calibreweb are vulnerable to server-side request forgery (SSRF).", "cve": "CVE-2022-0339", "id": "pyup.io-54237", "more_info_path": "/vulnerabilities/CVE-2022-0339/54237", @@ -15847,7 +15988,7 @@ "v": ">=0,<0.6.16" }, { - "advisory": "calibreweb prior to version 0.6.16 contains a cross-site scripting vulnerability.", + "advisory": "Affected versions of Calibreweb are vulnerable to cross-site scripting (XSS).", "cve": "CVE-2022-0352", "id": "pyup.io-54416", "more_info_path": "/vulnerabilities/CVE-2022-0352/54416", @@ -15857,7 +15998,7 @@ "v": ">=0,<0.6.16" }, { - "advisory": "calibreweb prior to version 0.6.17 is vulnerable to server-side request forgery (SSRF). This is due to an incomplete fix for [CVE-2022-0339](https://github.com/advisories/GHSA-4w8p-x6g8-fv64). The blacklist does not check for `0.0.0.0`, which would result in a payload of `0.0.0.0` resolving to `localhost`.\n\nAffected functions:\ncalibreweb.cps.helper.save_cover_from_url", + "advisory": "Affected versions of Calibreweb are vulnerable to server-side request forgery (SSRF). This is due to an incomplete fix for CVE-2022-0339. The blacklist does not check for `0.0.0.0`, which would result in a payload of `0.0.0.0` resolving to `localhost`.\r\nAffected functions: calibreweb.cps.helper.save_cover_from_url.", "cve": "CVE-2022-0766", "id": "pyup.io-54414", "more_info_path": "/vulnerabilities/CVE-2022-0766/54414", @@ -15867,7 +16008,7 @@ "v": ">=0,<0.6.17" }, { - "advisory": "calibreweb prior to version 0.6.17 is vulnerable to server-side request forgery (SSRF). This is a result of incomplete SSRF protection that can be bypassed via an HTTP redirect. An HTTP server set up to respond with a 302 redirect may redirect a request to `localhost`.\n\nAffected functions:\ncalibreweb.cps.helper.save_cover_from_url", + "advisory": "Affected versions of Calibreweb are vulnerable to server-side request forgery (SSRF). This is a result of incomplete SSRF protection that can be bypassed via an HTTP redirect. An HTTP server set up to respond with a 302 redirect may redirect a request to `localhost`.\r\nAffected functions:calibreweb.cps.helper.save_cover_from_url.", "cve": "CVE-2022-0767", "id": "pyup.io-54419", "more_info_path": "/vulnerabilities/CVE-2022-0767/54419", @@ -15877,7 +16018,7 @@ "v": ">=0,<0.6.17" }, { - "advisory": "Calibre-Web before 0.6.18 allows user table SQL Injection.", + "advisory": "Affected versions of Calibreweb are vulnerable to SQL Injection in User table.", "cve": "CVE-2022-30765", "id": "pyup.io-54445", "more_info_path": "/vulnerabilities/CVE-2022-30765/54445", @@ -15887,7 +16028,7 @@ "v": ">=0,<0.6.18" }, { - "advisory": "In \"Calibre-web\" application, v0.6.0 to v0.6.12, are vulnerable to Stored XSS in \"Metadata\". An attacker that has access to edit the metadata information, can inject JavaScript payload in the description field. When a victim tries to open the file, XSS will be triggered.", + "advisory": "Calibreweb versions 0.6.0 to 0.6.12 are vulnerable to Stored XSS in \"Metadata\". An attacker that has access to edit the metadata information, can inject JavaScript payload in the description field. When a victim tries to open the file, XSS will be triggered.", "cve": "CVE-2021-25964", "id": "pyup.io-62667", "more_info_path": "/vulnerabilities/CVE-2021-25964/62667", @@ -15986,16 +16127,6 @@ } ], "cancat": [ - { - "advisory": "Cancat 2.0.0 and prior uses a version of Arduino IDE that depends on a version of 'log4j' containing severe and critical vulnerabilities.", - "cve": "CVE-2021-45105", - "id": "pyup.io-43586", - "more_info_path": "/vulnerabilities/CVE-2021-45105/43586", - "specs": [ - "<=2.0.0" - ], - "v": "<=2.0.0" - }, { "advisory": "Cancat 2.0.0 and prior uses a version of Arduino IDE that depends on a version of 'log4j' containing severe and critical vulnerabilities.", "cve": "CVE-2021-45046", @@ -16025,6 +16156,16 @@ "<=2.0.0" ], "v": "<=2.0.0" + }, + { + "advisory": "Cancat 2.0.0 and prior uses a version of Arduino IDE that depends on a version of 'log4j' containing severe and critical vulnerabilities.", + "cve": "CVE-2021-45105", + "id": "pyup.io-43586", + "more_info_path": "/vulnerabilities/CVE-2021-45105/43586", + "specs": [ + "<=2.0.0" + ], + "v": "<=2.0.0" } ], "candig-server": [ @@ -16103,20 +16244,20 @@ "v": "<2.18" }, { - "advisory": "Canvaslms 2.18 updates its dependency 'cryptography' to version '41.0.2' to include a fix for an Improper Certificate Validation vulnerability.\r\nhttps://github.com/dbosk/canvaslms/pull/100", - "cve": "CVE-2023-38325", - "id": "pyup.io-60120", - "more_info_path": "/vulnerabilities/CVE-2023-38325/60120", + "advisory": "Canvaslms 2.18 updates its dependency 'pygments' to version '2.15.1' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/dbosk/canvaslms/pull/100", + "cve": "CVE-2022-40896", + "id": "pyup.io-60121", + "more_info_path": "/vulnerabilities/CVE-2022-40896/60121", "specs": [ "<2.18" ], "v": "<2.18" }, { - "advisory": "Canvaslms 2.18 updates its dependency 'pygments' to version '2.15.1' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/dbosk/canvaslms/pull/100", - "cve": "CVE-2022-40896", - "id": "pyup.io-60121", - "more_info_path": "/vulnerabilities/CVE-2022-40896/60121", + "advisory": "Canvaslms 2.18 updates its dependency 'cryptography' to version '41.0.2' to include a fix for an Improper Certificate Validation vulnerability.\r\nhttps://github.com/dbosk/canvaslms/pull/100", + "cve": "CVE-2023-38325", + "id": "pyup.io-60120", + "more_info_path": "/vulnerabilities/CVE-2023-38325/60120", "specs": [ "<2.18" ], @@ -16249,6 +16390,19 @@ "v": "<1.2.2" } ], + "cascadev": [ + { + "advisory": "The OpenSSF Package Analysis project has identified the PyPI package 'deepsolid' version 99.6 as malicious because it communicates with a domain associated with malicious activity, indicating potential security risks.", + "cve": "PVE-2024-74244", + "id": "pyup.io-74244", + "more_info_path": "/vulnerabilities/PVE-2024-74244/74244", + "specs": [ + ">=0", + "<=0" + ], + "v": ">=0,<=0" + } + ], "case-utils": [ { "advisory": "Case-utils is affected by an information leakage vulnerability. The vulnerability stems from a Python function, 'cdo_local_uuid.local_uuid()', and its original implementation 'case_utils.local_uuid()'.\r\nhttps://github.com/Cyber-Domain-Ontology/CDO-Utility-Local-UUID/security/advisories/GHSA-rgrf-6mf5-m882", @@ -16271,6 +16425,16 @@ } ], "cashocs": [ + { + "advisory": "Cashocs version 2.0.0 updates its pygments dependency to version 2.7.4 from the previous 2.5.2, addressing the vulnerability identified as CVE-2021-20270.\r\nhttps://github.com/sblauth/cashocs/pull/141/commits/1fb563e91e1b4d564cb4784c7c812bf27c7e15b7", + "cve": "CVE-2021-20270", + "id": "pyup.io-64944", + "more_info_path": "/vulnerabilities/CVE-2021-20270/64944", + "specs": [ + "<2.0.0" + ], + "v": "<2.0.0" + }, { "advisory": "Cashocs version 2.0.0 updates its pygments dependency to version 2.7.4 from the previous 2.5.2, addressing the vulnerability identified as CVE-2021-27291.\r\nhttps://github.com/sblauth/cashocs/pull/141/commits/1fb563e91e1b4d564cb4784c7c812bf27c7e15b7", "cve": "CVE-2021-27291", @@ -16292,20 +16456,10 @@ "v": "<2.0.0" }, { - "advisory": "Cashocs version 2.0.0 updates its pygments dependency to version 2.7.4 from the previous 2.5.2, addressing the vulnerability identified as CVE-2021-20270.\r\nhttps://github.com/sblauth/cashocs/pull/141/commits/1fb563e91e1b4d564cb4784c7c812bf27c7e15b7", - "cve": "CVE-2021-20270", - "id": "pyup.io-64944", - "more_info_path": "/vulnerabilities/CVE-2021-20270/64944", - "specs": [ - "<2.0.0" - ], - "v": "<2.0.0" - }, - { - "advisory": "Cashocs version 2.1.0 updates its fonttools dependency from version 4.38.0 to 4.43.0 to address the security issue identified as CVE-2023-45139.\r\nhttps://github.com/sblauth/cashocs/pull/372/commits/c15b23e743b3046b8afae8b6a0967044f163c8ce", - "cve": "CVE-2023-45139", - "id": "pyup.io-64980", - "more_info_path": "/vulnerabilities/CVE-2023-45139/64980", + "advisory": "Cashocs version 2.1.0 updates its Numpy dependency to 1.22.2 from the earlier version 1.21.3. This upgrade is in response to addressing the security vulnerability designated as CVE-2021-41495.\r\nhttps://github.com/sblauth/cashocs/pull/345", + "cve": "CVE-2021-41495", + "id": "pyup.io-64982", + "more_info_path": "/vulnerabilities/CVE-2021-41495/64982", "specs": [ "<2.1.0" ], @@ -16332,10 +16486,10 @@ "v": "<2.1.0" }, { - "advisory": "Cashocs version 2.1.0 updates its Numpy dependency to 1.22.2 from the earlier version 1.21.3. This upgrade is in response to addressing the security vulnerability designated as CVE-2021-41495.\r\nhttps://github.com/sblauth/cashocs/pull/345", - "cve": "CVE-2021-41495", - "id": "pyup.io-64982", - "more_info_path": "/vulnerabilities/CVE-2021-41495/64982", + "advisory": "Cashocs version 2.1.0 updates its fonttools dependency from version 4.38.0 to 4.43.0 to address the security issue identified as CVE-2023-45139.\r\nhttps://github.com/sblauth/cashocs/pull/372/commits/c15b23e743b3046b8afae8b6a0967044f163c8ce", + "cve": "CVE-2023-45139", + "id": "pyup.io-64980", + "more_info_path": "/vulnerabilities/CVE-2023-45139/64980", "specs": [ "<2.1.0" ], @@ -16344,20 +16498,20 @@ ], "cassandra-medusa": [ { - "advisory": "Cassandra-medusa version 0.20.0 has upgraded its Cryptography dependency to version 42.0.2 from 35.0, in response to CVE-2023-6129.", - "cve": "CVE-2023-6129", - "id": "pyup.io-67139", - "more_info_path": "/vulnerabilities/CVE-2023-6129/67139", + "advisory": "Cassandra-medusa version 0.20.0 upgrades its Pycryptodome dependency to 3.19.1 from the previous version 3.19.0, aiming to address the security concerns outlined in CVE-2023-52323.", + "cve": "CVE-2023-52323", + "id": "pyup.io-67422", + "more_info_path": "/vulnerabilities/CVE-2023-52323/67422", "specs": [ "<0.20.0" ], "v": "<0.20.0" }, { - "advisory": "Cassandra-medusa version 0.20.0 upgrades its Pycryptodome dependency to 3.19.1 from the previous version 3.19.0, aiming to address the security concerns outlined in CVE-2023-52323.", - "cve": "CVE-2023-52323", - "id": "pyup.io-67422", - "more_info_path": "/vulnerabilities/CVE-2023-52323/67422", + "advisory": "Cassandra-medusa version 0.20.0 has upgraded its Cryptography dependency to version 42.0.2 from 35.0, in response to CVE-2023-6129.", + "cve": "CVE-2023-6129", + "id": "pyup.io-67139", + "more_info_path": "/vulnerabilities/CVE-2023-6129/67139", "specs": [ "<0.20.0" ], @@ -16408,60 +16562,90 @@ "v": "<0.26" }, { - "advisory": "Catboost 1.2.1 updates its NPM dependency 'tar' to version '6.1.15' to include a fix for an Arbitrary File Write vulnerability.\r\nhttps://github.com/catboost/catboost/commit/f54bd997762dede21c31022ae27b7fd5be36c925", - "cve": "CVE-2021-37713", - "id": "pyup.io-60748", - "more_info_path": "/vulnerabilities/CVE-2021-37713/60748", + "advisory": "Catboost 1.2.1 updates its dependency 'snappy-java' to version '1.1.10.1' to include a fix for a DoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/b51a3b2302a1d6b1a596b406efef347c872d9a0e", + "cve": "CVE-2023-34455", + "id": "pyup.io-60767", + "more_info_path": "/vulnerabilities/CVE-2023-34455/60767", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its NPM dependency 'loader-utils' to version '1.4.2' to include a fix for a Prototype Pollution vulnerability.\r\nhttps://github.com/catboost/catboost/commit/fc169568301a2f20f1329ff0680e4d68dc965485", - "cve": "CVE-2022-37601", - "id": "pyup.io-60754", - "more_info_path": "/vulnerabilities/CVE-2022-37601/60754", + "advisory": "Catboost 1.2.1 updates its dependency 'snappy-java' to version '1.1.10.1' to include a fix for an Integer Overflow vulnerability.\r\nhttps://github.com/catboost/catboost/commit/b51a3b2302a1d6b1a596b406efef347c872d9a0e", + "cve": "CVE-2023-34454", + "id": "pyup.io-60766", + "more_info_path": "/vulnerabilities/CVE-2023-34454/60766", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its dependency 'guava' to version '32.0.0-jre' to include a fix for an Information Disclosure vulnerability.\r\nhttps://github.com/catboost/catboost/commit/cd66946c38a4e2acf9020de5a6f24065c9f16c2d", - "cve": "CVE-2020-8908", - "id": "pyup.io-60772", - "more_info_path": "/vulnerabilities/CVE-2020-8908/60772", + "advisory": "Catboost 1.2.1 updates its dependency 'snappy-java' to version '1.1.10.1' to include a fix for an Integer Overflow vulnerability.\r\nhttps://github.com/catboost/catboost/commit/b51a3b2302a1d6b1a596b406efef347c872d9a0e", + "cve": "CVE-2023-34453", + "id": "pyup.io-60768", + "more_info_path": "/vulnerabilities/CVE-2023-34453/60768", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its dependency 'nanoid' to version '3.3.6' to include a fix for an Information Exposure vulnerability.\r\nhttps://github.com/catboost/catboost/commit/9381a56a05fc7f2b8cecc323c5b26aa60d3703f0", - "cve": "CVE-2021-23566", - "id": "pyup.io-60761", - "more_info_path": "/vulnerabilities/CVE-2021-23566/60761", + "advisory": "Catboost 1.2.1 updates its NPM dependency 'path-parse' to version '1.0.7' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/c5384e8e53f4fee40190dd7d52ec0e1ee92a2560", + "cve": "CVE-2021-23343", + "id": "pyup.io-60758", + "more_info_path": "/vulnerabilities/CVE-2021-23343/60758", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its dependency 'normalize-url' to version '4.5.1' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/44e9f5fcf515e7d3d4bc891388e679ff7bceefb9", - "cve": "CVE-2021-33502", - "id": "pyup.io-60764", - "more_info_path": "/vulnerabilities/CVE-2021-33502/60764", + "advisory": "Catboost 1.2.1 updates its NPM dependency 'webpack' to version '5.76.0' to include a fix for a Sandbox Bypass vulnerability.\r\nhttps://github.com/catboost/catboost/commit/e132d847a527827023eb67165e11f1b05a19564f", + "cve": "CVE-2023-28154", + "id": "pyup.io-60751", + "more_info_path": "/vulnerabilities/CVE-2023-28154/60751", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its dependency 'snappy-java' to version '1.1.10.1' to include a fix for an Integer Overflow vulnerability.\r\nhttps://github.com/catboost/catboost/commit/b51a3b2302a1d6b1a596b406efef347c872d9a0e", - "cve": "CVE-2023-34454", - "id": "pyup.io-60766", - "more_info_path": "/vulnerabilities/CVE-2023-34454/60766", + "advisory": "Catboost 1.2.1 updates its NPM dependency 'tar' to version '6.1.15' to include a fix for an Arbitrary File Write vulnerability.\r\nhttps://github.com/catboost/catboost/commit/f54bd997762dede21c31022ae27b7fd5be36c925", + "cve": "CVE-2021-32804", + "id": "pyup.io-60750", + "more_info_path": "/vulnerabilities/CVE-2021-32804/60750", + "specs": [ + "<1.2.1" + ], + "v": "<1.2.1" + }, + { + "advisory": "Catboost 1.2.1 updates its NPM dependency 'http-cache-semantics' to version '4.1.1' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/d63e29198a2c5a12e7d857b2b068283298488e8d", + "cve": "CVE-2022-25881", + "id": "pyup.io-60745", + "more_info_path": "/vulnerabilities/CVE-2022-25881/60745", + "specs": [ + "<1.2.1" + ], + "v": "<1.2.1" + }, + { + "advisory": "Catboost 1.2.1 updates its dependency 'json5' to version '3.3.6' to include a fix for a Prototype Pollution vulnerability.\r\nhttps://github.com/catboost/catboost/commit/c6393bf6300ecc6d8bcbd98d61927149cb205100", + "cve": "CVE-2022-46175", + "id": "pyup.io-60762", + "more_info_path": "/vulnerabilities/CVE-2022-46175/60762", + "specs": [ + "<1.2.1" + ], + "v": "<1.2.1" + }, + { + "advisory": "Catboost 1.2.1 updates its dependency 'normalize-url' to version '4.5.1' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/44e9f5fcf515e7d3d4bc891388e679ff7bceefb9", + "cve": "CVE-2021-33502", + "id": "pyup.io-60764", + "more_info_path": "/vulnerabilities/CVE-2021-33502/60764", "specs": [ "<1.2.1" ], @@ -16478,20 +16662,20 @@ "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its dependency 'ansi-regex' to version '5.0.1' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/7eebbf8e2fec4d2e3225e819a86c0b14dde72c52", - "cve": "CVE-2021-3807", - "id": "pyup.io-60763", - "more_info_path": "/vulnerabilities/CVE-2021-3807/60763", + "advisory": "Catboost 1.2.1 updates its NPM dependency 'loader-utils' to version '1.4.2' to include a fix for a Prototype Pollution vulnerability.\r\nhttps://github.com/catboost/catboost/commit/fc169568301a2f20f1329ff0680e4d68dc965485", + "cve": "CVE-2022-37601", + "id": "pyup.io-60754", + "more_info_path": "/vulnerabilities/CVE-2022-37601/60754", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its NPM dependency 'tar' to version '6.1.15' to include a fix for an Arbitrary File Write vulnerability.\r\nhttps://github.com/catboost/catboost/commit/f54bd997762dede21c31022ae27b7fd5be36c925", - "cve": "CVE-2021-37701", - "id": "pyup.io-60746", - "more_info_path": "/vulnerabilities/CVE-2021-37701/60746", + "advisory": "Catboost 1.2.1 updates its dependency 'guava' to version '32.0.0-jre' to include a fix for an Information Disclosure vulnerability.\r\nhttps://github.com/catboost/catboost/commit/cd66946c38a4e2acf9020de5a6f24065c9f16c2d", + "cve": "CVE-2020-8908", + "id": "pyup.io-60772", + "more_info_path": "/vulnerabilities/CVE-2020-8908/60772", "specs": [ "<1.2.1" ], @@ -16509,29 +16693,19 @@ }, { "advisory": "Catboost 1.2.1 updates its NPM dependency 'tar' to version '6.1.15' to include a fix for an Arbitrary File Write vulnerability.\r\nhttps://github.com/catboost/catboost/commit/f54bd997762dede21c31022ae27b7fd5be36c925", - "cve": "CVE-2021-32804", - "id": "pyup.io-60750", - "more_info_path": "/vulnerabilities/CVE-2021-32804/60750", - "specs": [ - "<1.2.1" - ], - "v": "<1.2.1" - }, - { - "advisory": "Catboost 1.2.1 updates its NPM dependency 'semver' to version '5.7.2' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/d0183bfcf67525a3ad9f4427e23f1472ad9f588c", - "cve": "CVE-2022-25883", - "id": "pyup.io-60757", - "more_info_path": "/vulnerabilities/CVE-2022-25883/60757", + "cve": "CVE-2021-37701", + "id": "pyup.io-60746", + "more_info_path": "/vulnerabilities/CVE-2021-37701/60746", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its NPM dependency 'minimist' to version '1.2.8' to include a fix for a Prototype Pollution vulnerability.\r\nhttps://github.com/catboost/catboost/commit/63b0cd67faf62ba3fcd7281044dad144f8b6ff4d", - "cve": "CVE-2021-44906", - "id": "pyup.io-60755", - "more_info_path": "/vulnerabilities/CVE-2021-44906/60755", + "advisory": "Catboost 1.2.1 updates its dependency 'junit:junit' to version '4.13.1' to include a fix for an Information Exposure vulnerability.\r\nhttps://github.com/catboost/catboost/commit/95a9dca46d21133005b3d6d66be165384ba77f2d", + "cve": "CVE-2020-15250", + "id": "pyup.io-60765", + "more_info_path": "/vulnerabilities/CVE-2020-15250/60765", "specs": [ "<1.2.1" ], @@ -16548,70 +16722,60 @@ "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its NPM dependency 'webpack' to version '5.76.0' to include a fix for a Sandbox Bypass vulnerability.\r\nhttps://github.com/catboost/catboost/commit/e132d847a527827023eb67165e11f1b05a19564f", - "cve": "CVE-2023-28154", - "id": "pyup.io-60751", - "more_info_path": "/vulnerabilities/CVE-2023-28154/60751", - "specs": [ - "<1.2.1" - ], - "v": "<1.2.1" - }, - { - "advisory": "Catboost 1.2.1 updates its NPM dependency 'http-cache-semantics' to version '4.1.1' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/d63e29198a2c5a12e7d857b2b068283298488e8d", - "cve": "CVE-2022-25881", - "id": "pyup.io-60745", - "more_info_path": "/vulnerabilities/CVE-2022-25881/60745", + "advisory": "Catboost 1.2.1 updates its NPM dependency 'tar' to version '6.1.15' to include a fix for an Arbitrary File Write vulnerability.\r\nhttps://github.com/catboost/catboost/commit/f54bd997762dede21c31022ae27b7fd5be36c925", + "cve": "CVE-2021-32803", + "id": "pyup.io-60749", + "more_info_path": "/vulnerabilities/CVE-2021-32803/60749", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its NPM dependency 'path-parse' to version '1.0.7' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/c5384e8e53f4fee40190dd7d52ec0e1ee92a2560", - "cve": "CVE-2021-23343", - "id": "pyup.io-60758", - "more_info_path": "/vulnerabilities/CVE-2021-23343/60758", + "advisory": "Catboost 1.2.1 updates its NPM dependency 'postcss' to version '8.4.27' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/8143d912bc7364b488ae5a33e2c83e29b988420f", + "cve": "CVE-2021-23382", + "id": "pyup.io-60759", + "more_info_path": "/vulnerabilities/CVE-2021-23382/60759", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its NPM dependency 'tar' to version '6.1.15' to include a fix for an Arbitrary File Write vulnerability.\r\nhttps://github.com/catboost/catboost/commit/f54bd997762dede21c31022ae27b7fd5be36c925", - "cve": "CVE-2021-32803", - "id": "pyup.io-60749", - "more_info_path": "/vulnerabilities/CVE-2021-32803/60749", + "advisory": "Catboost 1.2.1 updates its dependency 'postcss' to version '8.4.27' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/8143d912bc7364b488ae5a33e2c83e29b988420f", + "cve": "CVE-2021-23368", + "id": "pyup.io-60760", + "more_info_path": "/vulnerabilities/CVE-2021-23368/60760", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its NPM dependency 'minimatch' to version '3.1.2' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/3b9820e0bfb7b9e34dbaf0403e95e0dcdc9d9ba3", - "cve": "CVE-2022-3517", - "id": "pyup.io-60744", - "more_info_path": "/vulnerabilities/CVE-2022-3517/60744", + "advisory": "Catboost 1.2.1 updates its NPM dependency 'semver' to version '5.7.2' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/d0183bfcf67525a3ad9f4427e23f1472ad9f588c", + "cve": "CVE-2022-25883", + "id": "pyup.io-60757", + "more_info_path": "/vulnerabilities/CVE-2022-25883/60757", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its NPM dependency 'postcss' to version '8.4.27' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/8143d912bc7364b488ae5a33e2c83e29b988420f", - "cve": "CVE-2021-23382", - "id": "pyup.io-60759", - "more_info_path": "/vulnerabilities/CVE-2021-23382/60759", + "advisory": "Catboost 1.2.1 updates its dependency 'nanoid' to version '3.3.6' to include a fix for an Information Exposure vulnerability.\r\nhttps://github.com/catboost/catboost/commit/9381a56a05fc7f2b8cecc323c5b26aa60d3703f0", + "cve": "CVE-2021-23566", + "id": "pyup.io-60761", + "more_info_path": "/vulnerabilities/CVE-2021-23566/60761", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its dependency 'snappy-java' to version '1.1.10.1' to include a fix for an Integer Overflow vulnerability.\r\nhttps://github.com/catboost/catboost/commit/b51a3b2302a1d6b1a596b406efef347c872d9a0e", - "cve": "CVE-2023-34453", - "id": "pyup.io-60768", - "more_info_path": "/vulnerabilities/CVE-2023-34453/60768", + "advisory": "Catboost 1.2.1 updates its NPM dependency 'minimatch' to version '3.1.2' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/3b9820e0bfb7b9e34dbaf0403e95e0dcdc9d9ba3", + "cve": "CVE-2022-3517", + "id": "pyup.io-60744", + "more_info_path": "/vulnerabilities/CVE-2022-3517/60744", "specs": [ "<1.2.1" ], @@ -16637,16 +16801,6 @@ ], "v": "<1.2.1" }, - { - "advisory": "Catboost 1.2.1 updates its dependency 'json5' to version '3.3.6' to include a fix for a Prototype Pollution vulnerability.\r\nhttps://github.com/catboost/catboost/commit/c6393bf6300ecc6d8bcbd98d61927149cb205100", - "cve": "CVE-2022-46175", - "id": "pyup.io-60762", - "more_info_path": "/vulnerabilities/CVE-2022-46175/60762", - "specs": [ - "<1.2.1" - ], - "v": "<1.2.1" - }, { "advisory": "Catboost 1.2.1 updates its dependency 'jackson-databind' to version '2.13.4.2' to include a fix for a DoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/e88f390a4e2630c9e8a5b82cd01c053a9fd29795", "cve": "CVE-2022-42003", @@ -16658,50 +16812,50 @@ "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its dependency 'snappy-java' to version '1.1.10.1' to include a fix for a DoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/b51a3b2302a1d6b1a596b406efef347c872d9a0e", - "cve": "CVE-2023-34455", - "id": "pyup.io-60767", - "more_info_path": "/vulnerabilities/CVE-2023-34455/60767", + "advisory": "Catboost 1.2.1 updates its dependency 'jackson-databind' to version '2.13.4.2' to include a fix for a DoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/e88f390a4e2630c9e8a5b82cd01c053a9fd29795", + "cve": "CVE-2022-42004", + "id": "pyup.io-60770", + "more_info_path": "/vulnerabilities/CVE-2022-42004/60770", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its dependency 'postcss' to version '8.4.27' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/8143d912bc7364b488ae5a33e2c83e29b988420f", - "cve": "CVE-2021-23368", - "id": "pyup.io-60760", - "more_info_path": "/vulnerabilities/CVE-2021-23368/60760", + "advisory": "Catboost 1.2.1 updates its NPM dependency 'loader-utils' to version '1.4.2' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/fc169568301a2f20f1329ff0680e4d68dc965485", + "cve": "CVE-2022-37599", + "id": "pyup.io-60752", + "more_info_path": "/vulnerabilities/CVE-2022-37599/60752", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its dependency 'junit:junit' to version '4.13.1' to include a fix for an Information Exposure vulnerability.\r\nhttps://github.com/catboost/catboost/commit/95a9dca46d21133005b3d6d66be165384ba77f2d", - "cve": "CVE-2020-15250", - "id": "pyup.io-60765", - "more_info_path": "/vulnerabilities/CVE-2020-15250/60765", + "advisory": "Catboost 1.2.1 updates its NPM dependency 'minimist' to version '1.2.8' to include a fix for a Prototype Pollution vulnerability.\r\nhttps://github.com/catboost/catboost/commit/63b0cd67faf62ba3fcd7281044dad144f8b6ff4d", + "cve": "CVE-2021-44906", + "id": "pyup.io-60755", + "more_info_path": "/vulnerabilities/CVE-2021-44906/60755", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its dependency 'jackson-databind' to version '2.13.4.2' to include a fix for a DoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/e88f390a4e2630c9e8a5b82cd01c053a9fd29795", - "cve": "CVE-2022-42004", - "id": "pyup.io-60770", - "more_info_path": "/vulnerabilities/CVE-2022-42004/60770", + "advisory": "Catboost 1.2.1 updates its NPM dependency 'tar' to version '6.1.15' to include a fix for an Arbitrary File Write vulnerability.\r\nhttps://github.com/catboost/catboost/commit/f54bd997762dede21c31022ae27b7fd5be36c925", + "cve": "CVE-2021-37713", + "id": "pyup.io-60748", + "more_info_path": "/vulnerabilities/CVE-2021-37713/60748", "specs": [ "<1.2.1" ], "v": "<1.2.1" }, { - "advisory": "Catboost 1.2.1 updates its NPM dependency 'loader-utils' to version '1.4.2' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/fc169568301a2f20f1329ff0680e4d68dc965485", - "cve": "CVE-2022-37599", - "id": "pyup.io-60752", - "more_info_path": "/vulnerabilities/CVE-2022-37599/60752", + "advisory": "Catboost 1.2.1 updates its dependency 'ansi-regex' to version '5.0.1' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/catboost/catboost/commit/7eebbf8e2fec4d2e3225e819a86c0b14dde72c52", + "cve": "CVE-2021-3807", + "id": "pyup.io-60763", + "more_info_path": "/vulnerabilities/CVE-2021-3807/60763", "specs": [ "<1.2.1" ], @@ -17004,20 +17158,20 @@ "v": "<0.9.0" }, { - "advisory": "Celery-director 0.9.0 updates its dependency 'redis' to v4.4.4 to include security fixes.", - "cve": "CVE-2023-28859", - "id": "pyup.io-55276", - "more_info_path": "/vulnerabilities/CVE-2023-28859/55276", + "advisory": "Celery-director 0.9.0 updates its dependency 'sentry-sdk' to v1.14.0 to include a security fix.", + "cve": "CVE-2023-28117", + "id": "pyup.io-55277", + "more_info_path": "/vulnerabilities/CVE-2023-28117/55277", "specs": [ "<0.9.0" ], "v": "<0.9.0" }, { - "advisory": "Celery-director 0.9.0 updates its dependency 'sentry-sdk' to v1.14.0 to include a security fix.", - "cve": "CVE-2023-28117", - "id": "pyup.io-55277", - "more_info_path": "/vulnerabilities/CVE-2023-28117/55277", + "advisory": "Celery-director 0.9.0 updates its dependency 'redis' to v4.4.4 to include security fixes.", + "cve": "CVE-2023-28859", + "id": "pyup.io-55276", + "more_info_path": "/vulnerabilities/CVE-2023-28859/55276", "specs": [ "<0.9.0" ], @@ -17026,20 +17180,20 @@ ], "cellxgene": [ { - "advisory": "Cellxgene 0.12.0 updates its NPM dependency 'set-value' to v2.0.1 to include a security fix.", - "cve": "CVE-2021-23440", - "id": "pyup.io-44976", - "more_info_path": "/vulnerabilities/CVE-2021-23440/44976", + "advisory": "Cellxgene 0.12.0 updates its NPM dependency 'eslint-utils' to a version ^1.4.2 to include a security fix.", + "cve": "CVE-2019-15657", + "id": "pyup.io-37801", + "more_info_path": "/vulnerabilities/CVE-2019-15657/37801", "specs": [ "<0.12.0" ], "v": "<0.12.0" }, { - "advisory": "Cellxgene 0.12.0 updates its NPM dependency 'eslint-utils' to a version ^1.4.2 to include a security fix.", - "cve": "CVE-2019-15657", - "id": "pyup.io-37801", - "more_info_path": "/vulnerabilities/CVE-2019-15657/37801", + "advisory": "Cellxgene 0.12.0 updates its NPM dependency 'set-value' to v2.0.1 to include a security fix.", + "cve": "CVE-2021-23440", + "id": "pyup.io-44976", + "more_info_path": "/vulnerabilities/CVE-2021-23440/44976", "specs": [ "<0.12.0" ], @@ -17056,20 +17210,20 @@ "v": "<0.12.0" }, { - "advisory": "Cellxgene 0.12.0 updates several more NPM dependencies to fix security issues.\r\nhttps://github.com/chanzuckerberg/cellxgene/commit/78a43402cb0c1beca5269b3970d4cc31615e4664", - "cve": "PVE-2022-44977", - "id": "pyup.io-44977", - "more_info_path": "/vulnerabilities/PVE-2022-44977/44977", + "advisory": "Cellxgene 0.12.0 stops requiring 'node-fetch' as a NPM dependency to avoid security issues.", + "cve": "CVE-2020-15168", + "id": "pyup.io-44975", + "more_info_path": "/vulnerabilities/CVE-2020-15168/44975", "specs": [ "<0.12.0" ], "v": "<0.12.0" }, { - "advisory": "Cellxgene 0.12.0 stops requiring 'node-fetch' as a NPM dependency to avoid security issues.", - "cve": "CVE-2020-15168", - "id": "pyup.io-44975", - "more_info_path": "/vulnerabilities/CVE-2020-15168/44975", + "advisory": "Cellxgene 0.12.0 updates several more NPM dependencies to fix security issues.\r\nhttps://github.com/chanzuckerberg/cellxgene/commit/78a43402cb0c1beca5269b3970d4cc31615e4664", + "cve": "PVE-2022-44977", + "id": "pyup.io-44977", + "more_info_path": "/vulnerabilities/PVE-2022-44977/44977", "specs": [ "<0.12.0" ], @@ -17147,9 +17301,9 @@ "certbot-dns-duckdns": [ { "advisory": "Certbot-dns-duckdns 1.3 updates its dependency 'cryptography' to latest version in the docker image, to include security fixes.", - "cve": "CVE-2023-0216", - "id": "pyup.io-53630", - "more_info_path": "/vulnerabilities/CVE-2023-0216/53630", + "cve": "CVE-2023-0401", + "id": "pyup.io-53624", + "more_info_path": "/vulnerabilities/CVE-2023-0401/53624", "specs": [ "<1.3" ], @@ -17157,9 +17311,9 @@ }, { "advisory": "Certbot-dns-duckdns 1.3 updates its dependency 'cryptography' to latest version in the docker image, to include security fixes.", - "cve": "CVE-2023-0401", - "id": "pyup.io-53624", - "more_info_path": "/vulnerabilities/CVE-2023-0401/53624", + "cve": "CVE-2023-0217", + "id": "pyup.io-53628", + "more_info_path": "/vulnerabilities/CVE-2023-0217/53628", "specs": [ "<1.3" ], @@ -17167,9 +17321,9 @@ }, { "advisory": "Certbot-dns-duckdns 1.3 updates its dependency 'cryptography' to latest version in the docker image, to include security fixes.", - "cve": "CVE-2023-0217", - "id": "pyup.io-53628", - "more_info_path": "/vulnerabilities/CVE-2023-0217/53628", + "cve": "CVE-2023-0216", + "id": "pyup.io-53630", + "more_info_path": "/vulnerabilities/CVE-2023-0216/53630", "specs": [ "<1.3" ], @@ -17179,9 +17333,9 @@ "certbot-dns-porkbun": [ { "advisory": "Certbot-dns-porkbun 0.8 updates 'cryptography' to v39.0.1 in Docker image to include security fixes.\r\nhttps://github.com/infinityofspace/certbot_dns_porkbun/commit/789959d75ef65b9e6e7fdf0651254bf18378b0a9", - "cve": "CVE-2023-0217", - "id": "pyup.io-53620", - "more_info_path": "/vulnerabilities/CVE-2023-0217/53620", + "cve": "CVE-2023-0401", + "id": "pyup.io-53618", + "more_info_path": "/vulnerabilities/CVE-2023-0401/53618", "specs": [ "<0.8" ], @@ -17190,8 +17344,8 @@ { "advisory": "Certbot-dns-porkbun 0.8 updates 'cryptography' to v39.0.1 in Docker image to include security fixes.\r\nhttps://github.com/infinityofspace/certbot_dns_porkbun/commit/789959d75ef65b9e6e7fdf0651254bf18378b0a9", "cve": "CVE-2023-0217", - "id": "pyup.io-53619", - "more_info_path": "/vulnerabilities/CVE-2023-0217/53619", + "id": "pyup.io-53620", + "more_info_path": "/vulnerabilities/CVE-2023-0217/53620", "specs": [ "<0.8" ], @@ -17209,9 +17363,9 @@ }, { "advisory": "Certbot-dns-porkbun 0.8 updates 'cryptography' to v39.0.1 in Docker image to include security fixes.\r\nhttps://github.com/infinityofspace/certbot_dns_porkbun/commit/789959d75ef65b9e6e7fdf0651254bf18378b0a9", - "cve": "CVE-2023-0401", - "id": "pyup.io-53618", - "more_info_path": "/vulnerabilities/CVE-2023-0401/53618", + "cve": "CVE-2023-0217", + "id": "pyup.io-53619", + "more_info_path": "/vulnerabilities/CVE-2023-0217/53619", "specs": [ "<0.8" ], @@ -17610,6 +17764,26 @@ ], "v": "<0.45.6" }, + { + "advisory": "Affected versions of changedetectionio are vulnerable to Path Traversal (CWE-22). This allows attackers to retrieve local system files by using crafted URLs like source:file:///etc/passwd. The vulnerability arises from improper URL validation in WebDriver\u2019s file fetching functions, enabling unauthorized file access.", + "cve": "CVE-2024-51483", + "id": "pyup.io-74073", + "more_info_path": "/vulnerabilities/CVE-2024-51483/74073", + "specs": [ + "<0.47.05" + ], + "v": "<0.47.05" + }, + { + "advisory": "Affected versions of changedetectionio are vulnerable to Path Traversal (CWE-22). The issue arises from inadequate URL validation in the URL processing functions, where file:/ URLs were not properly blocked. To exploit, an attacker can craft malicious file:/ URLs targeting the application to access sensitive local files. Mitigation involves updating to the version where the regex correctly blocks both file:// and file:/ schemes.\r\n#Note: This issue only affects instances with a webdriver enabled, and ALLOW_FILE_URI false or not defined.", + "cve": "CVE-2024-51998", + "id": "pyup.io-74072", + "more_info_path": "/vulnerabilities/CVE-2024-51998/74072", + "specs": [ + "<0.47.06" + ], + "v": "<0.47.06" + }, { "advisory": "Changedetection.io is vulnerable to an Incorrect Authorization vulnerability. API endpoint /api/v1/watch//history can be accessed by any unauthorized user.", "cve": "CVE-2024-23329", @@ -17700,9 +17874,9 @@ }, { "advisory": "Chaostoolkit 1.14.0 updates container image to include security fixes.", - "cve": "CVE-2022-1304", - "id": "pyup.io-54845", - "more_info_path": "/vulnerabilities/CVE-2022-1304/54845", + "cve": "CVE-2021-33560", + "id": "pyup.io-54859", + "more_info_path": "/vulnerabilities/CVE-2021-33560/54859", "specs": [ "<1.14.0" ], @@ -17711,8 +17885,8 @@ { "advisory": "Chaostoolkit 1.14.0 updates container image to include security fixes.", "cve": "CVE-2022-1304", - "id": "pyup.io-54863", - "more_info_path": "/vulnerabilities/CVE-2022-1304/54863", + "id": "pyup.io-54845", + "more_info_path": "/vulnerabilities/CVE-2022-1304/54845", "specs": [ "<1.14.0" ], @@ -17730,9 +17904,9 @@ }, { "advisory": "Chaostoolkit 1.14.0 updates container image to include security fixes.", - "cve": "CVE-2021-33560", - "id": "pyup.io-54859", - "more_info_path": "/vulnerabilities/CVE-2021-33560/54859", + "cve": "CVE-2022-1304", + "id": "pyup.io-54863", + "more_info_path": "/vulnerabilities/CVE-2022-1304/54863", "specs": [ "<1.14.0" ], @@ -17796,6 +17970,16 @@ ], "v": "<3.0.3" }, + { + "advisory": "Chartify 3.0.3 includes a version of 'pillow' (6.2.0) affected by several CVEs.", + "cve": "CVE-2020-5312", + "id": "pyup.io-43570", + "more_info_path": "/vulnerabilities/CVE-2020-5312/43570", + "specs": [ + "<=3.0.3" + ], + "v": "<=3.0.3" + }, { "advisory": "Chartify 3.0.4 updates its dependency 'pillow' requirement to '>=8.4.0' to include security fixes.", "cve": "CVE-2019-19911", @@ -17807,10 +17991,10 @@ "v": "<=3.0.3" }, { - "advisory": "Chartify 3.0.3 includes a version of 'pillow' (6.2.0) affected by several CVEs.", - "cve": "CVE-2020-5311", - "id": "pyup.io-43569", - "more_info_path": "/vulnerabilities/CVE-2020-5311/43569", + "advisory": "libImaging/FliDecode.c in Pillow before 6.2.2 has an FLI buffer overflow.", + "cve": "CVE-2020-5313", + "id": "pyup.io-43571", + "more_info_path": "/vulnerabilities/CVE-2020-5313/43571", "specs": [ "<=3.0.3" ], @@ -17818,9 +18002,9 @@ }, { "advisory": "Chartify 3.0.3 includes a version of 'pillow' (6.2.0) affected by several CVEs.", - "cve": "CVE-2020-5312", - "id": "pyup.io-43570", - "more_info_path": "/vulnerabilities/CVE-2020-5312/43570", + "cve": "CVE-2020-5311", + "id": "pyup.io-43569", + "more_info_path": "/vulnerabilities/CVE-2020-5311/43569", "specs": [ "<=3.0.3" ], @@ -17835,34 +18019,24 @@ "<=3.0.3" ], "v": "<=3.0.3" - }, - { - "advisory": "libImaging/FliDecode.c in Pillow before 6.2.2 has an FLI buffer overflow.", - "cve": "CVE-2020-5313", - "id": "pyup.io-43571", - "more_info_path": "/vulnerabilities/CVE-2020-5313/43571", - "specs": [ - "<=3.0.3" - ], - "v": "<=3.0.3" } ], "chartmogul": [ { - "advisory": "Chartmogul 4.3.1 updates its urllib3 dependency from <=2.0.4 to 1.26.19 to address security concerns, including several vulnerabilities such as CVE-2023-43804.", - "cve": "CVE-2023-43804", - "id": "pyup.io-71724", - "more_info_path": "/vulnerabilities/CVE-2023-43804/71724", + "advisory": "Chartmogul 4.3.1 updates its urllib3 dependency from <=2.0.4 to 1.26.19 to address security concerns, including several vulnerabilities such as CVE-2023-45803.", + "cve": "CVE-2023-45803", + "id": "pyup.io-71715", + "more_info_path": "/vulnerabilities/CVE-2023-45803/71715", "specs": [ "<4.3.1" ], "v": "<4.3.1" }, { - "advisory": "Chartmogul 4.3.1 updates its urllib3 dependency from <=2.0.4 to 1.26.19 to address security concerns, including several vulnerabilities such as CVE-2023-45803.", - "cve": "CVE-2023-45803", - "id": "pyup.io-71715", - "more_info_path": "/vulnerabilities/CVE-2023-45803/71715", + "advisory": "Chartmogul 4.3.1 updates its urllib3 dependency from <=2.0.4 to 1.26.19 to address security concerns, including several vulnerabilities such as CVE-2023-43804.", + "cve": "CVE-2023-43804", + "id": "pyup.io-71724", + "more_info_path": "/vulnerabilities/CVE-2023-43804/71724", "specs": [ "<4.3.1" ], @@ -17981,6 +18155,18 @@ "v": "<0.6.0" } ], + "chattts": [ + { + "advisory": "Affected versions of ChatTTS are vulnerable to Deserialization of Untrusted Data (CWE-502). This vulnerability allows arbitrary code execution due to unsafe deserialization when loading the tokenizer with torch.load. Attackers can exploit this by supplying a malicious tokenizer.pt file, leading to code execution during deserialization in the Tokenizer class's __init__ method. Exploitation requires the attacker to replace or tamper with the tokenizer.pt file. Mitigation involves updating ChatTTS to the version which replaces torch.load with the safer BertTokenizerFast.from_pretrained method. This issue is specific to Python applications using PyTorch's torch.load on untrusted data.", + "cve": "PVE-2024-74037", + "id": "pyup.io-74037", + "more_info_path": "/vulnerabilities/PVE-2024-74037/74037", + "specs": [ + "<0.2.0" + ], + "v": "<0.2.0" + } + ], "chaturbate-poller": [ { "advisory": "Affected versions of Chaturbate-poller are vulnerable to Sensitive Information Exposure.", @@ -18245,10 +18431,10 @@ "v": ">=2.1.0p0,<=2.1.0p10,<=2.0.0p28" }, { - "advisory": "Command injection in SMS notifications in Tribe29 Checkmk <= 2.1.0p10, Checkmk <= 2.0.0p27, and Checkmk <= 1.6.0p29 allows an attacker with User Management permissions, as well as LDAP administrators in certain scenarios, to perform arbitrary commands within the context of the application's local permissions.\r\nNote: Checkmk on PyPI is a placeholder. You may download the real package from its official website (https://checkmk.com/download).", - "cve": "CVE-2022-46303", - "id": "pyup.io-63129", - "more_info_path": "/vulnerabilities/CVE-2022-46303/63129", + "advisory": "PHP code injection in watolib auth.php and hosttags.php in Tribe29's Checkmk <= 2.1.0p10, Checkmk <= 2.0.0p27, and Checkmk <= 1.6.0p29 allows an attacker to inject and execute PHP code which will be executed upon request of the vulnerable component.\r\nNote: Checkmk on PyPI is a placeholder. You may download the real package from its official website (https://checkmk.com/download).", + "cve": "CVE-2022-46836", + "id": "pyup.io-63130", + "more_info_path": "/vulnerabilities/CVE-2022-46836/63130", "specs": [ ">=2.1.0p0,<=2.1.0p10", ">=2.0.0p0,<=2.0.0p27", @@ -18257,10 +18443,10 @@ "v": ">=2.1.0p0,<=2.1.0p10,>=2.0.0p0,<=2.0.0p27,<=1.6.0p29" }, { - "advisory": "PHP code injection in watolib auth.php and hosttags.php in Tribe29's Checkmk <= 2.1.0p10, Checkmk <= 2.0.0p27, and Checkmk <= 1.6.0p29 allows an attacker to inject and execute PHP code which will be executed upon request of the vulnerable component.\r\nNote: Checkmk on PyPI is a placeholder. You may download the real package from its official website (https://checkmk.com/download).", - "cve": "CVE-2022-46836", - "id": "pyup.io-63130", - "more_info_path": "/vulnerabilities/CVE-2022-46836/63130", + "advisory": "Command injection in SMS notifications in Tribe29 Checkmk <= 2.1.0p10, Checkmk <= 2.0.0p27, and Checkmk <= 1.6.0p29 allows an attacker with User Management permissions, as well as LDAP administrators in certain scenarios, to perform arbitrary commands within the context of the application's local permissions.\r\nNote: Checkmk on PyPI is a placeholder. You may download the real package from its official website (https://checkmk.com/download).", + "cve": "CVE-2022-46303", + "id": "pyup.io-63129", + "more_info_path": "/vulnerabilities/CVE-2022-46303/63129", "specs": [ ">=2.1.0p0,<=2.1.0p10", ">=2.0.0p0,<=2.0.0p27", @@ -18338,10 +18524,10 @@ "v": ">=2.2.0b0,<2.2.0p15,>=2.1.0b0,<2.1.0p37,<=2.0.0p39" }, { - "advisory": "Improper Input Validation in Checkmk <2.2.0p15, <2.1.0p37, <=2.0.0p39 allows privileged attackers to cause partial denial of service in the UI via long hostnames.\r\nNote: Checkmk on PyPI is a placeholder. You may download the real package from its official website (https://checkmk.com/download).", - "cve": "CVE-2023-23549", - "id": "pyup.io-63146", - "more_info_path": "/vulnerabilities/CVE-2023-23549/63146", + "advisory": "Improper neutralization of livestatus command delimiters in the availability timeline in Checkmk <= 2.0.0p39, < 2.1.0p37, and < 2.2.0p15 allows arbitrary livestatus command execution for authorized users.\r\nNote: Checkmk on PyPI is a placeholder. You may download the real package from its official website (https://checkmk.com/download).", + "cve": "CVE-2023-6156", + "id": "pyup.io-63149", + "more_info_path": "/vulnerabilities/CVE-2023-6156/63149", "specs": [ ">=2.2.0b0,<2.2.0p15", ">=2.1.0b0,<2.1.0p37", @@ -18350,10 +18536,10 @@ "v": ">=2.2.0b0,<2.2.0p15,>=2.1.0b0,<2.1.0p37,<=2.0.0p39" }, { - "advisory": "Improper neutralization of livestatus command delimiters in the availability timeline in Checkmk <= 2.0.0p39, < 2.1.0p37, and < 2.2.0p15 allows arbitrary livestatus command execution for authorized users.\r\nNote: Checkmk on PyPI is a placeholder. You may download the real package from its official website (https://checkmk.com/download).", - "cve": "CVE-2023-6156", - "id": "pyup.io-63149", - "more_info_path": "/vulnerabilities/CVE-2023-6156/63149", + "advisory": "Improper Input Validation in Checkmk <2.2.0p15, <2.1.0p37, <=2.0.0p39 allows privileged attackers to cause partial denial of service in the UI via long hostnames.\r\nNote: Checkmk on PyPI is a placeholder. You may download the real package from its official website (https://checkmk.com/download).", + "cve": "CVE-2023-23549", + "id": "pyup.io-63146", + "more_info_path": "/vulnerabilities/CVE-2023-23549/63146", "specs": [ ">=2.2.0b0,<2.2.0p15", ">=2.1.0b0,<2.1.0p37", @@ -18558,9 +18744,9 @@ "chia": [ { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29590", - "id": "pyup.io-44297", - "more_info_path": "/vulnerabilities/CVE-2021-29590/44297", + "cve": "CVE-2021-29555", + "id": "pyup.io-44260", + "more_info_path": "/vulnerabilities/CVE-2021-29555/44260", "specs": [ "<2.4.0" ], @@ -18568,9 +18754,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37652", - "id": "pyup.io-44344", - "more_info_path": "/vulnerabilities/CVE-2021-37652/44344", + "cve": "CVE-2021-29612", + "id": "pyup.io-44319", + "more_info_path": "/vulnerabilities/CVE-2021-29612/44319", "specs": [ "<2.4.0" ], @@ -18578,9 +18764,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37690", - "id": "pyup.io-44382", - "more_info_path": "/vulnerabilities/CVE-2021-37690/44382", + "cve": "CVE-2021-29553", + "id": "pyup.io-44258", + "more_info_path": "/vulnerabilities/CVE-2021-29553/44258", "specs": [ "<2.4.0" ], @@ -18588,9 +18774,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37642", - "id": "pyup.io-44334", - "more_info_path": "/vulnerabilities/CVE-2021-37642/44334", + "cve": "CVE-2021-29561", + "id": "pyup.io-44266", + "more_info_path": "/vulnerabilities/CVE-2021-29561/44266", "specs": [ "<2.4.0" ], @@ -18598,9 +18784,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37678", - "id": "pyup.io-44370", - "more_info_path": "/vulnerabilities/CVE-2021-37678/44370", + "cve": "CVE-2020-14155", + "id": "pyup.io-44175", + "more_info_path": "/vulnerabilities/CVE-2020-14155/44175", "specs": [ "<2.4.0" ], @@ -18608,9 +18794,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29612", - "id": "pyup.io-44319", - "more_info_path": "/vulnerabilities/CVE-2021-29612/44319", + "cve": "CVE-2021-29516", + "id": "pyup.io-44221", + "more_info_path": "/vulnerabilities/CVE-2021-29516/44221", "specs": [ "<2.4.0" ], @@ -18618,9 +18804,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37681", - "id": "pyup.io-44373", - "more_info_path": "/vulnerabilities/CVE-2021-37681/44373", + "cve": "CVE-2020-15206", + "id": "pyup.io-44192", + "more_info_path": "/vulnerabilities/CVE-2020-15206/44192", "specs": [ "<2.4.0" ], @@ -18628,9 +18814,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37687", - "id": "pyup.io-44379", - "more_info_path": "/vulnerabilities/CVE-2021-37687/44379", + "cve": "CVE-2021-29526", + "id": "pyup.io-44231", + "more_info_path": "/vulnerabilities/CVE-2021-29526/44231", "specs": [ "<2.4.0" ], @@ -18638,9 +18824,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29529", - "id": "pyup.io-44234", - "more_info_path": "/vulnerabilities/CVE-2021-29529/44234", + "cve": "CVE-2021-29577", + "id": "pyup.io-44284", + "more_info_path": "/vulnerabilities/CVE-2021-29577/44284", "specs": [ "<2.4.0" ], @@ -18648,9 +18834,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29554", - "id": "pyup.io-44259", - "more_info_path": "/vulnerabilities/CVE-2021-29554/44259", + "cve": "CVE-2021-29601", + "id": "pyup.io-44308", + "more_info_path": "/vulnerabilities/CVE-2021-29601/44308", "specs": [ "<2.4.0" ], @@ -18658,9 +18844,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29569", - "id": "pyup.io-44275", - "more_info_path": "/vulnerabilities/CVE-2021-29569/44275", + "cve": "CVE-2021-29524", + "id": "pyup.io-44229", + "more_info_path": "/vulnerabilities/CVE-2021-29524/44229", "specs": [ "<2.4.0" ], @@ -18668,9 +18854,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29573", - "id": "pyup.io-44280", - "more_info_path": "/vulnerabilities/CVE-2021-29573/44280", + "cve": "CVE-2021-29559", + "id": "pyup.io-44264", + "more_info_path": "/vulnerabilities/CVE-2021-29559/44264", "specs": [ "<2.4.0" ], @@ -18678,9 +18864,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37670", - "id": "pyup.io-44362", - "more_info_path": "/vulnerabilities/CVE-2021-37670/44362", + "cve": "CVE-2021-37656", + "id": "pyup.io-44348", + "more_info_path": "/vulnerabilities/CVE-2021-37656/44348", "specs": [ "<2.4.0" ], @@ -18688,9 +18874,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29609", - "id": "pyup.io-44316", - "more_info_path": "/vulnerabilities/CVE-2021-29609/44316", + "cve": "CVE-2021-29544", + "id": "pyup.io-44249", + "more_info_path": "/vulnerabilities/CVE-2021-29544/44249", "specs": [ "<2.4.0" ], @@ -18698,9 +18884,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37637", - "id": "pyup.io-44329", - "more_info_path": "/vulnerabilities/CVE-2021-37637/44329", + "cve": "CVE-2021-29582", + "id": "pyup.io-44289", + "more_info_path": "/vulnerabilities/CVE-2021-29582/44289", "specs": [ "<2.4.0" ], @@ -18708,9 +18894,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29608", - "id": "pyup.io-44315", - "more_info_path": "/vulnerabilities/CVE-2021-29608/44315", + "cve": "CVE-2020-15196", + "id": "pyup.io-44182", + "more_info_path": "/vulnerabilities/CVE-2020-15196/44182", "specs": [ "<2.4.0" ], @@ -18718,9 +18904,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37641", - "id": "pyup.io-44333", - "more_info_path": "/vulnerabilities/CVE-2021-37641/44333", + "cve": "CVE-2021-29573", + "id": "pyup.io-44280", + "more_info_path": "/vulnerabilities/CVE-2021-29573/44280", "specs": [ "<2.4.0" ], @@ -18728,9 +18914,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37662", - "id": "pyup.io-44354", - "more_info_path": "/vulnerabilities/CVE-2021-37662/44354", + "cve": "CVE-2021-29572", + "id": "pyup.io-44279", + "more_info_path": "/vulnerabilities/CVE-2021-29572/44279", "specs": [ "<2.4.0" ], @@ -18738,9 +18924,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37665", - "id": "pyup.io-44357", - "more_info_path": "/vulnerabilities/CVE-2021-37665/44357", + "cve": "CVE-2020-15203", + "id": "pyup.io-44189", + "more_info_path": "/vulnerabilities/CVE-2020-15203/44189", "specs": [ "<2.4.0" ], @@ -18748,9 +18934,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37643", - "id": "pyup.io-44335", - "more_info_path": "/vulnerabilities/CVE-2021-37643/44335", + "cve": "CVE-2021-29570", + "id": "pyup.io-44274", + "more_info_path": "/vulnerabilities/CVE-2021-29570/44274", "specs": [ "<2.4.0" ], @@ -18758,9 +18944,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37645", - "id": "pyup.io-44337", - "more_info_path": "/vulnerabilities/CVE-2021-37645/44337", + "cve": "CVE-2021-29570", + "id": "pyup.io-44276", + "more_info_path": "/vulnerabilities/CVE-2021-29570/44276", "specs": [ "<2.4.0" ], @@ -18768,9 +18954,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37654", - "id": "pyup.io-44346", - "more_info_path": "/vulnerabilities/CVE-2021-37654/44346", + "cve": "CVE-2021-29586", + "id": "pyup.io-44293", + "more_info_path": "/vulnerabilities/CVE-2021-29586/44293", "specs": [ "<2.4.0" ], @@ -18778,9 +18964,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37655", - "id": "pyup.io-44347", - "more_info_path": "/vulnerabilities/CVE-2021-37655/44347", + "cve": "CVE-2021-29569", + "id": "pyup.io-44275", + "more_info_path": "/vulnerabilities/CVE-2021-29569/44275", "specs": [ "<2.4.0" ], @@ -18788,9 +18974,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29613", - "id": "pyup.io-44320", - "more_info_path": "/vulnerabilities/CVE-2021-29613/44320", + "cve": "CVE-2021-29611", + "id": "pyup.io-44318", + "more_info_path": "/vulnerabilities/CVE-2021-29611/44318", "specs": [ "<2.4.0" ], @@ -18798,9 +18984,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29550", - "id": "pyup.io-44255", - "more_info_path": "/vulnerabilities/CVE-2021-29550/44255", + "cve": "CVE-2021-29614", + "id": "pyup.io-44321", + "more_info_path": "/vulnerabilities/CVE-2021-29614/44321", "specs": [ "<2.4.0" ], @@ -18808,9 +18994,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15202", - "id": "pyup.io-44188", - "more_info_path": "/vulnerabilities/CVE-2020-15202/44188", + "cve": "CVE-2021-29613", + "id": "pyup.io-44320", + "more_info_path": "/vulnerabilities/CVE-2021-29613/44320", "specs": [ "<2.4.0" ], @@ -18818,9 +19004,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37673", - "id": "pyup.io-44365", - "more_info_path": "/vulnerabilities/CVE-2021-37673/44365", + "cve": "CVE-2021-29592", + "id": "pyup.io-44299", + "more_info_path": "/vulnerabilities/CVE-2021-29592/44299", "specs": [ "<2.4.0" ], @@ -18828,9 +19014,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37689", - "id": "pyup.io-44381", - "more_info_path": "/vulnerabilities/CVE-2021-37689/44381", + "cve": "CVE-2021-29560", + "id": "pyup.io-44265", + "more_info_path": "/vulnerabilities/CVE-2021-29560/44265", "specs": [ "<2.4.0" ], @@ -18838,9 +19024,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29562", - "id": "pyup.io-44267", - "more_info_path": "/vulnerabilities/CVE-2021-29562/44267", + "cve": "CVE-2021-29556", + "id": "pyup.io-44261", + "more_info_path": "/vulnerabilities/CVE-2021-29556/44261", "specs": [ "<2.4.0" ], @@ -18848,9 +19034,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29563", - "id": "pyup.io-44268", - "more_info_path": "/vulnerabilities/CVE-2021-29563/44268", + "cve": "CVE-2021-37664", + "id": "pyup.io-44356", + "more_info_path": "/vulnerabilities/CVE-2021-37664/44356", "specs": [ "<2.4.0" ], @@ -18858,9 +19044,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29555", - "id": "pyup.io-44260", - "more_info_path": "/vulnerabilities/CVE-2021-29555/44260", + "cve": "CVE-2021-29609", + "id": "pyup.io-44316", + "more_info_path": "/vulnerabilities/CVE-2021-29609/44316", "specs": [ "<2.4.0" ], @@ -18868,9 +19054,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29564", - "id": "pyup.io-44269", - "more_info_path": "/vulnerabilities/CVE-2021-29564/44269", + "cve": "CVE-2021-29588", + "id": "pyup.io-44295", + "more_info_path": "/vulnerabilities/CVE-2021-29588/44295", "specs": [ "<2.4.0" ], @@ -18878,9 +19064,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29565", - "id": "pyup.io-44270", - "more_info_path": "/vulnerabilities/CVE-2021-29565/44270", + "cve": "CVE-2021-29606", + "id": "pyup.io-44313", + "more_info_path": "/vulnerabilities/CVE-2021-29606/44313", "specs": [ "<2.4.0" ], @@ -18888,9 +19074,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29569", - "id": "pyup.io-44273", - "more_info_path": "/vulnerabilities/CVE-2021-29569/44273", + "cve": "CVE-2020-15191", + "id": "pyup.io-44177", + "more_info_path": "/vulnerabilities/CVE-2020-15191/44177", "specs": [ "<2.4.0" ], @@ -18898,9 +19084,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15211", - "id": "pyup.io-44197", - "more_info_path": "/vulnerabilities/CVE-2020-15211/44197", + "cve": "CVE-2021-37667", + "id": "pyup.io-44359", + "more_info_path": "/vulnerabilities/CVE-2021-37667/44359", "specs": [ "<2.4.0" ], @@ -18908,9 +19094,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15212", - "id": "pyup.io-44198", - "more_info_path": "/vulnerabilities/CVE-2020-15212/44198", + "cve": "CVE-2021-29608", + "id": "pyup.io-44315", + "more_info_path": "/vulnerabilities/CVE-2021-29608/44315", "specs": [ "<2.4.0" ], @@ -18918,9 +19104,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29578", - "id": "pyup.io-44285", - "more_info_path": "/vulnerabilities/CVE-2021-29578/44285", + "cve": "CVE-2021-37684", + "id": "pyup.io-44376", + "more_info_path": "/vulnerabilities/CVE-2021-37684/44376", "specs": [ "<2.4.0" ], @@ -18928,9 +19114,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29583", - "id": "pyup.io-44290", - "more_info_path": "/vulnerabilities/CVE-2021-29583/44290", + "cve": "CVE-2021-37638", + "id": "pyup.io-44330", + "more_info_path": "/vulnerabilities/CVE-2021-37638/44330", "specs": [ "<2.4.0" ], @@ -18938,9 +19124,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29587", - "id": "pyup.io-44294", - "more_info_path": "/vulnerabilities/CVE-2021-29587/44294", + "cve": "CVE-2021-22901", + "id": "pyup.io-44217", + "more_info_path": "/vulnerabilities/CVE-2021-22901/44217", "specs": [ "<2.4.0" ], @@ -18948,9 +19134,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29540", - "id": "pyup.io-44245", - "more_info_path": "/vulnerabilities/CVE-2021-29540/44245", + "cve": "CVE-2021-22898", + "id": "pyup.io-44216", + "more_info_path": "/vulnerabilities/CVE-2021-22898/44216", "specs": [ "<2.4.0" ], @@ -18958,9 +19144,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29589", - "id": "pyup.io-44296", - "more_info_path": "/vulnerabilities/CVE-2021-29589/44296", + "cve": "CVE-2021-29563", + "id": "pyup.io-44268", + "more_info_path": "/vulnerabilities/CVE-2021-29563/44268", "specs": [ "<2.4.0" ], @@ -18968,9 +19154,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29557", - "id": "pyup.io-44262", - "more_info_path": "/vulnerabilities/CVE-2021-29557/44262", + "cve": "CVE-2021-29562", + "id": "pyup.io-44267", + "more_info_path": "/vulnerabilities/CVE-2021-29562/44267", "specs": [ "<2.4.0" ], @@ -18978,9 +19164,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29558", - "id": "pyup.io-44263", - "more_info_path": "/vulnerabilities/CVE-2021-29558/44263", + "cve": "CVE-2021-29574", + "id": "pyup.io-44281", + "more_info_path": "/vulnerabilities/CVE-2021-29574/44281", "specs": [ "<2.4.0" ], @@ -18988,9 +19174,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29561", - "id": "pyup.io-44266", - "more_info_path": "/vulnerabilities/CVE-2021-29561/44266", + "cve": "CVE-2021-29547", + "id": "pyup.io-44252", + "more_info_path": "/vulnerabilities/CVE-2021-29547/44252", "specs": [ "<2.4.0" ], @@ -18998,9 +19184,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29605", - "id": "pyup.io-44312", - "more_info_path": "/vulnerabilities/CVE-2021-29605/44312", + "cve": "CVE-2020-26271", + "id": "pyup.io-44208", + "more_info_path": "/vulnerabilities/CVE-2020-26271/44208", "specs": [ "<2.4.0" ], @@ -19008,9 +19194,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29615", - "id": "pyup.io-44322", - "more_info_path": "/vulnerabilities/CVE-2021-29615/44322", + "cve": "CVE-2021-29523", + "id": "pyup.io-44228", + "more_info_path": "/vulnerabilities/CVE-2021-29523/44228", "specs": [ "<2.4.0" ], @@ -19018,9 +19204,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29619", - "id": "pyup.io-44326", - "more_info_path": "/vulnerabilities/CVE-2021-29619/44326", + "cve": "CVE-2020-15200", + "id": "pyup.io-44186", + "more_info_path": "/vulnerabilities/CVE-2020-15200/44186", "specs": [ "<2.4.0" ], @@ -19028,9 +19214,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37656", - "id": "pyup.io-44348", - "more_info_path": "/vulnerabilities/CVE-2021-37656/44348", + "cve": "CVE-2020-26266", + "id": "pyup.io-44204", + "more_info_path": "/vulnerabilities/CVE-2020-26266/44204", "specs": [ "<2.4.0" ], @@ -19038,9 +19224,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37660", - "id": "pyup.io-44352", - "more_info_path": "/vulnerabilities/CVE-2021-37660/44352", + "cve": "CVE-2021-29527", + "id": "pyup.io-44232", + "more_info_path": "/vulnerabilities/CVE-2021-29527/44232", "specs": [ "<2.4.0" ], @@ -19048,9 +19234,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29570", - "id": "pyup.io-44274", - "more_info_path": "/vulnerabilities/CVE-2021-29570/44274", + "cve": "CVE-2021-29619", + "id": "pyup.io-44326", + "more_info_path": "/vulnerabilities/CVE-2021-29619/44326", "specs": [ "<2.4.0" ], @@ -19058,9 +19244,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29581", - "id": "pyup.io-44288", - "more_info_path": "/vulnerabilities/CVE-2021-29581/44288", + "cve": "CVE-2021-29603", + "id": "pyup.io-44310", + "more_info_path": "/vulnerabilities/CVE-2021-29603/44310", "specs": [ "<2.4.0" ], @@ -19068,9 +19254,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29586", - "id": "pyup.io-44293", - "more_info_path": "/vulnerabilities/CVE-2021-29586/44293", + "cve": "CVE-2021-29618", + "id": "pyup.io-44325", + "more_info_path": "/vulnerabilities/CVE-2021-29618/44325", "specs": [ "<2.4.0" ], @@ -19078,9 +19264,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29598", - "id": "pyup.io-44305", - "more_info_path": "/vulnerabilities/CVE-2021-29598/44305", + "cve": "CVE-2021-29602", + "id": "pyup.io-44309", + "more_info_path": "/vulnerabilities/CVE-2021-29602/44309", "specs": [ "<2.4.0" ], @@ -19088,9 +19274,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29600", - "id": "pyup.io-44307", - "more_info_path": "/vulnerabilities/CVE-2021-29600/44307", + "cve": "CVE-2021-29604", + "id": "pyup.io-44311", + "more_info_path": "/vulnerabilities/CVE-2021-29604/44311", "specs": [ "<2.4.0" ], @@ -19098,9 +19284,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29604", - "id": "pyup.io-44311", - "more_info_path": "/vulnerabilities/CVE-2021-29604/44311", + "cve": "CVE-2021-37689", + "id": "pyup.io-44381", + "more_info_path": "/vulnerabilities/CVE-2021-37689/44381", "specs": [ "<2.4.0" ], @@ -19108,9 +19294,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29579", - "id": "pyup.io-44286", - "more_info_path": "/vulnerabilities/CVE-2021-29579/44286", + "cve": "CVE-2021-29617", + "id": "pyup.io-44324", + "more_info_path": "/vulnerabilities/CVE-2021-29617/44324", "specs": [ "<2.4.0" ], @@ -19118,9 +19304,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29582", - "id": "pyup.io-44289", - "more_info_path": "/vulnerabilities/CVE-2021-29582/44289", + "cve": "CVE-2020-8177", + "id": "pyup.io-44210", + "more_info_path": "/vulnerabilities/CVE-2020-8177/44210", "specs": [ "<2.4.0" ], @@ -19128,9 +19314,19 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29536", - "id": "pyup.io-44241", - "more_info_path": "/vulnerabilities/CVE-2021-29536/44241", + "cve": "CVE-2021-29590", + "id": "pyup.io-44297", + "more_info_path": "/vulnerabilities/CVE-2021-29590/44297", + "specs": [ + "<2.4.0" + ], + "v": "<2.4.0" + }, + { + "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", + "cve": "CVE-2021-29600", + "id": "pyup.io-44307", + "more_info_path": "/vulnerabilities/CVE-2021-29600/44307", "specs": [ "<2.4.0" ], @@ -19148,9 +19344,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29585", - "id": "pyup.io-44292", - "more_info_path": "/vulnerabilities/CVE-2021-29585/44292", + "cve": "CVE-2021-29594", + "id": "pyup.io-44301", + "more_info_path": "/vulnerabilities/CVE-2021-29594/44301", "specs": [ "<2.4.0" ], @@ -19158,9 +19354,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29617", - "id": "pyup.io-44324", - "more_info_path": "/vulnerabilities/CVE-2021-29617/44324", + "cve": "CVE-2020-15210", + "id": "pyup.io-44196", + "more_info_path": "/vulnerabilities/CVE-2020-15210/44196", "specs": [ "<2.4.0" ], @@ -19168,9 +19364,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37635", - "id": "pyup.io-44327", - "more_info_path": "/vulnerabilities/CVE-2021-37635/44327", + "cve": "CVE-2021-29615", + "id": "pyup.io-44322", + "more_info_path": "/vulnerabilities/CVE-2021-29615/44322", "specs": [ "<2.4.0" ], @@ -19178,9 +19374,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29592", - "id": "pyup.io-44299", - "more_info_path": "/vulnerabilities/CVE-2021-29592/44299", + "cve": "CVE-2021-29540", + "id": "pyup.io-44245", + "more_info_path": "/vulnerabilities/CVE-2021-29540/44245", "specs": [ "<2.4.0" ], @@ -19188,9 +19384,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29594", - "id": "pyup.io-44301", - "more_info_path": "/vulnerabilities/CVE-2021-29594/44301", + "cve": "CVE-2021-29610", + "id": "pyup.io-44317", + "more_info_path": "/vulnerabilities/CVE-2021-29610/44317", "specs": [ "<2.4.0" ], @@ -19208,9 +19404,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29551", - "id": "pyup.io-44256", - "more_info_path": "/vulnerabilities/CVE-2021-29551/44256", + "cve": "CVE-2021-29576", + "id": "pyup.io-44283", + "more_info_path": "/vulnerabilities/CVE-2021-29576/44283", "specs": [ "<2.4.0" ], @@ -19218,9 +19414,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29553", - "id": "pyup.io-44258", - "more_info_path": "/vulnerabilities/CVE-2021-29553/44258", + "cve": "CVE-2021-37666", + "id": "pyup.io-44358", + "more_info_path": "/vulnerabilities/CVE-2021-37666/44358", "specs": [ "<2.4.0" ], @@ -19228,9 +19424,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29556", - "id": "pyup.io-44261", - "more_info_path": "/vulnerabilities/CVE-2021-29556/44261", + "cve": "CVE-2021-29591", + "id": "pyup.io-44298", + "more_info_path": "/vulnerabilities/CVE-2021-29591/44298", "specs": [ "<2.4.0" ], @@ -19238,9 +19434,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29559", - "id": "pyup.io-44264", - "more_info_path": "/vulnerabilities/CVE-2021-29559/44264", + "cve": "CVE-2021-29533", + "id": "pyup.io-44238", + "more_info_path": "/vulnerabilities/CVE-2021-29533/44238", "specs": [ "<2.4.0" ], @@ -19248,9 +19444,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29560", - "id": "pyup.io-44265", - "more_info_path": "/vulnerabilities/CVE-2021-29560/44265", + "cve": "CVE-2021-29539", + "id": "pyup.io-44244", + "more_info_path": "/vulnerabilities/CVE-2021-29539/44244", "specs": [ "<2.4.0" ], @@ -19258,9 +19454,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29571", - "id": "pyup.io-44278", - "more_info_path": "/vulnerabilities/CVE-2021-29571/44278", + "cve": "CVE-2021-29607", + "id": "pyup.io-44314", + "more_info_path": "/vulnerabilities/CVE-2021-29607/44314", "specs": [ "<2.4.0" ], @@ -19268,9 +19464,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29572", - "id": "pyup.io-44279", - "more_info_path": "/vulnerabilities/CVE-2021-29572/44279", + "cve": "CVE-2021-29566", + "id": "pyup.io-44271", + "more_info_path": "/vulnerabilities/CVE-2021-29566/44271", "specs": [ "<2.4.0" ], @@ -19278,9 +19474,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29577", - "id": "pyup.io-44284", - "more_info_path": "/vulnerabilities/CVE-2021-29577/44284", + "cve": "CVE-2021-29616", + "id": "pyup.io-44323", + "more_info_path": "/vulnerabilities/CVE-2021-29616/44323", "specs": [ "<2.4.0" ], @@ -19288,9 +19484,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29588", - "id": "pyup.io-44295", - "more_info_path": "/vulnerabilities/CVE-2021-29588/44295", + "cve": "CVE-2020-8286", + "id": "pyup.io-44213", + "more_info_path": "/vulnerabilities/CVE-2020-8286/44213", "specs": [ "<2.4.0" ], @@ -19298,9 +19494,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29576", - "id": "pyup.io-44283", - "more_info_path": "/vulnerabilities/CVE-2021-29576/44283", + "cve": "CVE-2021-37639", + "id": "pyup.io-44331", + "more_info_path": "/vulnerabilities/CVE-2021-37639/44331", "specs": [ "<2.4.0" ], @@ -19308,9 +19504,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29595", - "id": "pyup.io-44302", - "more_info_path": "/vulnerabilities/CVE-2021-29595/44302", + "cve": "CVE-2021-37650", + "id": "pyup.io-44342", + "more_info_path": "/vulnerabilities/CVE-2021-37650/44342", "specs": [ "<2.4.0" ], @@ -19318,9 +19514,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29599", - "id": "pyup.io-44306", - "more_info_path": "/vulnerabilities/CVE-2021-29599/44306", + "cve": "CVE-2021-37649", + "id": "pyup.io-44341", + "more_info_path": "/vulnerabilities/CVE-2021-37649/44341", "specs": [ "<2.4.0" ], @@ -19328,9 +19524,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29601", - "id": "pyup.io-44308", - "more_info_path": "/vulnerabilities/CVE-2021-29601/44308", + "cve": "CVE-2021-37658", + "id": "pyup.io-44350", + "more_info_path": "/vulnerabilities/CVE-2021-37658/44350", "specs": [ "<2.4.0" ], @@ -19338,9 +19534,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29602", - "id": "pyup.io-44309", - "more_info_path": "/vulnerabilities/CVE-2021-29602/44309", + "cve": "CVE-2021-37653", + "id": "pyup.io-44345", + "more_info_path": "/vulnerabilities/CVE-2021-37653/44345", "specs": [ "<2.4.0" ], @@ -19348,9 +19544,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29606", - "id": "pyup.io-44313", - "more_info_path": "/vulnerabilities/CVE-2021-29606/44313", + "cve": "CVE-2021-37675", + "id": "pyup.io-44367", + "more_info_path": "/vulnerabilities/CVE-2021-37675/44367", "specs": [ "<2.4.0" ], @@ -19358,9 +19554,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29611", - "id": "pyup.io-44318", - "more_info_path": "/vulnerabilities/CVE-2021-29611/44318", + "cve": "CVE-2021-37679", + "id": "pyup.io-44371", + "more_info_path": "/vulnerabilities/CVE-2021-37679/44371", "specs": [ "<2.4.0" ], @@ -19368,9 +19564,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29618", - "id": "pyup.io-44325", - "more_info_path": "/vulnerabilities/CVE-2021-29618/44325", + "cve": "CVE-2021-37680", + "id": "pyup.io-44372", + "more_info_path": "/vulnerabilities/CVE-2021-37680/44372", "specs": [ "<2.4.0" ], @@ -19378,9 +19574,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37636", - "id": "pyup.io-44328", - "more_info_path": "/vulnerabilities/CVE-2021-37636/44328", + "cve": "CVE-2020-15195", + "id": "pyup.io-44181", + "more_info_path": "/vulnerabilities/CVE-2020-15195/44181", "specs": [ "<2.4.0" ], @@ -19388,9 +19584,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37640", - "id": "pyup.io-44332", - "more_info_path": "/vulnerabilities/CVE-2021-37640/44332", + "cve": "CVE-2020-15213", + "id": "pyup.io-44199", + "more_info_path": "/vulnerabilities/CVE-2020-15213/44199", "specs": [ "<2.4.0" ], @@ -19398,9 +19594,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37661", - "id": "pyup.io-44353", - "more_info_path": "/vulnerabilities/CVE-2021-37661/44353", + "cve": "CVE-2020-26267", + "id": "pyup.io-44205", + "more_info_path": "/vulnerabilities/CVE-2020-26267/44205", "specs": [ "<2.4.0" ], @@ -19408,9 +19604,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37666", - "id": "pyup.io-44358", - "more_info_path": "/vulnerabilities/CVE-2021-37666/44358", + "cve": "CVE-2021-29521", + "id": "pyup.io-44226", + "more_info_path": "/vulnerabilities/CVE-2021-29521/44226", "specs": [ "<2.4.0" ], @@ -19418,9 +19614,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29584", - "id": "pyup.io-44291", - "more_info_path": "/vulnerabilities/CVE-2021-29584/44291", + "cve": "CVE-2021-29541", + "id": "pyup.io-44246", + "more_info_path": "/vulnerabilities/CVE-2021-29541/44246", "specs": [ "<2.4.0" ], @@ -19428,9 +19624,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37669", - "id": "pyup.io-44361", - "more_info_path": "/vulnerabilities/CVE-2021-37669/44361", + "cve": "CVE-2021-29543", + "id": "pyup.io-44248", + "more_info_path": "/vulnerabilities/CVE-2021-29543/44248", "specs": [ "<2.4.0" ], @@ -19438,9 +19634,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37688", - "id": "pyup.io-44380", - "more_info_path": "/vulnerabilities/CVE-2021-37688/44380", + "cve": "CVE-2021-29581", + "id": "pyup.io-44288", + "more_info_path": "/vulnerabilities/CVE-2021-29581/44288", "specs": [ "<2.4.0" ], @@ -19448,9 +19644,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29580", - "id": "pyup.io-44287", - "more_info_path": "/vulnerabilities/CVE-2021-29580/44287", + "cve": "CVE-2020-8169", + "id": "pyup.io-44209", + "more_info_path": "/vulnerabilities/CVE-2020-8169/44209", "specs": [ "<2.4.0" ], @@ -19458,9 +19654,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29596", - "id": "pyup.io-44303", - "more_info_path": "/vulnerabilities/CVE-2021-29596/44303", + "cve": "CVE-2021-29598", + "id": "pyup.io-44305", + "more_info_path": "/vulnerabilities/CVE-2021-29598/44305", "specs": [ "<2.4.0" ], @@ -19478,9 +19674,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29591", - "id": "pyup.io-44298", - "more_info_path": "/vulnerabilities/CVE-2021-29591/44298", + "cve": "CVE-2019-20838", + "id": "pyup.io-41298", + "more_info_path": "/vulnerabilities/CVE-2019-20838/41298", "specs": [ "<2.4.0" ], @@ -19488,9 +19684,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37664", - "id": "pyup.io-44356", - "more_info_path": "/vulnerabilities/CVE-2021-37664/44356", + "cve": "CVE-2021-29595", + "id": "pyup.io-44302", + "more_info_path": "/vulnerabilities/CVE-2021-29595/44302", "specs": [ "<2.4.0" ], @@ -19498,9 +19694,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37682", - "id": "pyup.io-44374", - "more_info_path": "/vulnerabilities/CVE-2021-37682/44374", + "cve": "CVE-2021-29589", + "id": "pyup.io-44296", + "more_info_path": "/vulnerabilities/CVE-2021-29589/44296", "specs": [ "<2.4.0" ], @@ -19508,9 +19704,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15208", - "id": "pyup.io-44194", - "more_info_path": "/vulnerabilities/CVE-2020-15208/44194", + "cve": "CVE-2021-29578", + "id": "pyup.io-44285", + "more_info_path": "/vulnerabilities/CVE-2021-29578/44285", "specs": [ "<2.4.0" ], @@ -19518,9 +19714,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29548", - "id": "pyup.io-44253", - "more_info_path": "/vulnerabilities/CVE-2021-29548/44253", + "cve": "CVE-2021-29587", + "id": "pyup.io-44294", + "more_info_path": "/vulnerabilities/CVE-2021-29587/44294", "specs": [ "<2.4.0" ], @@ -19528,9 +19724,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15190", - "id": "pyup.io-44176", - "more_info_path": "/vulnerabilities/CVE-2020-15190/44176", + "cve": "CVE-2021-29585", + "id": "pyup.io-44292", + "more_info_path": "/vulnerabilities/CVE-2021-29585/44292", "specs": [ "<2.4.0" ], @@ -19538,9 +19734,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15191", - "id": "pyup.io-44177", - "more_info_path": "/vulnerabilities/CVE-2020-15191/44177", + "cve": "CVE-2021-29584", + "id": "pyup.io-44291", + "more_info_path": "/vulnerabilities/CVE-2021-29584/44291", "specs": [ "<2.4.0" ], @@ -19548,9 +19744,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15194", - "id": "pyup.io-44180", - "more_info_path": "/vulnerabilities/CVE-2020-15194/44180", + "cve": "CVE-2020-15358", + "id": "pyup.io-44203", + "more_info_path": "/vulnerabilities/CVE-2020-15358/44203", "specs": [ "<2.4.0" ], @@ -19558,9 +19754,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15203", - "id": "pyup.io-44189", - "more_info_path": "/vulnerabilities/CVE-2020-15203/44189", + "cve": "CVE-2021-29583", + "id": "pyup.io-44290", + "more_info_path": "/vulnerabilities/CVE-2021-29583/44290", "specs": [ "<2.4.0" ], @@ -19568,9 +19764,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15206", - "id": "pyup.io-44192", - "more_info_path": "/vulnerabilities/CVE-2020-15206/44192", + "cve": "CVE-2021-29580", + "id": "pyup.io-44287", + "more_info_path": "/vulnerabilities/CVE-2021-29580/44287", "specs": [ "<2.4.0" ], @@ -19578,9 +19774,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15266", - "id": "pyup.io-44202", - "more_info_path": "/vulnerabilities/CVE-2020-15266/44202", + "cve": "CVE-2021-29579", + "id": "pyup.io-44286", + "more_info_path": "/vulnerabilities/CVE-2021-29579/44286", "specs": [ "<2.4.0" ], @@ -19588,9 +19784,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29516", - "id": "pyup.io-44221", - "more_info_path": "/vulnerabilities/CVE-2021-29516/44221", + "cve": "CVE-2021-29571", + "id": "pyup.io-44278", + "more_info_path": "/vulnerabilities/CVE-2021-29571/44278", "specs": [ "<2.4.0" ], @@ -19598,9 +19794,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29519", - "id": "pyup.io-44224", - "more_info_path": "/vulnerabilities/CVE-2021-29519/44224", + "cve": "CVE-2021-29575", + "id": "pyup.io-44282", + "more_info_path": "/vulnerabilities/CVE-2021-29575/44282", "specs": [ "<2.4.0" ], @@ -19608,9 +19804,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29524", - "id": "pyup.io-44229", - "more_info_path": "/vulnerabilities/CVE-2021-29524/44229", + "cve": "CVE-2021-29569", + "id": "pyup.io-44273", + "more_info_path": "/vulnerabilities/CVE-2021-29569/44273", "specs": [ "<2.4.0" ], @@ -19618,9 +19814,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29530", - "id": "pyup.io-44235", - "more_info_path": "/vulnerabilities/CVE-2021-29530/44235", + "cve": "CVE-2021-29569", + "id": "pyup.io-44277", + "more_info_path": "/vulnerabilities/CVE-2021-29569/44277", "specs": [ "<2.4.0" ], @@ -19628,9 +19824,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29533", - "id": "pyup.io-44238", - "more_info_path": "/vulnerabilities/CVE-2021-29533/44238", + "cve": "CVE-2021-29568", + "id": "pyup.io-44272", + "more_info_path": "/vulnerabilities/CVE-2021-29568/44272", "specs": [ "<2.4.0" ], @@ -19638,9 +19834,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29535", - "id": "pyup.io-44240", - "more_info_path": "/vulnerabilities/CVE-2021-29535/44240", + "cve": "CVE-2021-29565", + "id": "pyup.io-44270", + "more_info_path": "/vulnerabilities/CVE-2021-29565/44270", "specs": [ "<2.4.0" ], @@ -19648,9 +19844,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29537", - "id": "pyup.io-44242", - "more_info_path": "/vulnerabilities/CVE-2021-29537/44242", + "cve": "CVE-2021-29558", + "id": "pyup.io-44263", + "more_info_path": "/vulnerabilities/CVE-2021-29558/44263", "specs": [ "<2.4.0" ], @@ -19658,9 +19854,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29539", - "id": "pyup.io-44244", - "more_info_path": "/vulnerabilities/CVE-2021-29539/44244", + "cve": "CVE-2021-29557", + "id": "pyup.io-44262", + "more_info_path": "/vulnerabilities/CVE-2021-29557/44262", "specs": [ "<2.4.0" ], @@ -19668,9 +19864,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29549", - "id": "pyup.io-44254", - "more_info_path": "/vulnerabilities/CVE-2021-29549/44254", + "cve": "CVE-2021-29548", + "id": "pyup.io-44253", + "more_info_path": "/vulnerabilities/CVE-2021-29548/44253", "specs": [ "<2.4.0" ], @@ -19678,9 +19874,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37638", - "id": "pyup.io-44330", - "more_info_path": "/vulnerabilities/CVE-2021-37638/44330", + "cve": "CVE-2021-29552", + "id": "pyup.io-44257", + "more_info_path": "/vulnerabilities/CVE-2021-29552/44257", "specs": [ "<2.4.0" ], @@ -19688,9 +19884,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29603", - "id": "pyup.io-44310", - "more_info_path": "/vulnerabilities/CVE-2021-29603/44310", + "cve": "CVE-2021-29546", + "id": "pyup.io-44251", + "more_info_path": "/vulnerabilities/CVE-2021-29546/44251", "specs": [ "<2.4.0" ], @@ -19698,9 +19894,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29607", - "id": "pyup.io-44314", - "more_info_path": "/vulnerabilities/CVE-2021-29607/44314", + "cve": "CVE-2021-29545", + "id": "pyup.io-44250", + "more_info_path": "/vulnerabilities/CVE-2021-29545/44250", "specs": [ "<2.4.0" ], @@ -19708,9 +19904,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29616", - "id": "pyup.io-44323", - "more_info_path": "/vulnerabilities/CVE-2021-29616/44323", + "cve": "CVE-2021-29549", + "id": "pyup.io-44254", + "more_info_path": "/vulnerabilities/CVE-2021-29549/44254", "specs": [ "<2.4.0" ], @@ -19718,9 +19914,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29552", - "id": "pyup.io-44257", - "more_info_path": "/vulnerabilities/CVE-2021-29552/44257", + "cve": "CVE-2020-15211", + "id": "pyup.io-44197", + "more_info_path": "/vulnerabilities/CVE-2020-15211/44197", "specs": [ "<2.4.0" ], @@ -19728,9 +19924,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29566", - "id": "pyup.io-44271", - "more_info_path": "/vulnerabilities/CVE-2021-29566/44271", + "cve": "CVE-2021-29542", + "id": "pyup.io-44247", + "more_info_path": "/vulnerabilities/CVE-2021-29542/44247", "specs": [ "<2.4.0" ], @@ -19738,9 +19934,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29570", - "id": "pyup.io-44276", - "more_info_path": "/vulnerabilities/CVE-2021-29570/44276", + "cve": "CVE-2021-29550", + "id": "pyup.io-44255", + "more_info_path": "/vulnerabilities/CVE-2021-29550/44255", "specs": [ "<2.4.0" ], @@ -19748,9 +19944,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29569", - "id": "pyup.io-44277", - "more_info_path": "/vulnerabilities/CVE-2021-29569/44277", + "cve": "CVE-2021-29536", + "id": "pyup.io-44241", + "more_info_path": "/vulnerabilities/CVE-2021-29536/44241", "specs": [ "<2.4.0" ], @@ -19758,9 +19954,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29574", - "id": "pyup.io-44281", - "more_info_path": "/vulnerabilities/CVE-2021-29574/44281", + "cve": "CVE-2021-29535", + "id": "pyup.io-44240", + "more_info_path": "/vulnerabilities/CVE-2021-29535/44240", "specs": [ "<2.4.0" ], @@ -19768,9 +19964,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29575", - "id": "pyup.io-44282", - "more_info_path": "/vulnerabilities/CVE-2021-29575/44282", + "cve": "CVE-2021-29534", + "id": "pyup.io-44239", + "more_info_path": "/vulnerabilities/CVE-2021-29534/44239", "specs": [ "<2.4.0" ], @@ -19778,9 +19974,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37639", - "id": "pyup.io-44331", - "more_info_path": "/vulnerabilities/CVE-2021-37639/44331", + "cve": "CVE-2021-29532", + "id": "pyup.io-44237", + "more_info_path": "/vulnerabilities/CVE-2021-29532/44237", "specs": [ "<2.4.0" ], @@ -19788,9 +19984,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37644", - "id": "pyup.io-44336", - "more_info_path": "/vulnerabilities/CVE-2021-37644/44336", + "cve": "CVE-2021-29531", + "id": "pyup.io-44236", + "more_info_path": "/vulnerabilities/CVE-2021-29531/44236", "specs": [ "<2.4.0" ], @@ -19798,9 +19994,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37646", - "id": "pyup.io-44338", - "more_info_path": "/vulnerabilities/CVE-2021-37646/44338", + "cve": "CVE-2021-29530", + "id": "pyup.io-44235", + "more_info_path": "/vulnerabilities/CVE-2021-29530/44235", "specs": [ "<2.4.0" ], @@ -19808,9 +20004,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37647", - "id": "pyup.io-44339", - "more_info_path": "/vulnerabilities/CVE-2021-37647/44339", + "cve": "CVE-2021-29529", + "id": "pyup.io-44234", + "more_info_path": "/vulnerabilities/CVE-2021-29529/44234", "specs": [ "<2.4.0" ], @@ -19818,9 +20014,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37648", - "id": "pyup.io-44340", - "more_info_path": "/vulnerabilities/CVE-2021-37648/44340", + "cve": "CVE-2021-29528", + "id": "pyup.io-44233", + "more_info_path": "/vulnerabilities/CVE-2021-29528/44233", "specs": [ "<2.4.0" ], @@ -19828,9 +20024,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37649", - "id": "pyup.io-44341", - "more_info_path": "/vulnerabilities/CVE-2021-37649/44341", + "cve": "CVE-2021-29538", + "id": "pyup.io-44243", + "more_info_path": "/vulnerabilities/CVE-2021-29538/44243", "specs": [ "<2.4.0" ], @@ -19838,9 +20034,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37650", - "id": "pyup.io-44342", - "more_info_path": "/vulnerabilities/CVE-2021-37650/44342", + "cve": "CVE-2021-29537", + "id": "pyup.io-44242", + "more_info_path": "/vulnerabilities/CVE-2021-29537/44242", "specs": [ "<2.4.0" ], @@ -19848,9 +20044,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37651", - "id": "pyup.io-44343", - "more_info_path": "/vulnerabilities/CVE-2021-37651/44343", + "cve": "CVE-2021-29522", + "id": "pyup.io-44227", + "more_info_path": "/vulnerabilities/CVE-2021-29522/44227", "specs": [ "<2.4.0" ], @@ -19858,9 +20054,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37653", - "id": "pyup.io-44345", - "more_info_path": "/vulnerabilities/CVE-2021-37653/44345", + "cve": "CVE-2021-29520", + "id": "pyup.io-44225", + "more_info_path": "/vulnerabilities/CVE-2021-29520/44225", "specs": [ "<2.4.0" ], @@ -19868,9 +20064,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37657", - "id": "pyup.io-44349", - "more_info_path": "/vulnerabilities/CVE-2021-37657/44349", + "cve": "CVE-2021-29519", + "id": "pyup.io-44224", + "more_info_path": "/vulnerabilities/CVE-2021-29519/44224", "specs": [ "<2.4.0" ], @@ -19878,9 +20074,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37658", - "id": "pyup.io-44350", - "more_info_path": "/vulnerabilities/CVE-2021-37658/44350", + "cve": "CVE-2021-29518", + "id": "pyup.io-44223", + "more_info_path": "/vulnerabilities/CVE-2021-29518/44223", "specs": [ "<2.4.0" ], @@ -19888,9 +20084,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37659", - "id": "pyup.io-44351", - "more_info_path": "/vulnerabilities/CVE-2021-37659/44351", + "cve": "CVE-2021-29517", + "id": "pyup.io-44222", + "more_info_path": "/vulnerabilities/CVE-2021-29517/44222", "specs": [ "<2.4.0" ], @@ -19898,9 +20094,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37663", - "id": "pyup.io-44355", - "more_info_path": "/vulnerabilities/CVE-2021-37663/44355", + "cve": "CVE-2021-29515", + "id": "pyup.io-44220", + "more_info_path": "/vulnerabilities/CVE-2021-29515/44220", "specs": [ "<2.4.0" ], @@ -19908,9 +20104,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37667", - "id": "pyup.io-44359", - "more_info_path": "/vulnerabilities/CVE-2021-37667/44359", + "cve": "CVE-2021-29514", + "id": "pyup.io-44219", + "more_info_path": "/vulnerabilities/CVE-2021-29514/44219", "specs": [ "<2.4.0" ], @@ -19918,9 +20114,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37668", - "id": "pyup.io-44360", - "more_info_path": "/vulnerabilities/CVE-2021-37668/44360", + "cve": "CVE-2021-29554", + "id": "pyup.io-44259", + "more_info_path": "/vulnerabilities/CVE-2021-29554/44259", "specs": [ "<2.4.0" ], @@ -19928,9 +20124,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37671", - "id": "pyup.io-44363", - "more_info_path": "/vulnerabilities/CVE-2021-37671/44363", + "cve": "CVE-2021-29525", + "id": "pyup.io-44230", + "more_info_path": "/vulnerabilities/CVE-2021-29525/44230", "specs": [ "<2.4.0" ], @@ -19938,9 +20134,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37672", - "id": "pyup.io-44364", - "more_info_path": "/vulnerabilities/CVE-2021-37672/44364", + "cve": "CVE-2021-29599", + "id": "pyup.io-44306", + "more_info_path": "/vulnerabilities/CVE-2021-29599/44306", "specs": [ "<2.4.0" ], @@ -19948,9 +20144,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37675", - "id": "pyup.io-44367", - "more_info_path": "/vulnerabilities/CVE-2021-37675/44367", + "cve": "CVE-2021-22876", + "id": "pyup.io-44214", + "more_info_path": "/vulnerabilities/CVE-2021-22876/44214", "specs": [ "<2.4.0" ], @@ -19958,9 +20154,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37676", - "id": "pyup.io-44368", - "more_info_path": "/vulnerabilities/CVE-2021-37676/44368", + "cve": "CVE-2020-8284", + "id": "pyup.io-44212", + "more_info_path": "/vulnerabilities/CVE-2020-8284/44212", "specs": [ "<2.4.0" ], @@ -19968,9 +20164,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37679", - "id": "pyup.io-44371", - "more_info_path": "/vulnerabilities/CVE-2021-37679/44371", + "cve": "CVE-2021-37691", + "id": "pyup.io-44383", + "more_info_path": "/vulnerabilities/CVE-2021-37691/44383", "specs": [ "<2.4.0" ], @@ -19978,9 +20174,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37680", - "id": "pyup.io-44372", - "more_info_path": "/vulnerabilities/CVE-2021-37680/44372", + "cve": "CVE-2021-37674", + "id": "pyup.io-44366", + "more_info_path": "/vulnerabilities/CVE-2021-37674/44366", "specs": [ "<2.4.0" ], @@ -19988,9 +20184,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37683", - "id": "pyup.io-44375", - "more_info_path": "/vulnerabilities/CVE-2021-37683/44375", + "cve": "CVE-2021-37672", + "id": "pyup.io-44364", + "more_info_path": "/vulnerabilities/CVE-2021-37672/44364", "specs": [ "<2.4.0" ], @@ -19998,9 +20194,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37684", - "id": "pyup.io-44376", - "more_info_path": "/vulnerabilities/CVE-2021-37684/44376", + "cve": "CVE-2021-37677", + "id": "pyup.io-44369", + "more_info_path": "/vulnerabilities/CVE-2021-37677/44369", "specs": [ "<2.4.0" ], @@ -20008,9 +20204,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37685", - "id": "pyup.io-44377", - "more_info_path": "/vulnerabilities/CVE-2021-37685/44377", + "cve": "CVE-2020-26270", + "id": "pyup.io-44207", + "more_info_path": "/vulnerabilities/CVE-2020-26270/44207", "specs": [ "<2.4.0" ], @@ -20018,9 +20214,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37686", - "id": "pyup.io-44378", - "more_info_path": "/vulnerabilities/CVE-2021-37686/44378", + "cve": "CVE-2020-26268", + "id": "pyup.io-44206", + "more_info_path": "/vulnerabilities/CVE-2020-26268/44206", "specs": [ "<2.4.0" ], @@ -20028,19 +20224,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37691", - "id": "pyup.io-44383", - "more_info_path": "/vulnerabilities/CVE-2021-37691/44383", - "specs": [ - "<2.4.0" - ], - "v": "<2.4.0" - }, - { - "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15195", - "id": "pyup.io-44181", - "more_info_path": "/vulnerabilities/CVE-2020-15195/44181", + "cve": "CVE-2021-37670", + "id": "pyup.io-44362", + "more_info_path": "/vulnerabilities/CVE-2021-37670/44362", "specs": [ "<2.4.0" ], @@ -20048,9 +20234,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15209", - "id": "pyup.io-44195", - "more_info_path": "/vulnerabilities/CVE-2020-15209/44195", + "cve": "CVE-2020-15266", + "id": "pyup.io-44202", + "more_info_path": "/vulnerabilities/CVE-2020-15266/44202", "specs": [ "<2.4.0" ], @@ -20058,9 +20244,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-26271", - "id": "pyup.io-44208", - "more_info_path": "/vulnerabilities/CVE-2020-26271/44208", + "cve": "CVE-2020-15265", + "id": "pyup.io-44201", + "more_info_path": "/vulnerabilities/CVE-2020-15265/44201", "specs": [ "<2.4.0" ], @@ -20068,9 +20254,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29517", - "id": "pyup.io-44222", - "more_info_path": "/vulnerabilities/CVE-2021-29517/44222", + "cve": "CVE-2020-15214", + "id": "pyup.io-44200", + "more_info_path": "/vulnerabilities/CVE-2020-15214/44200", "specs": [ "<2.4.0" ], @@ -20078,9 +20264,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29518", - "id": "pyup.io-44223", - "more_info_path": "/vulnerabilities/CVE-2021-29518/44223", + "cve": "CVE-2020-15212", + "id": "pyup.io-44198", + "more_info_path": "/vulnerabilities/CVE-2020-15212/44198", "specs": [ "<2.4.0" ], @@ -20088,9 +20274,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29523", - "id": "pyup.io-44228", - "more_info_path": "/vulnerabilities/CVE-2021-29523/44228", + "cve": "CVE-2020-15209", + "id": "pyup.io-44195", + "more_info_path": "/vulnerabilities/CVE-2020-15209/44195", "specs": [ "<2.4.0" ], @@ -20098,9 +20284,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29526", - "id": "pyup.io-44231", - "more_info_path": "/vulnerabilities/CVE-2021-29526/44231", + "cve": "CVE-2020-15208", + "id": "pyup.io-44194", + "more_info_path": "/vulnerabilities/CVE-2020-15208/44194", "specs": [ "<2.4.0" ], @@ -20108,9 +20294,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29534", - "id": "pyup.io-44239", - "more_info_path": "/vulnerabilities/CVE-2021-29534/44239", + "cve": "CVE-2020-15207", + "id": "pyup.io-44193", + "more_info_path": "/vulnerabilities/CVE-2020-15207/44193", "specs": [ "<2.4.0" ], @@ -20118,9 +20304,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15192", - "id": "pyup.io-44178", - "more_info_path": "/vulnerabilities/CVE-2020-15192/44178", + "cve": "CVE-2020-15205", + "id": "pyup.io-44191", + "more_info_path": "/vulnerabilities/CVE-2020-15205/44191", "specs": [ "<2.4.0" ], @@ -20128,9 +20314,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15193", - "id": "pyup.io-44179", - "more_info_path": "/vulnerabilities/CVE-2020-15193/44179", + "cve": "CVE-2020-8231", + "id": "pyup.io-44211", + "more_info_path": "/vulnerabilities/CVE-2020-8231/44211", "specs": [ "<2.4.0" ], @@ -20138,9 +20324,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15196", - "id": "pyup.io-44182", - "more_info_path": "/vulnerabilities/CVE-2020-15196/44182", + "cve": "CVE-2020-15202", + "id": "pyup.io-44188", + "more_info_path": "/vulnerabilities/CVE-2020-15202/44188", "specs": [ "<2.4.0" ], @@ -20148,9 +20334,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15197", - "id": "pyup.io-44183", - "more_info_path": "/vulnerabilities/CVE-2020-15197/44183", + "cve": "CVE-2020-15201", + "id": "pyup.io-44187", + "more_info_path": "/vulnerabilities/CVE-2020-15201/44187", "specs": [ "<2.4.0" ], @@ -20168,9 +20354,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15199", - "id": "pyup.io-44185", - "more_info_path": "/vulnerabilities/CVE-2020-15199/44185", + "cve": "CVE-2021-22897", + "id": "pyup.io-44215", + "more_info_path": "/vulnerabilities/CVE-2021-22897/44215", "specs": [ "<2.4.0" ], @@ -20178,9 +20364,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15200", - "id": "pyup.io-44186", - "more_info_path": "/vulnerabilities/CVE-2020-15200/44186", + "cve": "CVE-2020-13790", + "id": "pyup.io-44174", + "more_info_path": "/vulnerabilities/CVE-2020-13790/44174", "specs": [ "<2.4.0" ], @@ -20188,9 +20374,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15201", - "id": "pyup.io-44187", - "more_info_path": "/vulnerabilities/CVE-2020-15201/44187", + "cve": "CVE-2020-15197", + "id": "pyup.io-44183", + "more_info_path": "/vulnerabilities/CVE-2020-15197/44183", "specs": [ "<2.4.0" ], @@ -20198,9 +20384,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15205", - "id": "pyup.io-44191", - "more_info_path": "/vulnerabilities/CVE-2020-15205/44191", + "cve": "CVE-2021-29605", + "id": "pyup.io-44312", + "more_info_path": "/vulnerabilities/CVE-2021-29605/44312", "specs": [ "<2.4.0" ], @@ -20208,9 +20394,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15207", - "id": "pyup.io-44193", - "more_info_path": "/vulnerabilities/CVE-2020-15207/44193", + "cve": "CVE-2020-15194", + "id": "pyup.io-44180", + "more_info_path": "/vulnerabilities/CVE-2020-15194/44180", "specs": [ "<2.4.0" ], @@ -20218,9 +20404,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15210", - "id": "pyup.io-44196", - "more_info_path": "/vulnerabilities/CVE-2020-15210/44196", + "cve": "CVE-2020-15193", + "id": "pyup.io-44179", + "more_info_path": "/vulnerabilities/CVE-2020-15193/44179", "specs": [ "<2.4.0" ], @@ -20228,9 +20414,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15213", - "id": "pyup.io-44199", - "more_info_path": "/vulnerabilities/CVE-2020-15213/44199", + "cve": "CVE-2020-15192", + "id": "pyup.io-44178", + "more_info_path": "/vulnerabilities/CVE-2020-15192/44178", "specs": [ "<2.4.0" ], @@ -20238,9 +20424,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15214", - "id": "pyup.io-44200", - "more_info_path": "/vulnerabilities/CVE-2020-15214/44200", + "cve": "CVE-2020-15190", + "id": "pyup.io-44176", + "more_info_path": "/vulnerabilities/CVE-2020-15190/44176", "specs": [ "<2.4.0" ], @@ -20248,9 +20434,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-26266", - "id": "pyup.io-44204", - "more_info_path": "/vulnerabilities/CVE-2020-26266/44204", + "cve": "CVE-2021-29596", + "id": "pyup.io-44303", + "more_info_path": "/vulnerabilities/CVE-2021-29596/44303", "specs": [ "<2.4.0" ], @@ -20258,9 +20444,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15265", - "id": "pyup.io-44201", - "more_info_path": "/vulnerabilities/CVE-2020-15265/44201", + "cve": "CVE-2021-29551", + "id": "pyup.io-44256", + "more_info_path": "/vulnerabilities/CVE-2021-29551/44256", "specs": [ "<2.4.0" ], @@ -20268,9 +20454,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-26267", - "id": "pyup.io-44205", - "more_info_path": "/vulnerabilities/CVE-2020-26267/44205", + "cve": "CVE-2021-37678", + "id": "pyup.io-44370", + "more_info_path": "/vulnerabilities/CVE-2021-37678/44370", "specs": [ "<2.4.0" ], @@ -20278,9 +20464,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-26268", - "id": "pyup.io-44206", - "more_info_path": "/vulnerabilities/CVE-2020-26268/44206", + "cve": "CVE-2020-15199", + "id": "pyup.io-44185", + "more_info_path": "/vulnerabilities/CVE-2020-15199/44185", "specs": [ "<2.4.0" ], @@ -20288,9 +20474,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-26270", - "id": "pyup.io-44207", - "more_info_path": "/vulnerabilities/CVE-2020-26270/44207", + "cve": "CVE-2021-29564", + "id": "pyup.io-44269", + "more_info_path": "/vulnerabilities/CVE-2021-29564/44269", "specs": [ "<2.4.0" ], @@ -20308,9 +20494,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29514", - "id": "pyup.io-44219", - "more_info_path": "/vulnerabilities/CVE-2021-29514/44219", + "cve": "CVE-2021-37642", + "id": "pyup.io-44334", + "more_info_path": "/vulnerabilities/CVE-2021-37642/44334", "specs": [ "<2.4.0" ], @@ -20318,9 +20504,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29515", - "id": "pyup.io-44220", - "more_info_path": "/vulnerabilities/CVE-2021-29515/44220", + "cve": "CVE-2021-37690", + "id": "pyup.io-44382", + "more_info_path": "/vulnerabilities/CVE-2021-37690/44382", "specs": [ "<2.4.0" ], @@ -20328,9 +20514,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29521", - "id": "pyup.io-44226", - "more_info_path": "/vulnerabilities/CVE-2021-29521/44226", + "cve": "CVE-2021-37687", + "id": "pyup.io-44379", + "more_info_path": "/vulnerabilities/CVE-2021-37687/44379", "specs": [ "<2.4.0" ], @@ -20338,9 +20524,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29522", - "id": "pyup.io-44227", - "more_info_path": "/vulnerabilities/CVE-2021-29522/44227", + "cve": "CVE-2021-37685", + "id": "pyup.io-44377", + "more_info_path": "/vulnerabilities/CVE-2021-37685/44377", "specs": [ "<2.4.0" ], @@ -20348,9 +20534,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29525", - "id": "pyup.io-44230", - "more_info_path": "/vulnerabilities/CVE-2021-29525/44230", + "cve": "CVE-2021-37683", + "id": "pyup.io-44375", + "more_info_path": "/vulnerabilities/CVE-2021-37683/44375", "specs": [ "<2.4.0" ], @@ -20358,9 +20544,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29527", - "id": "pyup.io-44232", - "more_info_path": "/vulnerabilities/CVE-2021-29527/44232", + "cve": "CVE-2021-37682", + "id": "pyup.io-44374", + "more_info_path": "/vulnerabilities/CVE-2021-37682/44374", "specs": [ "<2.4.0" ], @@ -20368,9 +20554,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29528", - "id": "pyup.io-44233", - "more_info_path": "/vulnerabilities/CVE-2021-29528/44233", + "cve": "CVE-2021-37673", + "id": "pyup.io-44365", + "more_info_path": "/vulnerabilities/CVE-2021-37673/44365", "specs": [ "<2.4.0" ], @@ -20378,9 +20564,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29531", - "id": "pyup.io-44236", - "more_info_path": "/vulnerabilities/CVE-2021-29531/44236", + "cve": "CVE-2021-37669", + "id": "pyup.io-44361", + "more_info_path": "/vulnerabilities/CVE-2021-37669/44361", "specs": [ "<2.4.0" ], @@ -20388,9 +20574,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29532", - "id": "pyup.io-44237", - "more_info_path": "/vulnerabilities/CVE-2021-29532/44237", + "cve": "CVE-2021-37668", + "id": "pyup.io-44360", + "more_info_path": "/vulnerabilities/CVE-2021-37668/44360", "specs": [ "<2.4.0" ], @@ -20398,9 +20584,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29538", - "id": "pyup.io-44243", - "more_info_path": "/vulnerabilities/CVE-2021-29538/44243", + "cve": "CVE-2021-37665", + "id": "pyup.io-44357", + "more_info_path": "/vulnerabilities/CVE-2021-37665/44357", "specs": [ "<2.4.0" ], @@ -20408,9 +20594,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29541", - "id": "pyup.io-44246", - "more_info_path": "/vulnerabilities/CVE-2021-29541/44246", + "cve": "CVE-2021-37663", + "id": "pyup.io-44355", + "more_info_path": "/vulnerabilities/CVE-2021-37663/44355", "specs": [ "<2.4.0" ], @@ -20418,9 +20604,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29545", - "id": "pyup.io-44250", - "more_info_path": "/vulnerabilities/CVE-2021-29545/44250", + "cve": "CVE-2021-37688", + "id": "pyup.io-44380", + "more_info_path": "/vulnerabilities/CVE-2021-37688/44380", "specs": [ "<2.4.0" ], @@ -20428,9 +20614,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29543", - "id": "pyup.io-44248", - "more_info_path": "/vulnerabilities/CVE-2021-29543/44248", + "cve": "CVE-2021-37686", + "id": "pyup.io-44378", + "more_info_path": "/vulnerabilities/CVE-2021-37686/44378", "specs": [ "<2.4.0" ], @@ -20438,9 +20624,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29546", - "id": "pyup.io-44251", - "more_info_path": "/vulnerabilities/CVE-2021-29546/44251", + "cve": "CVE-2021-37681", + "id": "pyup.io-44373", + "more_info_path": "/vulnerabilities/CVE-2021-37681/44373", "specs": [ "<2.4.0" ], @@ -20448,9 +20634,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29547", - "id": "pyup.io-44252", - "more_info_path": "/vulnerabilities/CVE-2021-29547/44252", + "cve": "CVE-2021-37676", + "id": "pyup.io-44368", + "more_info_path": "/vulnerabilities/CVE-2021-37676/44368", "specs": [ "<2.4.0" ], @@ -20458,9 +20644,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-8177", - "id": "pyup.io-44210", - "more_info_path": "/vulnerabilities/CVE-2020-8177/44210", + "cve": "CVE-2021-37671", + "id": "pyup.io-44363", + "more_info_path": "/vulnerabilities/CVE-2021-37671/44363", "specs": [ "<2.4.0" ], @@ -20468,9 +20654,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29614", - "id": "pyup.io-44321", - "more_info_path": "/vulnerabilities/CVE-2021-29614/44321", + "cve": "CVE-2021-37652", + "id": "pyup.io-44344", + "more_info_path": "/vulnerabilities/CVE-2021-37652/44344", "specs": [ "<2.4.0" ], @@ -20478,9 +20664,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29542", - "id": "pyup.io-44247", - "more_info_path": "/vulnerabilities/CVE-2021-29542/44247", + "cve": "CVE-2021-37648", + "id": "pyup.io-44340", + "more_info_path": "/vulnerabilities/CVE-2021-37648/44340", "specs": [ "<2.4.0" ], @@ -20488,9 +20674,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29520", - "id": "pyup.io-44225", - "more_info_path": "/vulnerabilities/CVE-2021-29520/44225", + "cve": "CVE-2021-37662", + "id": "pyup.io-44354", + "more_info_path": "/vulnerabilities/CVE-2021-37662/44354", "specs": [ "<2.4.0" ], @@ -20498,9 +20684,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-22876", - "id": "pyup.io-44214", - "more_info_path": "/vulnerabilities/CVE-2021-22876/44214", + "cve": "CVE-2021-37661", + "id": "pyup.io-44353", + "more_info_path": "/vulnerabilities/CVE-2021-37661/44353", "specs": [ "<2.4.0" ], @@ -20508,9 +20694,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-8286", - "id": "pyup.io-44213", - "more_info_path": "/vulnerabilities/CVE-2020-8286/44213", + "cve": "CVE-2021-37659", + "id": "pyup.io-44351", + "more_info_path": "/vulnerabilities/CVE-2021-37659/44351", "specs": [ "<2.4.0" ], @@ -20518,9 +20704,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-8169", - "id": "pyup.io-44209", - "more_info_path": "/vulnerabilities/CVE-2020-8169/44209", + "cve": "CVE-2021-37657", + "id": "pyup.io-44349", + "more_info_path": "/vulnerabilities/CVE-2021-37657/44349", "specs": [ "<2.4.0" ], @@ -20528,9 +20714,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-14155", - "id": "pyup.io-44175", - "more_info_path": "/vulnerabilities/CVE-2020-14155/44175", + "cve": "CVE-2021-37655", + "id": "pyup.io-44347", + "more_info_path": "/vulnerabilities/CVE-2021-37655/44347", "specs": [ "<2.4.0" ], @@ -20538,9 +20724,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2019-20838", - "id": "pyup.io-41298", - "more_info_path": "/vulnerabilities/CVE-2019-20838/41298", + "cve": "CVE-2021-37654", + "id": "pyup.io-44346", + "more_info_path": "/vulnerabilities/CVE-2021-37654/44346", "specs": [ "<2.4.0" ], @@ -20548,9 +20734,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-15358", - "id": "pyup.io-44203", - "more_info_path": "/vulnerabilities/CVE-2020-15358/44203", + "cve": "CVE-2021-37651", + "id": "pyup.io-44343", + "more_info_path": "/vulnerabilities/CVE-2021-37651/44343", "specs": [ "<2.4.0" ], @@ -20558,9 +20744,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29610", - "id": "pyup.io-44317", - "more_info_path": "/vulnerabilities/CVE-2021-29610/44317", + "cve": "CVE-2021-37646", + "id": "pyup.io-44338", + "more_info_path": "/vulnerabilities/CVE-2021-37646/44338", "specs": [ "<2.4.0" ], @@ -20568,9 +20754,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-8284", - "id": "pyup.io-44212", - "more_info_path": "/vulnerabilities/CVE-2020-8284/44212", + "cve": "CVE-2021-37645", + "id": "pyup.io-44337", + "more_info_path": "/vulnerabilities/CVE-2021-37645/44337", "specs": [ "<2.4.0" ], @@ -20578,9 +20764,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37674", - "id": "pyup.io-44366", - "more_info_path": "/vulnerabilities/CVE-2021-37674/44366", + "cve": "CVE-2021-37644", + "id": "pyup.io-44336", + "more_info_path": "/vulnerabilities/CVE-2021-37644/44336", "specs": [ "<2.4.0" ], @@ -20588,9 +20774,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29568", - "id": "pyup.io-44272", - "more_info_path": "/vulnerabilities/CVE-2021-29568/44272", + "cve": "CVE-2021-37641", + "id": "pyup.io-44333", + "more_info_path": "/vulnerabilities/CVE-2021-37641/44333", "specs": [ "<2.4.0" ], @@ -20598,9 +20784,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-37677", - "id": "pyup.io-44369", - "more_info_path": "/vulnerabilities/CVE-2021-37677/44369", + "cve": "CVE-2021-37635", + "id": "pyup.io-44327", + "more_info_path": "/vulnerabilities/CVE-2021-37635/44327", "specs": [ "<2.4.0" ], @@ -20608,9 +20794,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-8231", - "id": "pyup.io-44211", - "more_info_path": "/vulnerabilities/CVE-2020-8231/44211", + "cve": "CVE-2021-37647", + "id": "pyup.io-44339", + "more_info_path": "/vulnerabilities/CVE-2021-37647/44339", "specs": [ "<2.4.0" ], @@ -20618,9 +20804,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2020-13790", - "id": "pyup.io-44174", - "more_info_path": "/vulnerabilities/CVE-2020-13790/44174", + "cve": "CVE-2021-37643", + "id": "pyup.io-44335", + "more_info_path": "/vulnerabilities/CVE-2021-37643/44335", "specs": [ "<2.4.0" ], @@ -20628,9 +20814,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-22901", - "id": "pyup.io-44217", - "more_info_path": "/vulnerabilities/CVE-2021-22901/44217", + "cve": "CVE-2021-37637", + "id": "pyup.io-44329", + "more_info_path": "/vulnerabilities/CVE-2021-37637/44329", "specs": [ "<2.4.0" ], @@ -20638,9 +20824,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-22898", - "id": "pyup.io-44216", - "more_info_path": "/vulnerabilities/CVE-2021-22898/44216", + "cve": "CVE-2021-37660", + "id": "pyup.io-44352", + "more_info_path": "/vulnerabilities/CVE-2021-37660/44352", "specs": [ "<2.4.0" ], @@ -20648,9 +20834,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-22897", - "id": "pyup.io-44215", - "more_info_path": "/vulnerabilities/CVE-2021-22897/44215", + "cve": "CVE-2021-37640", + "id": "pyup.io-44332", + "more_info_path": "/vulnerabilities/CVE-2021-37640/44332", "specs": [ "<2.4.0" ], @@ -20658,9 +20844,9 @@ }, { "advisory": "Chia 2.4.0 updates Tensorflow to v2.4.3 to include security fixes.", - "cve": "CVE-2021-29544", - "id": "pyup.io-44249", - "more_info_path": "/vulnerabilities/CVE-2021-29544/44249", + "cve": "CVE-2021-37636", + "id": "pyup.io-44328", + "more_info_path": "/vulnerabilities/CVE-2021-37636/44328", "specs": [ "<2.4.0" ], @@ -20668,9 +20854,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41213", - "id": "pyup.io-46814", - "more_info_path": "/vulnerabilities/CVE-2021-41213/46814", + "cve": "CVE-2021-22925", + "id": "pyup.io-46795", + "more_info_path": "/vulnerabilities/CVE-2021-22925/46795", "specs": [ "<=2.5.0" ], @@ -20678,9 +20864,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23568", - "id": "pyup.io-46858", - "more_info_path": "/vulnerabilities/CVE-2022-23568/46858", + "cve": "CVE-2021-41211", + "id": "pyup.io-46812", + "more_info_path": "/vulnerabilities/CVE-2021-41211/46812", "specs": [ "<=2.5.0" ], @@ -20688,9 +20874,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41214", - "id": "pyup.io-46815", - "more_info_path": "/vulnerabilities/CVE-2021-41214/46815", + "cve": "CVE-2021-22922", + "id": "pyup.io-46792", + "more_info_path": "/vulnerabilities/CVE-2021-22922/46792", "specs": [ "<=2.5.0" ], @@ -20698,9 +20884,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41220", - "id": "pyup.io-46821", - "more_info_path": "/vulnerabilities/CVE-2021-41220/46821", + "cve": "CVE-2021-41221", + "id": "pyup.io-46822", + "more_info_path": "/vulnerabilities/CVE-2021-41221/46822", "specs": [ "<=2.5.0" ], @@ -20708,9 +20894,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41223", - "id": "pyup.io-46824", - "more_info_path": "/vulnerabilities/CVE-2021-41223/46824", + "cve": "CVE-2022-21734", + "id": "pyup.io-46839", + "more_info_path": "/vulnerabilities/CVE-2022-21734/46839", "specs": [ "<=2.5.0" ], @@ -20718,9 +20904,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41224", - "id": "pyup.io-46825", - "more_info_path": "/vulnerabilities/CVE-2021-41224/46825", + "cve": "CVE-2022-21731", + "id": "pyup.io-46836", + "more_info_path": "/vulnerabilities/CVE-2022-21731/46836", "specs": [ "<=2.5.0" ], @@ -20728,9 +20914,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41226", - "id": "pyup.io-46827", - "more_info_path": "/vulnerabilities/CVE-2021-41226/46827", + "cve": "CVE-2022-21737", + "id": "pyup.io-46842", + "more_info_path": "/vulnerabilities/CVE-2022-21737/46842", "specs": [ "<=2.5.0" ], @@ -20738,9 +20924,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41227", - "id": "pyup.io-46828", - "more_info_path": "/vulnerabilities/CVE-2021-41227/46828", + "cve": "CVE-2022-23559", + "id": "pyup.io-46849", + "more_info_path": "/vulnerabilities/CVE-2022-23559/46849", "specs": [ "<=2.5.0" ], @@ -20748,9 +20934,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21729", - "id": "pyup.io-46834", - "more_info_path": "/vulnerabilities/CVE-2022-21729/46834", + "cve": "CVE-2022-23562", + "id": "pyup.io-46852", + "more_info_path": "/vulnerabilities/CVE-2022-23562/46852", "specs": [ "<=2.5.0" ], @@ -20758,9 +20944,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21730", - "id": "pyup.io-46835", - "more_info_path": "/vulnerabilities/CVE-2022-21730/46835", + "cve": "CVE-2022-23565", + "id": "pyup.io-46855", + "more_info_path": "/vulnerabilities/CVE-2022-23565/46855", "specs": [ "<=2.5.0" ], @@ -20768,9 +20954,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21732", - "id": "pyup.io-46837", - "more_info_path": "/vulnerabilities/CVE-2022-21732/46837", + "cve": "CVE-2022-23573", + "id": "pyup.io-46863", + "more_info_path": "/vulnerabilities/CVE-2022-23573/46863", "specs": [ "<=2.5.0" ], @@ -20778,9 +20964,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21733", - "id": "pyup.io-46838", - "more_info_path": "/vulnerabilities/CVE-2022-21733/46838", + "cve": "CVE-2022-23575", + "id": "pyup.io-46865", + "more_info_path": "/vulnerabilities/CVE-2022-23575/46865", "specs": [ "<=2.5.0" ], @@ -20788,9 +20974,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23587", - "id": "pyup.io-46877", - "more_info_path": "/vulnerabilities/CVE-2022-23587/46877", + "cve": "CVE-2021-41219", + "id": "pyup.io-46820", + "more_info_path": "/vulnerabilities/CVE-2021-41219/46820", "specs": [ "<=2.5.0" ], @@ -20798,9 +20984,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23589", - "id": "pyup.io-46879", - "more_info_path": "/vulnerabilities/CVE-2022-23589/46879", + "cve": "CVE-2022-23591", + "id": "pyup.io-46880", + "more_info_path": "/vulnerabilities/CVE-2022-23591/46880", "specs": [ "<=2.5.0" ], @@ -20808,9 +20994,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23595", - "id": "pyup.io-46881", - "more_info_path": "/vulnerabilities/CVE-2022-23595/46881", + "cve": "CVE-2021-22923", + "id": "pyup.io-46793", + "more_info_path": "/vulnerabilities/CVE-2021-22923/46793", "specs": [ "<=2.5.0" ], @@ -20818,9 +21004,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41219", - "id": "pyup.io-46820", - "more_info_path": "/vulnerabilities/CVE-2021-41219/46820", + "cve": "CVE-2022-21725", + "id": "pyup.io-46830", + "more_info_path": "/vulnerabilities/CVE-2022-21725/46830", "specs": [ "<=2.5.0" ], @@ -20828,9 +21014,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41221", - "id": "pyup.io-46822", - "more_info_path": "/vulnerabilities/CVE-2021-41221/46822", + "cve": "CVE-2021-41206", + "id": "pyup.io-46807", + "more_info_path": "/vulnerabilities/CVE-2021-41206/46807", "specs": [ "<=2.5.0" ], @@ -20838,9 +21024,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41222", - "id": "pyup.io-46823", - "more_info_path": "/vulnerabilities/CVE-2021-41222/46823", + "cve": "CVE-2021-41208", + "id": "pyup.io-46809", + "more_info_path": "/vulnerabilities/CVE-2021-41208/46809", "specs": [ "<=2.5.0" ], @@ -20848,9 +21034,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41225", - "id": "pyup.io-46826", - "more_info_path": "/vulnerabilities/CVE-2021-41225/46826", + "cve": "CVE-2021-41209", + "id": "pyup.io-46810", + "more_info_path": "/vulnerabilities/CVE-2021-41209/46810", "specs": [ "<=2.5.0" ], @@ -20858,9 +21044,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21726", - "id": "pyup.io-46831", - "more_info_path": "/vulnerabilities/CVE-2022-21726/46831", + "cve": "CVE-2021-41196", + "id": "pyup.io-46797", + "more_info_path": "/vulnerabilities/CVE-2021-41196/46797", "specs": [ "<=2.5.0" ], @@ -20868,9 +21054,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41228", - "id": "pyup.io-46829", - "more_info_path": "/vulnerabilities/CVE-2021-41228/46829", + "cve": "CVE-2021-41200", + "id": "pyup.io-46801", + "more_info_path": "/vulnerabilities/CVE-2021-41200/46801", "specs": [ "<=2.5.0" ], @@ -20878,9 +21064,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21727", - "id": "pyup.io-46832", - "more_info_path": "/vulnerabilities/CVE-2022-21727/46832", + "cve": "CVE-2021-41216", + "id": "pyup.io-46817", + "more_info_path": "/vulnerabilities/CVE-2021-41216/46817", "specs": [ "<=2.5.0" ], @@ -20888,9 +21074,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21728", - "id": "pyup.io-46833", - "more_info_path": "/vulnerabilities/CVE-2022-21728/46833", + "cve": "CVE-2022-23563", + "id": "pyup.io-46853", + "more_info_path": "/vulnerabilities/CVE-2022-23563/46853", "specs": [ "<=2.5.0" ], @@ -20898,9 +21084,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21731", - "id": "pyup.io-46836", - "more_info_path": "/vulnerabilities/CVE-2022-21731/46836", + "cve": "CVE-2022-23583", + "id": "pyup.io-46873", + "more_info_path": "/vulnerabilities/CVE-2022-23583/46873", "specs": [ "<=2.5.0" ], @@ -20908,9 +21094,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21734", - "id": "pyup.io-46839", - "more_info_path": "/vulnerabilities/CVE-2022-21734/46839", + "cve": "CVE-2021-22924", + "id": "pyup.io-46794", + "more_info_path": "/vulnerabilities/CVE-2021-22924/46794", "specs": [ "<=2.5.0" ], @@ -20918,9 +21104,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21736", - "id": "pyup.io-46841", - "more_info_path": "/vulnerabilities/CVE-2022-21736/46841", + "cve": "CVE-2021-41224", + "id": "pyup.io-46825", + "more_info_path": "/vulnerabilities/CVE-2021-41224/46825", "specs": [ "<=2.5.0" ], @@ -20928,9 +21114,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21735", - "id": "pyup.io-46840", - "more_info_path": "/vulnerabilities/CVE-2022-21735/46840", + "cve": "CVE-2021-41212", + "id": "pyup.io-46813", + "more_info_path": "/vulnerabilities/CVE-2021-41212/46813", "specs": [ "<=2.5.0" ], @@ -20938,9 +21124,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21737", - "id": "pyup.io-46842", - "more_info_path": "/vulnerabilities/CVE-2022-21737/46842", + "cve": "CVE-2021-41203", + "id": "pyup.io-46804", + "more_info_path": "/vulnerabilities/CVE-2021-41203/46804", "specs": [ "<=2.5.0" ], @@ -20948,9 +21134,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21738", - "id": "pyup.io-46843", - "more_info_path": "/vulnerabilities/CVE-2022-21738/46843", + "cve": "CVE-2021-41222", + "id": "pyup.io-46823", + "more_info_path": "/vulnerabilities/CVE-2021-41222/46823", "specs": [ "<=2.5.0" ], @@ -20958,9 +21144,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21739", - "id": "pyup.io-46844", - "more_info_path": "/vulnerabilities/CVE-2022-21739/46844", + "cve": "CVE-2021-41198", + "id": "pyup.io-46799", + "more_info_path": "/vulnerabilities/CVE-2021-41198/46799", "specs": [ "<=2.5.0" ], @@ -20968,9 +21154,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21740", - "id": "pyup.io-46845", - "more_info_path": "/vulnerabilities/CVE-2022-21740/46845", + "cve": "CVE-2021-41195", + "id": "pyup.io-46796", + "more_info_path": "/vulnerabilities/CVE-2021-41195/46796", "specs": [ "<=2.5.0" ], @@ -20978,9 +21164,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21741", - "id": "pyup.io-46846", - "more_info_path": "/vulnerabilities/CVE-2022-21741/46846", + "cve": "CVE-2021-41228", + "id": "pyup.io-46829", + "more_info_path": "/vulnerabilities/CVE-2021-41228/46829", "specs": [ "<=2.5.0" ], @@ -20988,9 +21174,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23557", - "id": "pyup.io-46847", - "more_info_path": "/vulnerabilities/CVE-2022-23557/46847", + "cve": "CVE-2021-41225", + "id": "pyup.io-46826", + "more_info_path": "/vulnerabilities/CVE-2021-41225/46826", "specs": [ "<=2.5.0" ], @@ -20998,9 +21184,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23558", - "id": "pyup.io-46848", - "more_info_path": "/vulnerabilities/CVE-2022-23558/46848", + "cve": "CVE-2020-10531", + "id": "pyup.io-46791", + "more_info_path": "/vulnerabilities/CVE-2020-10531/46791", "specs": [ "<=2.5.0" ], @@ -21008,9 +21194,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23559", - "id": "pyup.io-46849", - "more_info_path": "/vulnerabilities/CVE-2022-23559/46849", + "cve": "CVE-2021-41217", + "id": "pyup.io-46818", + "more_info_path": "/vulnerabilities/CVE-2021-41217/46818", "specs": [ "<=2.5.0" ], @@ -21018,9 +21204,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23560", - "id": "pyup.io-46850", - "more_info_path": "/vulnerabilities/CVE-2022-23560/46850", + "cve": "CVE-2021-41202", + "id": "pyup.io-46803", + "more_info_path": "/vulnerabilities/CVE-2021-41202/46803", "specs": [ "<=2.5.0" ], @@ -21028,9 +21214,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23561", - "id": "pyup.io-46851", - "more_info_path": "/vulnerabilities/CVE-2022-23561/46851", + "cve": "CVE-2021-41199", + "id": "pyup.io-46800", + "more_info_path": "/vulnerabilities/CVE-2021-41199/46800", "specs": [ "<=2.5.0" ], @@ -21038,9 +21224,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23562", - "id": "pyup.io-46852", - "more_info_path": "/vulnerabilities/CVE-2022-23562/46852", + "cve": "CVE-2021-41205", + "id": "pyup.io-46806", + "more_info_path": "/vulnerabilities/CVE-2021-41205/46806", "specs": [ "<=2.5.0" ], @@ -21048,9 +21234,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23564", - "id": "pyup.io-46854", - "more_info_path": "/vulnerabilities/CVE-2022-23564/46854", + "cve": "CVE-2021-41223", + "id": "pyup.io-46824", + "more_info_path": "/vulnerabilities/CVE-2021-41223/46824", "specs": [ "<=2.5.0" ], @@ -21058,9 +21244,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23565", - "id": "pyup.io-46855", - "more_info_path": "/vulnerabilities/CVE-2022-23565/46855", + "cve": "CVE-2021-41210", + "id": "pyup.io-46811", + "more_info_path": "/vulnerabilities/CVE-2021-41210/46811", "specs": [ "<=2.5.0" ], @@ -21068,9 +21254,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23566", - "id": "pyup.io-46856", - "more_info_path": "/vulnerabilities/CVE-2022-23566/46856", + "cve": "CVE-2021-41215", + "id": "pyup.io-46816", + "more_info_path": "/vulnerabilities/CVE-2021-41215/46816", "specs": [ "<=2.5.0" ], @@ -21078,9 +21264,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23567", - "id": "pyup.io-46857", - "more_info_path": "/vulnerabilities/CVE-2022-23567/46857", + "cve": "CVE-2021-41220", + "id": "pyup.io-46821", + "more_info_path": "/vulnerabilities/CVE-2021-41220/46821", "specs": [ "<=2.5.0" ], @@ -21088,9 +21274,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23569", - "id": "pyup.io-46859", - "more_info_path": "/vulnerabilities/CVE-2022-23569/46859", + "cve": "CVE-2021-41218", + "id": "pyup.io-46819", + "more_info_path": "/vulnerabilities/CVE-2021-41218/46819", "specs": [ "<=2.5.0" ], @@ -21098,9 +21284,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23570", - "id": "pyup.io-46860", - "more_info_path": "/vulnerabilities/CVE-2022-23570/46860", + "cve": "CVE-2022-23595", + "id": "pyup.io-46881", + "more_info_path": "/vulnerabilities/CVE-2022-23595/46881", "specs": [ "<=2.5.0" ], @@ -21108,9 +21294,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23571", - "id": "pyup.io-46861", - "more_info_path": "/vulnerabilities/CVE-2022-23571/46861", + "cve": "CVE-2021-41207", + "id": "pyup.io-46808", + "more_info_path": "/vulnerabilities/CVE-2021-41207/46808", "specs": [ "<=2.5.0" ], @@ -21118,9 +21304,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23572", - "id": "pyup.io-46862", - "more_info_path": "/vulnerabilities/CVE-2022-23572/46862", + "cve": "CVE-2021-41213", + "id": "pyup.io-46814", + "more_info_path": "/vulnerabilities/CVE-2021-41213/46814", "specs": [ "<=2.5.0" ], @@ -21128,9 +21314,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23573", - "id": "pyup.io-46863", - "more_info_path": "/vulnerabilities/CVE-2022-23573/46863", + "cve": "CVE-2022-23589", + "id": "pyup.io-46879", + "more_info_path": "/vulnerabilities/CVE-2022-23589/46879", "specs": [ "<=2.5.0" ], @@ -21138,9 +21324,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23574", - "id": "pyup.io-46864", - "more_info_path": "/vulnerabilities/CVE-2022-23574/46864", + "cve": "CVE-2022-23588", + "id": "pyup.io-46878", + "more_info_path": "/vulnerabilities/CVE-2022-23588/46878", "specs": [ "<=2.5.0" ], @@ -21148,9 +21334,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23575", - "id": "pyup.io-46865", - "more_info_path": "/vulnerabilities/CVE-2022-23575/46865", + "cve": "CVE-2022-23587", + "id": "pyup.io-46877", + "more_info_path": "/vulnerabilities/CVE-2022-23587/46877", "specs": [ "<=2.5.0" ], @@ -21158,9 +21344,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23576", - "id": "pyup.io-46866", - "more_info_path": "/vulnerabilities/CVE-2022-23576/46866", + "cve": "CVE-2021-41226", + "id": "pyup.io-46827", + "more_info_path": "/vulnerabilities/CVE-2021-41226/46827", "specs": [ "<=2.5.0" ], @@ -21168,9 +21354,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23577", - "id": "pyup.io-46867", - "more_info_path": "/vulnerabilities/CVE-2022-23577/46867", + "cve": "CVE-2021-41227", + "id": "pyup.io-46828", + "more_info_path": "/vulnerabilities/CVE-2021-41227/46828", "specs": [ "<=2.5.0" ], @@ -21178,9 +21364,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23578", - "id": "pyup.io-46868", - "more_info_path": "/vulnerabilities/CVE-2022-23578/46868", + "cve": "CVE-2022-23586", + "id": "pyup.io-46876", + "more_info_path": "/vulnerabilities/CVE-2022-23586/46876", "specs": [ "<=2.5.0" ], @@ -21188,9 +21374,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23579", - "id": "pyup.io-46869", - "more_info_path": "/vulnerabilities/CVE-2022-23579/46869", + "cve": "CVE-2022-23585", + "id": "pyup.io-46875", + "more_info_path": "/vulnerabilities/CVE-2022-23585/46875", "specs": [ "<=2.5.0" ], @@ -21198,9 +21384,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23581", - "id": "pyup.io-46871", - "more_info_path": "/vulnerabilities/CVE-2022-23581/46871", + "cve": "CVE-2022-23584", + "id": "pyup.io-46874", + "more_info_path": "/vulnerabilities/CVE-2022-23584/46874", "specs": [ "<=2.5.0" ], @@ -21208,9 +21394,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23582", - "id": "pyup.io-46872", - "more_info_path": "/vulnerabilities/CVE-2022-23582/46872", + "cve": "CVE-2021-41214", + "id": "pyup.io-46815", + "more_info_path": "/vulnerabilities/CVE-2021-41214/46815", "specs": [ "<=2.5.0" ], @@ -21218,9 +21404,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23584", - "id": "pyup.io-46874", - "more_info_path": "/vulnerabilities/CVE-2022-23584/46874", + "cve": "CVE-2022-23582", + "id": "pyup.io-46872", + "more_info_path": "/vulnerabilities/CVE-2022-23582/46872", "specs": [ "<=2.5.0" ], @@ -21228,9 +21414,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23585", - "id": "pyup.io-46875", - "more_info_path": "/vulnerabilities/CVE-2022-23585/46875", + "cve": "CVE-2021-41204", + "id": "pyup.io-46805", + "more_info_path": "/vulnerabilities/CVE-2021-41204/46805", "specs": [ "<=2.5.0" ], @@ -21238,9 +21424,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23586", - "id": "pyup.io-46876", - "more_info_path": "/vulnerabilities/CVE-2022-23586/46876", + "cve": "CVE-2022-23581", + "id": "pyup.io-46871", + "more_info_path": "/vulnerabilities/CVE-2022-23581/46871", "specs": [ "<=2.5.0" ], @@ -21248,9 +21434,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23588", - "id": "pyup.io-46878", - "more_info_path": "/vulnerabilities/CVE-2022-23588/46878", + "cve": "CVE-2022-23580", + "id": "pyup.io-46870", + "more_info_path": "/vulnerabilities/CVE-2022-23580/46870", "specs": [ "<=2.5.0" ], @@ -21258,9 +21444,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41202", - "id": "pyup.io-46803", - "more_info_path": "/vulnerabilities/CVE-2021-41202/46803", + "cve": "CVE-2021-41201", + "id": "pyup.io-46802", + "more_info_path": "/vulnerabilities/CVE-2021-41201/46802", "specs": [ "<=2.5.0" ], @@ -21268,9 +21454,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41211", - "id": "pyup.io-46812", - "more_info_path": "/vulnerabilities/CVE-2021-41211/46812", + "cve": "CVE-2022-23579", + "id": "pyup.io-46869", + "more_info_path": "/vulnerabilities/CVE-2022-23579/46869", "specs": [ "<=2.5.0" ], @@ -21278,9 +21464,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41218", - "id": "pyup.io-46819", - "more_info_path": "/vulnerabilities/CVE-2021-41218/46819", + "cve": "CVE-2022-23578", + "id": "pyup.io-46868", + "more_info_path": "/vulnerabilities/CVE-2022-23578/46868", "specs": [ "<=2.5.0" ], @@ -21288,9 +21474,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23591", - "id": "pyup.io-46880", - "more_info_path": "/vulnerabilities/CVE-2022-23591/46880", + "cve": "CVE-2022-23577", + "id": "pyup.io-46867", + "more_info_path": "/vulnerabilities/CVE-2022-23577/46867", "specs": [ "<=2.5.0" ], @@ -21298,9 +21484,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-21725", - "id": "pyup.io-46830", - "more_info_path": "/vulnerabilities/CVE-2022-21725/46830", + "cve": "CVE-2021-41197", + "id": "pyup.io-46798", + "more_info_path": "/vulnerabilities/CVE-2021-41197/46798", "specs": [ "<=2.5.0" ], @@ -21308,9 +21494,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41195", - "id": "pyup.io-46796", - "more_info_path": "/vulnerabilities/CVE-2021-41195/46796", + "cve": "CVE-2022-23576", + "id": "pyup.io-46866", + "more_info_path": "/vulnerabilities/CVE-2022-23576/46866", "specs": [ "<=2.5.0" ], @@ -21318,9 +21504,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41196", - "id": "pyup.io-46797", - "more_info_path": "/vulnerabilities/CVE-2021-41196/46797", + "cve": "CVE-2022-23574", + "id": "pyup.io-46864", + "more_info_path": "/vulnerabilities/CVE-2022-23574/46864", "specs": [ "<=2.5.0" ], @@ -21328,9 +21514,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41197", - "id": "pyup.io-46798", - "more_info_path": "/vulnerabilities/CVE-2021-41197/46798", + "cve": "CVE-2022-23572", + "id": "pyup.io-46862", + "more_info_path": "/vulnerabilities/CVE-2022-23572/46862", "specs": [ "<=2.5.0" ], @@ -21338,9 +21524,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41200", - "id": "pyup.io-46801", - "more_info_path": "/vulnerabilities/CVE-2021-41200/46801", + "cve": "CVE-2022-23571", + "id": "pyup.io-46861", + "more_info_path": "/vulnerabilities/CVE-2022-23571/46861", "specs": [ "<=2.5.0" ], @@ -21348,9 +21534,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41198", - "id": "pyup.io-46799", - "more_info_path": "/vulnerabilities/CVE-2021-41198/46799", + "cve": "CVE-2022-23570", + "id": "pyup.io-46860", + "more_info_path": "/vulnerabilities/CVE-2022-23570/46860", "specs": [ "<=2.5.0" ], @@ -21358,9 +21544,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41199", - "id": "pyup.io-46800", - "more_info_path": "/vulnerabilities/CVE-2021-41199/46800", + "cve": "CVE-2022-23566", + "id": "pyup.io-46856", + "more_info_path": "/vulnerabilities/CVE-2022-23566/46856", "specs": [ "<=2.5.0" ], @@ -21368,9 +21554,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41201", - "id": "pyup.io-46802", - "more_info_path": "/vulnerabilities/CVE-2021-41201/46802", + "cve": "CVE-2022-23564", + "id": "pyup.io-46854", + "more_info_path": "/vulnerabilities/CVE-2022-23564/46854", "specs": [ "<=2.5.0" ], @@ -21378,9 +21564,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41204", - "id": "pyup.io-46805", - "more_info_path": "/vulnerabilities/CVE-2021-41204/46805", + "cve": "CVE-2022-23561", + "id": "pyup.io-46851", + "more_info_path": "/vulnerabilities/CVE-2022-23561/46851", "specs": [ "<=2.5.0" ], @@ -21388,9 +21574,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41203", - "id": "pyup.io-46804", - "more_info_path": "/vulnerabilities/CVE-2021-41203/46804", + "cve": "CVE-2022-23560", + "id": "pyup.io-46850", + "more_info_path": "/vulnerabilities/CVE-2022-23560/46850", "specs": [ "<=2.5.0" ], @@ -21398,9 +21584,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41205", - "id": "pyup.io-46806", - "more_info_path": "/vulnerabilities/CVE-2021-41205/46806", + "cve": "CVE-2022-23558", + "id": "pyup.io-46848", + "more_info_path": "/vulnerabilities/CVE-2022-23558/46848", "specs": [ "<=2.5.0" ], @@ -21408,9 +21594,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41206", - "id": "pyup.io-46807", - "more_info_path": "/vulnerabilities/CVE-2021-41206/46807", + "cve": "CVE-2022-23557", + "id": "pyup.io-46847", + "more_info_path": "/vulnerabilities/CVE-2022-23557/46847", "specs": [ "<=2.5.0" ], @@ -21418,9 +21604,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41207", - "id": "pyup.io-46808", - "more_info_path": "/vulnerabilities/CVE-2021-41207/46808", + "cve": "CVE-2022-21741", + "id": "pyup.io-46846", + "more_info_path": "/vulnerabilities/CVE-2022-21741/46846", "specs": [ "<=2.5.0" ], @@ -21428,9 +21614,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41208", - "id": "pyup.io-46809", - "more_info_path": "/vulnerabilities/CVE-2021-41208/46809", + "cve": "CVE-2022-21740", + "id": "pyup.io-46845", + "more_info_path": "/vulnerabilities/CVE-2022-21740/46845", "specs": [ "<=2.5.0" ], @@ -21438,9 +21624,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41209", - "id": "pyup.io-46810", - "more_info_path": "/vulnerabilities/CVE-2021-41209/46810", + "cve": "CVE-2022-21739", + "id": "pyup.io-46844", + "more_info_path": "/vulnerabilities/CVE-2022-21739/46844", "specs": [ "<=2.5.0" ], @@ -21448,9 +21634,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41210", - "id": "pyup.io-46811", - "more_info_path": "/vulnerabilities/CVE-2021-41210/46811", + "cve": "CVE-2022-21738", + "id": "pyup.io-46843", + "more_info_path": "/vulnerabilities/CVE-2022-21738/46843", "specs": [ "<=2.5.0" ], @@ -21458,9 +21644,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41212", - "id": "pyup.io-46813", - "more_info_path": "/vulnerabilities/CVE-2021-41212/46813", + "cve": "CVE-2022-23569", + "id": "pyup.io-46859", + "more_info_path": "/vulnerabilities/CVE-2022-23569/46859", "specs": [ "<=2.5.0" ], @@ -21468,9 +21654,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41215", - "id": "pyup.io-46816", - "more_info_path": "/vulnerabilities/CVE-2021-41215/46816", + "cve": "CVE-2022-21735", + "id": "pyup.io-46840", + "more_info_path": "/vulnerabilities/CVE-2022-21735/46840", "specs": [ "<=2.5.0" ], @@ -21478,9 +21664,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41216", - "id": "pyup.io-46817", - "more_info_path": "/vulnerabilities/CVE-2021-41216/46817", + "cve": "CVE-2022-21729", + "id": "pyup.io-46834", + "more_info_path": "/vulnerabilities/CVE-2022-21729/46834", "specs": [ "<=2.5.0" ], @@ -21488,9 +21674,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-41217", - "id": "pyup.io-46818", - "more_info_path": "/vulnerabilities/CVE-2021-41217/46818", + "cve": "CVE-2022-23568", + "id": "pyup.io-46858", + "more_info_path": "/vulnerabilities/CVE-2022-23568/46858", "specs": [ "<=2.5.0" ], @@ -21498,9 +21684,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23563", - "id": "pyup.io-46853", - "more_info_path": "/vulnerabilities/CVE-2022-23563/46853", + "cve": "CVE-2022-23567", + "id": "pyup.io-46857", + "more_info_path": "/vulnerabilities/CVE-2022-23567/46857", "specs": [ "<=2.5.0" ], @@ -21508,9 +21694,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23583", - "id": "pyup.io-46873", - "more_info_path": "/vulnerabilities/CVE-2022-23583/46873", + "cve": "CVE-2022-21736", + "id": "pyup.io-46841", + "more_info_path": "/vulnerabilities/CVE-2022-21736/46841", "specs": [ "<=2.5.0" ], @@ -21518,9 +21704,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2022-23580", - "id": "pyup.io-46870", - "more_info_path": "/vulnerabilities/CVE-2022-23580/46870", + "cve": "CVE-2022-21733", + "id": "pyup.io-46838", + "more_info_path": "/vulnerabilities/CVE-2022-21733/46838", "specs": [ "<=2.5.0" ], @@ -21528,9 +21714,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-22924", - "id": "pyup.io-46794", - "more_info_path": "/vulnerabilities/CVE-2021-22924/46794", + "cve": "CVE-2022-21732", + "id": "pyup.io-46837", + "more_info_path": "/vulnerabilities/CVE-2022-21732/46837", "specs": [ "<=2.5.0" ], @@ -21538,9 +21724,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-22923", - "id": "pyup.io-46793", - "more_info_path": "/vulnerabilities/CVE-2021-22923/46793", + "cve": "CVE-2022-21730", + "id": "pyup.io-46835", + "more_info_path": "/vulnerabilities/CVE-2022-21730/46835", "specs": [ "<=2.5.0" ], @@ -21548,9 +21734,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2020-10531", - "id": "pyup.io-46791", - "more_info_path": "/vulnerabilities/CVE-2020-10531/46791", + "cve": "CVE-2022-21728", + "id": "pyup.io-46833", + "more_info_path": "/vulnerabilities/CVE-2022-21728/46833", "specs": [ "<=2.5.0" ], @@ -21558,9 +21744,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-22925", - "id": "pyup.io-46795", - "more_info_path": "/vulnerabilities/CVE-2021-22925/46795", + "cve": "CVE-2022-21727", + "id": "pyup.io-46832", + "more_info_path": "/vulnerabilities/CVE-2022-21727/46832", "specs": [ "<=2.5.0" ], @@ -21568,9 +21754,9 @@ }, { "advisory": "Chia versions 2.5.0 and prior require as minimum dependency TensorFlow v2.6.0 or lower, that have several known vulnerabilities.", - "cve": "CVE-2021-22922", - "id": "pyup.io-46792", - "more_info_path": "/vulnerabilities/CVE-2021-22922/46792", + "cve": "CVE-2022-21726", + "id": "pyup.io-46831", + "more_info_path": "/vulnerabilities/CVE-2022-21726/46831", "specs": [ "<=2.5.0" ], @@ -21589,20 +21775,20 @@ "v": "<1.0b19" }, { - "advisory": "Chia-blockchain 1.0b27 updates its GUI to handle CVE-2020-28477.\r\nhttps://github.com/Chia-Network/chia-blockchain/commit/45c85c0030a9b07bd3d07fc0e7f7afc540b53009", - "cve": "CVE-2020-28477", - "id": "pyup.io-42341", - "more_info_path": "/vulnerabilities/CVE-2020-28477/42341", + "advisory": "Chia-blockchain 1.0b27 updates its dependency 'pyyaml' to v5.4.1 to include a security fix.\r\nhttps://github.com/Chia-Network/chia-blockchain/commit/c3eae20b877a85eface0d4043abb5777fad3acf4", + "cve": "CVE-2020-14343", + "id": "pyup.io-42367", + "more_info_path": "/vulnerabilities/CVE-2020-14343/42367", "specs": [ "<1.0b27" ], "v": "<1.0b27" }, { - "advisory": "Chia-blockchain 1.0b27 updates its dependency 'pyyaml' to v5.4.1 to include a security fix.\r\nhttps://github.com/Chia-Network/chia-blockchain/commit/c3eae20b877a85eface0d4043abb5777fad3acf4", - "cve": "CVE-2020-14343", - "id": "pyup.io-42367", - "more_info_path": "/vulnerabilities/CVE-2020-14343/42367", + "advisory": "Chia-blockchain 1.0b27 updates its GUI to handle CVE-2020-28477.\r\nhttps://github.com/Chia-Network/chia-blockchain/commit/45c85c0030a9b07bd3d07fc0e7f7afc540b53009", + "cve": "CVE-2020-28477", + "id": "pyup.io-42341", + "more_info_path": "/vulnerabilities/CVE-2020-28477/42341", "specs": [ "<1.0b27" ], @@ -21700,9 +21886,9 @@ }, { "advisory": "Chia-blockchain 1.8.1rc4 updates its NPM dependency 'Electron' to 22.3.7 to include security fixes.", - "cve": "CVE-2023-2133", - "id": "pyup.io-64104", - "more_info_path": "/vulnerabilities/CVE-2023-2133/64104", + "cve": "CVE-2023-2033", + "id": "pyup.io-63738", + "more_info_path": "/vulnerabilities/CVE-2023-2033/63738", "specs": [ "<1.8.1rc4" ], @@ -21710,9 +21896,9 @@ }, { "advisory": "Chia-blockchain 1.8.1rc4 updates its NPM dependency 'Electron' to 22.3.7 to include security fixes.", - "cve": "CVE-2023-2033", - "id": "pyup.io-63738", - "more_info_path": "/vulnerabilities/CVE-2023-2033/63738", + "cve": "CVE-2023-2133", + "id": "pyup.io-64104", + "more_info_path": "/vulnerabilities/CVE-2023-2133/64104", "specs": [ "<1.8.1rc4" ], @@ -21750,9 +21936,9 @@ }, { "advisory": "Chia-blockchain 2.0.0rc4 updates its NPM dependency 'Electron' to 25.4.0 to include security fixes.\r\nhttps://github.com/Chia-Network/chia-blockchain-gui/pull/1976", - "cve": "CVE-2023-3728", - "id": "pyup.io-64108", - "more_info_path": "/vulnerabilities/CVE-2023-3728/64108", + "cve": "CVE-2023-3730", + "id": "pyup.io-64109", + "more_info_path": "/vulnerabilities/CVE-2023-3730/64109", "specs": [ "<2.0.0rc4" ], @@ -21760,9 +21946,9 @@ }, { "advisory": "Chia-blockchain 2.0.0rc4 updates its NPM dependency 'Electron' to 25.4.0 to include security fixes.\r\nhttps://github.com/Chia-Network/chia-blockchain-gui/pull/1976", - "cve": "CVE-2023-3730", - "id": "pyup.io-64109", - "more_info_path": "/vulnerabilities/CVE-2023-3730/64109", + "cve": "CVE-2023-3728", + "id": "pyup.io-64108", + "more_info_path": "/vulnerabilities/CVE-2023-3728/64108", "specs": [ "<2.0.0rc4" ], @@ -22764,9 +22950,9 @@ "cloudvision": [ { "advisory": "Cloudvision 1.13.0 updates 'cryptography' minimum version to v41.0.3 to include security fixes in bundled OpenSSL.", - "cve": "CVE-2023-3446", - "id": "pyup.io-61131", - "more_info_path": "/vulnerabilities/CVE-2023-3446/61131", + "cve": "CVE-2023-2975", + "id": "pyup.io-61130", + "more_info_path": "/vulnerabilities/CVE-2023-2975/61130", "specs": [ "<1.13.0" ], @@ -22784,9 +22970,9 @@ }, { "advisory": "Cloudvision 1.13.0 updates 'cryptography' minimum version to v41.0.3 to include security fixes in bundled OpenSSL.", - "cve": "CVE-2023-2975", - "id": "pyup.io-61130", - "more_info_path": "/vulnerabilities/CVE-2023-2975/61130", + "cve": "CVE-2023-3446", + "id": "pyup.io-61131", + "more_info_path": "/vulnerabilities/CVE-2023-3446/61131", "specs": [ "<1.13.0" ], @@ -23008,20 +23194,20 @@ "v": "<2.6.0" }, { - "advisory": "Cobbler version Verified as present in Cobbler versions 2.6.11+, but code inspection suggests at least 2.0.0+ or possibly even older versions may be vulnerable contains a Cross Site Scripting (XSS) vulnerability in cobbler-web that can result in Privilege escalation to admin.. This attack appear to be exploitable via \"network connectivity\". Sending unauthenticated JavaScript payload to the Cobbler XMLRPC API (/cobbler_api).", - "cve": "CVE-2018-1000225", - "id": "pyup.io-67945", - "more_info_path": "/vulnerabilities/CVE-2018-1000225/67945", + "advisory": "Cobbler version Verified as present in Cobbler versions 2.6.11+, but code inspection suggests at least 2.0.0+ or possibly even older versions may be vulnerable contains a Incorrect Access Control vulnerability in XMLRPC API (/cobbler_api) that can result in Privilege escalation, data manipulation or exfiltration, LDAP credential harvesting. This attack appear to be exploitable via \"network connectivity\". Taking advantage of improper validation of security tokens in API endpoints. Please note this is a different issue than CVE-2018-10931.", + "cve": "CVE-2018-1000226", + "id": "pyup.io-65837", + "more_info_path": "/vulnerabilities/CVE-2018-1000226/65837", "specs": [ "<3.0.0" ], "v": "<3.0.0" }, { - "advisory": "Cobbler version Verified as present in Cobbler versions 2.6.11+, but code inspection suggests at least 2.0.0+ or possibly even older versions may be vulnerable contains a Incorrect Access Control vulnerability in XMLRPC API (/cobbler_api) that can result in Privilege escalation, data manipulation or exfiltration, LDAP credential harvesting. This attack appear to be exploitable via \"network connectivity\". Taking advantage of improper validation of security tokens in API endpoints. Please note this is a different issue than CVE-2018-10931.", - "cve": "CVE-2018-1000226", - "id": "pyup.io-65837", - "more_info_path": "/vulnerabilities/CVE-2018-1000226/65837", + "advisory": "Cobbler version Verified as present in Cobbler versions 2.6.11+, but code inspection suggests at least 2.0.0+ or possibly even older versions may be vulnerable contains a Cross Site Scripting (XSS) vulnerability in cobbler-web that can result in Privilege escalation to admin.. This attack appear to be exploitable via \"network connectivity\". Sending unauthenticated JavaScript payload to the Cobbler XMLRPC API (/cobbler_api).", + "cve": "CVE-2018-1000225", + "id": "pyup.io-67945", + "more_info_path": "/vulnerabilities/CVE-2018-1000225/67945", "specs": [ "<3.0.0" ], @@ -23068,50 +23254,50 @@ "v": "<3.3.0" }, { - "advisory": "Cobbler 3.3.1 removes testing module, which was shipping a well known username and password combination.\r\nhttps://github.com/cobbler/cobbler/pull/2908", - "cve": "PVE-2022-45320", - "id": "pyup.io-45320", - "more_info_path": "/vulnerabilities/PVE-2022-45320/45320", + "advisory": "Cobbler 3.3.1 includes a fix for CVE-2021-45083: Files in /etc/cobbler are world readable. Two of those files contain some sensitive information that can be exposed to a local user who has non-privileged access to the server. The users.digest file contains the sha2-512 digest of users in a Cobbler local installation. In the case of an easy-to-guess password, it's trivial to obtain the plaintext string. The settings.yaml file contains secrets such as the hashed default password.", + "cve": "CVE-2021-45083", + "id": "pyup.io-45317", + "more_info_path": "/vulnerabilities/CVE-2021-45083/45317", "specs": [ "<3.3.1" ], "v": "<3.3.1" }, { - "advisory": "Cobbler 3.3.1 validates the data before logging it to avoid log file pollution.\r\nhttps://github.com/cobbler/cobbler/pull/2911", - "cve": "PVE-2022-45319", - "id": "pyup.io-45319", - "more_info_path": "/vulnerabilities/PVE-2022-45319/45319", + "advisory": "An issue was discovered in Cobbler before 3.3.1. In the templar.py file, the function check_for_invalid_imports can allow Cheetah code to import Python modules via the \"#from MODULE import\" substring. (Only lines beginning with #import are blocked.)", + "cve": "CVE-2021-45082", + "id": "pyup.io-45286", + "more_info_path": "/vulnerabilities/CVE-2021-45082/45286", "specs": [ "<3.3.1" ], "v": "<3.3.1" }, { - "advisory": "Cobbler 3.3.1 includes a fix for CVE-2021-45083: Files in /etc/cobbler are world readable. Two of those files contain some sensitive information that can be exposed to a local user who has non-privileged access to the server. The users.digest file contains the sha2-512 digest of users in a Cobbler local installation. In the case of an easy-to-guess password, it's trivial to obtain the plaintext string. The settings.yaml file contains secrets such as the hashed default password.", - "cve": "CVE-2021-45083", - "id": "pyup.io-45317", - "more_info_path": "/vulnerabilities/CVE-2021-45083/45317", + "advisory": "Cobbler 3.3.1 removes testing module, which was shipping a well known username and password combination.\r\nhttps://github.com/cobbler/cobbler/pull/2908", + "cve": "PVE-2022-45320", + "id": "pyup.io-45320", + "more_info_path": "/vulnerabilities/PVE-2022-45320/45320", "specs": [ "<3.3.1" ], "v": "<3.3.1" }, { - "advisory": "Cobbler 3.3.1 stabalizes the MongoDB serializer. In mongodb serializer class, when the config file is read, there is no sanity check. If the file get somewhat corrupted, it can lead to unexpected behaviour.\r\nhttps://github.com/cobbler/cobbler/pull/2919", - "cve": "PVE-2022-45318", - "id": "pyup.io-45318", - "more_info_path": "/vulnerabilities/PVE-2022-45318/45318", + "advisory": "Cobbler 3.3.1 validates the data before logging it to avoid log file pollution.\r\nhttps://github.com/cobbler/cobbler/pull/2911", + "cve": "PVE-2022-45319", + "id": "pyup.io-45319", + "more_info_path": "/vulnerabilities/PVE-2022-45319/45319", "specs": [ "<3.3.1" ], "v": "<3.3.1" }, { - "advisory": "An issue was discovered in Cobbler before 3.3.1. In the templar.py file, the function check_for_invalid_imports can allow Cheetah code to import Python modules via the \"#from MODULE import\" substring. (Only lines beginning with #import are blocked.)", - "cve": "CVE-2021-45082", - "id": "pyup.io-45286", - "more_info_path": "/vulnerabilities/CVE-2021-45082/45286", + "advisory": "Cobbler 3.3.1 stabalizes the MongoDB serializer. In mongodb serializer class, when the config file is read, there is no sanity check. If the file get somewhat corrupted, it can lead to unexpected behaviour.\r\nhttps://github.com/cobbler/cobbler/pull/2919", + "cve": "PVE-2022-45318", + "id": "pyup.io-45318", + "more_info_path": "/vulnerabilities/PVE-2022-45318/45318", "specs": [ "<3.3.1" ], @@ -23196,6 +23382,17 @@ ">=2.6.0,<3.0.0" ], "v": ">=2.6.0,<3.0.0" + }, + { + "advisory": "Affected versions of Cobbler are vulnerable to Improper Authentication. Anyone can connect to cobbler XML-RPC server with known password and make changes.", + "cve": "CVE-2024-47533", + "id": "pyup.io-74187", + "more_info_path": "/vulnerabilities/CVE-2024-47533/74187", + "specs": [ + ">=3.0.0,<3.2.3", + ">=3.3.0,<3.3.7" + ], + "v": ">=3.0.0,<3.2.3,>=3.3.0,<3.3.7" } ], "cockroachdb": [ @@ -23902,20 +24099,20 @@ "v": "<2.4.0" }, { - "advisory": "Compliance-trestle 2.5.0 updates its cryptography dependency to version 41.0.6 due to a critical vulnerability (CVE-2023-49083). This vulnerability could lead to a NULL-pointer dereference and segfault when deserializing a PKCS7 blob/certificate, potentially causing a Denial of Service (DoS) and system instability.\r\nhttps://github.com/oscal-compass/compliance-trestle/pull/1479/commits/1ed9f5ccec1e37f625eb9253dd07f8dee994cfe4", - "cve": "CVE-2023-49083", - "id": "pyup.io-63242", - "more_info_path": "/vulnerabilities/CVE-2023-49083/63242", + "advisory": "Compliance-trestle 2.5.0 updates its cryptography dependency to version 41.0.6 due to a vulnerability (CVE-2023-48795). This vulnerability, known as the Terrapin attack, allows remote attackers to bypass integrity checks, potentially downgrading or disabling some security features in the SSH transport protocol.\r\nhttps://github.com/oscal-compass/compliance-trestle/pull/1486/commits/5657b72a757b094777773b5e1d7849ce3b970dd1", + "cve": "CVE-2023-48795", + "id": "pyup.io-63247", + "more_info_path": "/vulnerabilities/CVE-2023-48795/63247", "specs": [ "<2.5.0" ], "v": "<2.5.0" }, { - "advisory": "Compliance-trestle 2.5.0 updates its cryptography dependency to version 41.0.6 due to a vulnerability (CVE-2023-48795). This vulnerability, known as the Terrapin attack, allows remote attackers to bypass integrity checks, potentially downgrading or disabling some security features in the SSH transport protocol.\r\nhttps://github.com/oscal-compass/compliance-trestle/pull/1486/commits/5657b72a757b094777773b5e1d7849ce3b970dd1", - "cve": "CVE-2023-48795", - "id": "pyup.io-63247", - "more_info_path": "/vulnerabilities/CVE-2023-48795/63247", + "advisory": "Compliance-trestle 2.5.0 updates its cryptography dependency to version 41.0.6 due to a critical vulnerability (CVE-2023-49083). This vulnerability could lead to a NULL-pointer dereference and segfault when deserializing a PKCS7 blob/certificate, potentially causing a Denial of Service (DoS) and system instability.\r\nhttps://github.com/oscal-compass/compliance-trestle/pull/1479/commits/1ed9f5ccec1e37f625eb9253dd07f8dee994cfe4", + "cve": "CVE-2023-49083", + "id": "pyup.io-63242", + "more_info_path": "/vulnerabilities/CVE-2023-49083/63242", "specs": [ "<2.5.0" ], @@ -23965,9 +24162,9 @@ "composer": [ { "advisory": "Composer 0.13.0 updates its dependency 'pillow' to v9.0.0 in Dockerfile to include security fixes.\r\nhttps://github.com/mosaicml/composer/pull/2007", - "cve": "PVE-2021-44525", - "id": "pyup.io-53693", - "more_info_path": "/vulnerabilities/PVE-2021-44525/53693", + "cve": "CVE-2021-34552", + "id": "pyup.io-53694", + "more_info_path": "/vulnerabilities/CVE-2021-34552/53694", "specs": [ "<0.13.0" ], @@ -23984,10 +24181,10 @@ "v": "<0.13.0" }, { - "advisory": "Composer 0.13.0 updates its dependency 'pillow' to v9.0.0 in Dockerfile to include security fixes.\r\nhttps://github.com/mosaicml/composer/pull/2007", - "cve": "CVE-2021-34552", - "id": "pyup.io-53694", - "more_info_path": "/vulnerabilities/CVE-2021-34552/53694", + "advisory": "Composer 0.13.0 updates its dependency 'certifi' requirement to '>=2022.12.7' in Dockerfile to include a security fix.\r\nhttps://github.com/mosaicml/composer/pull/2007", + "cve": "CVE-2022-23491", + "id": "pyup.io-53695", + "more_info_path": "/vulnerabilities/CVE-2022-23491/53695", "specs": [ "<0.13.0" ], @@ -23995,19 +24192,19 @@ }, { "advisory": "Composer 0.13.0 updates its dependency 'pillow' to v9.0.0 in Dockerfile to include security fixes.\r\nhttps://github.com/mosaicml/composer/pull/2007", - "cve": "PVE-2022-44524", - "id": "pyup.io-53692", - "more_info_path": "/vulnerabilities/PVE-2022-44524/53692", + "cve": "PVE-2021-44525", + "id": "pyup.io-53693", + "more_info_path": "/vulnerabilities/PVE-2021-44525/53693", "specs": [ "<0.13.0" ], "v": "<0.13.0" }, { - "advisory": "Composer 0.13.0 updates its dependency 'certifi' requirement to '>=2022.12.7' in Dockerfile to include a security fix.\r\nhttps://github.com/mosaicml/composer/pull/2007", - "cve": "CVE-2022-23491", - "id": "pyup.io-53695", - "more_info_path": "/vulnerabilities/CVE-2022-23491/53695", + "advisory": "Composer 0.13.0 updates its dependency 'pillow' to v9.0.0 in Dockerfile to include security fixes.\r\nhttps://github.com/mosaicml/composer/pull/2007", + "cve": "PVE-2022-44524", + "id": "pyup.io-53692", + "more_info_path": "/vulnerabilities/PVE-2022-44524/53692", "specs": [ "<0.13.0" ], @@ -24459,6 +24656,16 @@ } ], "connect-sdk-python2": [ + { + "advisory": "Connect-sdk-python2 3.33.0 updates the minimum 'requests' version from 2.20.0 to 2.25.0, as earlier versions depend on a vulnerable 'urllib3' version.", + "cve": "CVE-2021-33503", + "id": "pyup.io-51387", + "more_info_path": "/vulnerabilities/CVE-2021-33503/51387", + "specs": [ + "<3.33.0" + ], + "v": "<3.33.0" + }, { "advisory": "Connect-sdk-python2 3.33.0 updates the minimum 'requests' version from 2.20.0 to 2.25.0, as earlier versions depend on a vulnerable 'urllib3' version.", "cve": "CVE-2020-26137", @@ -24498,16 +24705,6 @@ "<3.33.0" ], "v": "<3.33.0" - }, - { - "advisory": "Connect-sdk-python2 3.33.0 updates the minimum 'requests' version from 2.20.0 to 2.25.0, as earlier versions depend on a vulnerable 'urllib3' version.", - "cve": "CVE-2021-33503", - "id": "pyup.io-51387", - "more_info_path": "/vulnerabilities/CVE-2021-33503/51387", - "specs": [ - "<3.33.0" - ], - "v": "<3.33.0" } ], "connect-sdk-python3": [ @@ -24834,20 +25031,20 @@ "v": "<1.2.8" }, { - "advisory": "Copyparty 1.8.2 includes a fix for a Path Traversal vulnerability: An attacker may use the /.cpr endpoint to have full access to the server filesystem.\r\nhttps://github.com/9001/copyparty/commit/043e3c7dd683113e2b1c15cacb9c8e68f76513ff\r\nhttps://github.com/9001/copyparty/security/advisories/GHSA-pxfv-7rr3-2qjg", - "cve": "CVE-2023-37474", - "id": "pyup.io-59466", - "more_info_path": "/vulnerabilities/CVE-2023-37474/59466", + "advisory": "Copyparty 1.8.2 includes a fix for a Race Condition vulnerability. Impact is on availability.\r\nhttps://github.com/9001/copyparty/commit/77f1e5144455eb946db7368792ea11c934f0f6da\r\nhttps://github.com/9001/copyparty/commit/8f59afb1593a75b8ce8c91ceee304097a07aea6e", + "cve": "PVE-2023-59475", + "id": "pyup.io-59475", + "more_info_path": "/vulnerabilities/PVE-2023-59475/59475", "specs": [ "<1.8.2" ], "v": "<1.8.2" }, { - "advisory": "Copyparty 1.8.2 includes a fix for a Race Condition vulnerability. Impact is on availability.\r\nhttps://github.com/9001/copyparty/commit/77f1e5144455eb946db7368792ea11c934f0f6da\r\nhttps://github.com/9001/copyparty/commit/8f59afb1593a75b8ce8c91ceee304097a07aea6e", - "cve": "PVE-2023-59475", - "id": "pyup.io-59475", - "more_info_path": "/vulnerabilities/PVE-2023-59475/59475", + "advisory": "Copyparty 1.8.2 includes a fix for a Path Traversal vulnerability: An attacker may use the /.cpr endpoint to have full access to the server filesystem.\r\nhttps://github.com/9001/copyparty/commit/043e3c7dd683113e2b1c15cacb9c8e68f76513ff\r\nhttps://github.com/9001/copyparty/security/advisories/GHSA-pxfv-7rr3-2qjg", + "cve": "CVE-2023-37474", + "id": "pyup.io-59466", + "more_info_path": "/vulnerabilities/CVE-2023-37474/59466", "specs": [ "<1.8.2" ], @@ -25055,11 +25252,21 @@ } ], "crate-docs-theme": [ + { + "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'jquery' to v3.5.1 to include security fixes.", + "cve": "CVE-2012-6708", + "id": "pyup.io-49056", + "more_info_path": "/vulnerabilities/CVE-2012-6708/49056", + "specs": [ + "<0.13.0" + ], + "v": "<0.13.0" + }, { "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'bootstrap' to v4.5.3 to include security fixes.", - "cve": "CVE-2018-14040", - "id": "pyup.io-49066", - "more_info_path": "/vulnerabilities/CVE-2018-14040/49066", + "cve": "CVE-2016-10735", + "id": "pyup.io-49068", + "more_info_path": "/vulnerabilities/CVE-2016-10735/49068", "specs": [ "<0.13.0" ], @@ -25067,9 +25274,9 @@ }, { "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'jquery' to v3.5.1 to include security fixes.", - "cve": "CVE-2019-11358", - "id": "pyup.io-49060", - "more_info_path": "/vulnerabilities/CVE-2019-11358/49060", + "cve": "CVE-2012-6708", + "id": "pyup.io-49057", + "more_info_path": "/vulnerabilities/CVE-2012-6708/49057", "specs": [ "<0.13.0" ], @@ -25086,10 +25293,10 @@ "v": "<0.13.0" }, { - "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'bootstrap' to v4.5.3 to include security fixes.", - "cve": "CVE-2018-14042", - "id": "pyup.io-49067", - "more_info_path": "/vulnerabilities/CVE-2018-14042/49067", + "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'jquery' to v3.5.1 to include security fixes.", + "cve": "CVE-2015-9251", + "id": "pyup.io-49059", + "more_info_path": "/vulnerabilities/CVE-2015-9251/49059", "specs": [ "<0.13.0" ], @@ -25097,9 +25304,9 @@ }, { "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'bootstrap' to v4.5.3 to include security fixes.", - "cve": "CVE-2018-20677", - "id": "pyup.io-49064", - "more_info_path": "/vulnerabilities/CVE-2018-20677/49064", + "cve": "CVE-2018-14040", + "id": "pyup.io-49066", + "more_info_path": "/vulnerabilities/CVE-2018-14040/49066", "specs": [ "<0.13.0" ], @@ -25107,39 +25314,39 @@ }, { "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'jquery' to v3.5.1 to include security fixes.", - "cve": "CVE-2020-7656", - "id": "pyup.io-49062", - "more_info_path": "/vulnerabilities/CVE-2020-7656/49062", + "cve": "CVE-2019-11358", + "id": "pyup.io-49060", + "more_info_path": "/vulnerabilities/CVE-2019-11358/49060", "specs": [ "<0.13.0" ], "v": "<0.13.0" }, { - "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'bootstrap' to v4.5.3 to include security fixes.", - "cve": "CVE-2016-10735", - "id": "pyup.io-49068", - "more_info_path": "/vulnerabilities/CVE-2016-10735/49068", + "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'jquery' to v3.5.1 to include security fixes.", + "cve": "CVE-2020-7656", + "id": "pyup.io-49062", + "more_info_path": "/vulnerabilities/CVE-2020-7656/49062", "specs": [ "<0.13.0" ], "v": "<0.13.0" }, { - "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'jquery' to v3.5.1 to include security fixes.", - "cve": "CVE-2015-9251", - "id": "pyup.io-49058", - "more_info_path": "/vulnerabilities/CVE-2015-9251/49058", + "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'bootstrap' to v4.5.3 to include security fixes.", + "cve": "CVE-2018-14042", + "id": "pyup.io-49067", + "more_info_path": "/vulnerabilities/CVE-2018-14042/49067", "specs": [ "<0.13.0" ], "v": "<0.13.0" }, { - "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'jquery' to v3.5.1 to include security fixes.", - "cve": "CVE-2011-4969", - "id": "pyup.io-39529", - "more_info_path": "/vulnerabilities/CVE-2011-4969/39529", + "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'bootstrap' to v4.5.3 to include security fixes.", + "cve": "CVE-2018-20676", + "id": "pyup.io-49065", + "more_info_path": "/vulnerabilities/CVE-2018-20676/49065", "specs": [ "<0.13.0" ], @@ -25155,21 +25362,11 @@ ], "v": "<0.13.0" }, - { - "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'bootstrap' to v4.5.3 to include security fixes.", - "cve": "CVE-2018-20676", - "id": "pyup.io-49065", - "more_info_path": "/vulnerabilities/CVE-2018-20676/49065", - "specs": [ - "<0.13.0" - ], - "v": "<0.13.0" - }, { "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'jquery' to v3.5.1 to include security fixes.", - "cve": "CVE-2012-6708", - "id": "pyup.io-49057", - "more_info_path": "/vulnerabilities/CVE-2012-6708/49057", + "cve": "CVE-2011-4969", + "id": "pyup.io-39529", + "more_info_path": "/vulnerabilities/CVE-2011-4969/39529", "specs": [ "<0.13.0" ], @@ -25177,19 +25374,19 @@ }, { "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'jquery' to v3.5.1 to include security fixes.", - "cve": "CVE-2012-6708", - "id": "pyup.io-49056", - "more_info_path": "/vulnerabilities/CVE-2012-6708/49056", + "cve": "CVE-2015-9251", + "id": "pyup.io-49058", + "more_info_path": "/vulnerabilities/CVE-2015-9251/49058", "specs": [ "<0.13.0" ], "v": "<0.13.0" }, { - "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'jquery' to v3.5.1 to include security fixes.", - "cve": "CVE-2015-9251", - "id": "pyup.io-49059", - "more_info_path": "/vulnerabilities/CVE-2015-9251/49059", + "advisory": "Crate-docs-theme 0.13.0 updates its NPM dependency 'bootstrap' to v4.5.3 to include security fixes.", + "cve": "CVE-2018-20677", + "id": "pyup.io-49064", + "more_info_path": "/vulnerabilities/CVE-2018-20677/49064", "specs": [ "<0.13.0" ], @@ -25403,6 +25600,18 @@ "v": "<2.0.2" } ], + "cryptoaitools": [ + { + "advisory": "The CryptoAiTools package on PyPI is malicious and designed to steal sensitive information, specifically targeting cryptocurrency wallet and browser data. It affects both Windows and macOS systems.", + "cve": "PVE-2024-73997", + "id": "pyup.io-73997", + "more_info_path": "/vulnerabilities/PVE-2024-73997/73997", + "specs": [ + ">=0" + ], + "v": ">=0" + } + ], "cryptoasset-data-downloader": [ { "advisory": "The cryptoasset-data-downloader package in PyPI v1.0.0 to v1.0.1 was discovered to contain a code execution backdoor via the request package. This vulnerability allows attackers to access sensitive user information and digital currency keys, as well as escalate privileges.", @@ -25530,36 +25739,6 @@ ], "v": "<39.0.1" }, - { - "advisory": "Cryptography 39.0.1 updates its dependency 'OpenSSL' to v3.0.8 to include security fixes.\r\nhttps://github.com/pyca/cryptography/issues/8229", - "cve": "CVE-2023-0217", - "id": "pyup.io-53306", - "more_info_path": "/vulnerabilities/CVE-2023-0217/53306", - "specs": [ - "<39.0.1" - ], - "v": "<39.0.1" - }, - { - "advisory": "Cryptography 39.0.1 includes a fix for CVE-2022-3996, a DoS vulnerability affecting openssl.\r\nhttps://github.com/pyca/cryptography/issues/7940", - "cve": "CVE-2022-3996", - "id": "pyup.io-53298", - "more_info_path": "/vulnerabilities/CVE-2022-3996/53298", - "specs": [ - "<39.0.1" - ], - "v": "<39.0.1" - }, - { - "advisory": "Cryptography 39.0.1 updates its dependency 'OpenSSL' to v3.0.8 to include security fixes.\r\nhttps://github.com/pyca/cryptography/issues/8229", - "cve": "CVE-2022-4203", - "id": "pyup.io-53301", - "more_info_path": "/vulnerabilities/CVE-2022-4203/53301", - "specs": [ - "<39.0.1" - ], - "v": "<39.0.1" - }, { "advisory": "Cryptography 39.0.1 updates its dependency 'OpenSSL' to v3.0.8 to include security fixes.\r\nhttps://github.com/pyca/cryptography/issues/8229", "cve": "CVE-2023-0216", @@ -25582,9 +25761,9 @@ }, { "advisory": "Cryptography 39.0.1 updates its dependency 'OpenSSL' to v3.0.8 to include security fixes.\r\nhttps://github.com/pyca/cryptography/issues/8229", - "cve": "CVE-2023-0286", - "id": "pyup.io-53304", - "more_info_path": "/vulnerabilities/CVE-2023-0286/53304", + "cve": "CVE-2022-4450", + "id": "pyup.io-53299", + "more_info_path": "/vulnerabilities/CVE-2022-4450/53299", "specs": [ "<39.0.1" ], @@ -25592,9 +25771,39 @@ }, { "advisory": "Cryptography 39.0.1 updates its dependency 'OpenSSL' to v3.0.8 to include security fixes.\r\nhttps://github.com/pyca/cryptography/issues/8229", - "cve": "CVE-2022-4450", - "id": "pyup.io-53299", - "more_info_path": "/vulnerabilities/CVE-2022-4450/53299", + "cve": "CVE-2023-0217", + "id": "pyup.io-53306", + "more_info_path": "/vulnerabilities/CVE-2023-0217/53306", + "specs": [ + "<39.0.1" + ], + "v": "<39.0.1" + }, + { + "advisory": "Cryptography 39.0.1 includes a fix for CVE-2022-3996, a DoS vulnerability affecting openssl.\r\nhttps://github.com/pyca/cryptography/issues/7940", + "cve": "CVE-2022-3996", + "id": "pyup.io-53298", + "more_info_path": "/vulnerabilities/CVE-2022-3996/53298", + "specs": [ + "<39.0.1" + ], + "v": "<39.0.1" + }, + { + "advisory": "Cryptography 39.0.1 updates its dependency 'OpenSSL' to v3.0.8 to include security fixes.\r\nhttps://github.com/pyca/cryptography/issues/8229", + "cve": "CVE-2022-4203", + "id": "pyup.io-53301", + "more_info_path": "/vulnerabilities/CVE-2022-4203/53301", + "specs": [ + "<39.0.1" + ], + "v": "<39.0.1" + }, + { + "advisory": "Cryptography 39.0.1 updates its dependency 'OpenSSL' to v3.0.8 to include security fixes.\r\nhttps://github.com/pyca/cryptography/issues/8229", + "cve": "CVE-2023-0286", + "id": "pyup.io-53304", + "more_info_path": "/vulnerabilities/CVE-2023-0286/53304", "specs": [ "<39.0.1" ], @@ -25651,20 +25860,20 @@ "v": "<41.0.5" }, { - "advisory": "Affected versions of Cryptography may allow a remote attacker to decrypt captured messages in TLS servers that use RSA key exchanges, which may lead to exposure of confidential or sensitive data.", - "cve": "CVE-2023-50782", - "id": "pyup.io-65278", - "more_info_path": "/vulnerabilities/CVE-2023-50782/65278", + "advisory": "Cryptography starting from version 42.0.0 updates its CI configurations to use newer versions of BoringSSL or OpenSSL as a countermeasure to CVE-2023-5678. This vulnerability, affecting the package, could cause Denial of Service through specific DH key generation and verification functions when given overly long parameters.", + "cve": "CVE-2023-5678", + "id": "pyup.io-65510", + "more_info_path": "/vulnerabilities/CVE-2023-5678/65510", "specs": [ "<42.0.0" ], "v": "<42.0.0" }, { - "advisory": "Cryptography starting from version 42.0.0 updates its CI configurations to use newer versions of BoringSSL or OpenSSL as a countermeasure to CVE-2023-5678. This vulnerability, affecting the package, could cause Denial of Service through specific DH key generation and verification functions when given overly long parameters.", - "cve": "CVE-2023-5678", - "id": "pyup.io-65510", - "more_info_path": "/vulnerabilities/CVE-2023-5678/65510", + "advisory": "Affected versions of Cryptography may allow a remote attacker to decrypt captured messages in TLS servers that use RSA key exchanges, which may lead to exposure of confidential or sensitive data.", + "cve": "CVE-2023-50782", + "id": "pyup.io-65278", + "more_info_path": "/vulnerabilities/CVE-2023-50782/65278", "specs": [ "<42.0.0" ], @@ -25721,20 +25930,20 @@ "v": ">=0,<1.1" }, { - "advisory": "Cryptography 41.0.3 updates its bundled OpenSSL version to include a fix for a Denial of Service vulnerability.\r\nhttps://github.com/pyca/cryptography/commit/b22271cf3c3dd8dc8978f8f4b00b5c7060b6538d\r\nhttps://www.openssl.org/news/secadv/20230731.txt", - "cve": "CVE-2023-3817", - "id": "pyup.io-60223", - "more_info_path": "/vulnerabilities/CVE-2023-3817/60223", + "advisory": "Cryptography 41.0.3 updates its bundled OpenSSL version to include a fix for CVE-2023-2975: AES-SIV implementation ignores empty associated data entries.\r\nhttps://github.com/pyca/cryptography/commit/bfa4d95f0f356f2d535efd5c775e0fb3efe90ef2\r\nhttps://www.openssl.org/news/secadv/20230714.txt", + "cve": "CVE-2023-2975", + "id": "pyup.io-60224", + "more_info_path": "/vulnerabilities/CVE-2023-2975/60224", "specs": [ ">=0.8,<41.0.3" ], "v": ">=0.8,<41.0.3" }, { - "advisory": "Cryptography 41.0.3 updates its bundled OpenSSL version to include a fix for CVE-2023-2975: AES-SIV implementation ignores empty associated data entries.\r\nhttps://github.com/pyca/cryptography/commit/bfa4d95f0f356f2d535efd5c775e0fb3efe90ef2\r\nhttps://www.openssl.org/news/secadv/20230714.txt", - "cve": "CVE-2023-2975", - "id": "pyup.io-60224", - "more_info_path": "/vulnerabilities/CVE-2023-2975/60224", + "advisory": "Cryptography 41.0.3 updates its bundled OpenSSL version to include a fix for a Denial of Service vulnerability.\r\nhttps://github.com/pyca/cryptography/commit/b22271cf3c3dd8dc8978f8f4b00b5c7060b6538d\r\nhttps://www.openssl.org/news/secadv/20230731.txt", + "cve": "CVE-2023-3817", + "id": "pyup.io-60223", + "more_info_path": "/vulnerabilities/CVE-2023-3817/60223", "specs": [ ">=0.8,<41.0.3" ], @@ -25781,14 +25990,14 @@ "v": ">=3.1,<41.0.6" }, { - "advisory": "Checking excessively long invalid RSA public keys may take a long time. Applications that use the function EVP_PKEY_public_check() to check RSA public keys may experience long delays. Where the key that is being checked has been obtained from an untrusted source, this may lead to a Denial of Service. When function EVP_PKEY_public_check() is called on RSA public keys, a computation is done to confirm that the RSA modulus, n, is composite. For valid RSA keys, n is a product of two or more large primes and this computation completes quickly. However, if n is an overly large prime, then this computation would take a long time. An application that calls EVP_PKEY_public_check() and supplies an RSA key obtained from an untrusted source could be vulnerable to a Denial of Service attack. The function EVP_PKEY_public_check() is not called from other OpenSSL functions, however it is called from the OpenSSL pkey command line application. For that reason, that application is also vulnerable if used with the '-pubin' and '-check' options on untrusted data. The OpenSSL SSL/TLS implementation is not affected by this issue. The OpenSSL 3.0 and 3.1 FIPS providers are affected by this issue.", + "advisory": "Cryptography 42.0.0 updates its bundled dependency 'OpenSSL' so to include the commit fix for CVE-2023-6237: Checking excessively long invalid RSA public keys may take a long time.", "cve": "CVE-2023-6237", "id": "pyup.io-66777", "more_info_path": "/vulnerabilities/CVE-2023-6237/66777", "specs": [ - ">=35.0.0,<42.0.2" + ">=35.0.0,<42.0.0" ], - "v": ">=35.0.0,<42.0.2" + "v": ">=35.0.0,<42.0.0" }, { "advisory": "Versions of Cryptograph starting from 35.0.0 are susceptible to a security flaw in the POLY1305 MAC algorithm on PowerPC CPUs, which allows an attacker to disrupt the application's state. This disruption might result in false calculations or cause a denial of service. The vulnerability's exploitation hinges on the attacker's ability to alter the algorithm's application and the dependency of the software on non-volatile XMM registers.\r\nhttps://github.com/pyca/cryptography/commit/89d0d56fb104ac4e0e6db63d78fc22b8c53d27e9", @@ -25812,9 +26021,9 @@ }, { "advisory": "Cryptography versions from 37.0.0 and before 38.0.2 include a statically linked copy of OpenSSL that has known vulnerabilities.\r\nhttps://github.com/pyca/cryptography/security/advisories/GHSA-39hc-v87j-747x", - "cve": "CVE-2022-3602", - "id": "pyup.io-52174", - "more_info_path": "/vulnerabilities/CVE-2022-3602/52174", + "cve": "CVE-2022-3786", + "id": "pyup.io-52173", + "more_info_path": "/vulnerabilities/CVE-2022-3786/52173", "specs": [ ">=37.0.0,<38.0.3" ], @@ -25822,9 +26031,9 @@ }, { "advisory": "Cryptography versions from 37.0.0 and before 38.0.2 include a statically linked copy of OpenSSL that has known vulnerabilities.\r\nhttps://github.com/pyca/cryptography/security/advisories/GHSA-39hc-v87j-747x", - "cve": "CVE-2022-3786", - "id": "pyup.io-52173", - "more_info_path": "/vulnerabilities/CVE-2022-3786/52173", + "cve": "CVE-2022-3602", + "id": "pyup.io-52174", + "more_info_path": "/vulnerabilities/CVE-2022-3602/52174", "specs": [ ">=37.0.0,<38.0.3" ], @@ -25947,6 +26156,18 @@ "v": ">0" } ], + "cudaq": [ + { + "advisory": "Affected versions of cudaq are vulnerable to Race Conditions (CWE-362) in asynchronous operations. This flaw allows concurrent threads to access and modify shared resources (asyncArgsHolder), potentially leading to data corruption or unexpected behavior. The vulnerability arises from using a global std::unordered_map without proper synchronization in py_observe.cpp and py_sample.cpp. Exploitation involves initiating multiple asynchronous tasks that manipulate shared arguments simultaneously. To mitigate, upgrade to the version which removes the shared asyncArgsHolder and adopts thread-safe argument handling.", + "cve": "PVE-2024-74193", + "id": "pyup.io-74193", + "more_info_path": "/vulnerabilities/PVE-2024-74193/74193", + "specs": [ + "<0.6.0" + ], + "v": "<0.6.0" + } + ], "cumulusci": [ { "advisory": "Cumulusci 3.67.0 uses 'defusedxml' to prevent XXE vulnerabilities.\r\nhttps://github.com/SFDO-Tooling/CumulusCI/pull/3375", @@ -26120,20 +26341,20 @@ ], "cve-bin-tool": [ { - "advisory": "Cve-bin-tool version 3.3rc3 updates its Pillow dependency to version 10.0.1 from 9.5.0 to address the security vulnerability outlined in CVE-2023-44271.", - "cve": "CVE-2023-44271", - "id": "pyup.io-67593", - "more_info_path": "/vulnerabilities/CVE-2023-44271/67593", + "advisory": "Cve-bin-tool version 3.3rc3 updates its Pillow dependency to version 10.0.1 from 9.5.0 to address the security vulnerability outlined in CVE-2023-4863.", + "cve": "CVE-2023-4863", + "id": "pyup.io-67586", + "more_info_path": "/vulnerabilities/CVE-2023-4863/67586", "specs": [ "<3.3rc3" ], "v": "<3.3rc3" }, { - "advisory": "Cve-bin-tool version 3.3rc3 updates its Pillow dependency to version 10.0.1 from 9.5.0 to address the security vulnerability outlined in CVE-2023-4863.", - "cve": "CVE-2023-4863", - "id": "pyup.io-67586", - "more_info_path": "/vulnerabilities/CVE-2023-4863/67586", + "advisory": "Cve-bin-tool version 3.3rc3 updates its Pillow dependency to version 10.0.1 from 9.5.0 to address the security vulnerability outlined in CVE-2023-44271.", + "cve": "CVE-2023-44271", + "id": "pyup.io-67593", + "more_info_path": "/vulnerabilities/CVE-2023-44271/67593", "specs": [ "<3.3rc3" ], @@ -26293,16 +26514,6 @@ } ], "d8s-asns": [ - { - "advisory": "D8s-asns 0.1.0 is vulnerable to CVE-2022-40426: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-networking package.", - "cve": "CVE-2022-40426", - "id": "pyup.io-51131", - "more_info_path": "/vulnerabilities/CVE-2022-40426/51131", - "specs": [ - "==0.1.0" - ], - "v": "==0.1.0" - }, { "advisory": "D8s-asns 0.1.0 is vulnerable to CVE-2022-42037: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-csv package.", "cve": "CVE-2022-42037", @@ -26322,19 +26533,19 @@ "==0.1.0" ], "v": "==0.1.0" - } - ], - "d8s-dates": [ + }, { - "advisory": "D8s-dates 0.1.0 is vulnerable to CVE-2022-40808: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-hypothesis package.", - "cve": "CVE-2022-40808", - "id": "pyup.io-51141", - "more_info_path": "/vulnerabilities/CVE-2022-40808/51141", + "advisory": "D8s-asns 0.1.0 is vulnerable to CVE-2022-40426: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-networking package.", + "cve": "CVE-2022-40426", + "id": "pyup.io-51131", + "more_info_path": "/vulnerabilities/CVE-2022-40426/51131", "specs": [ "==0.1.0" ], "v": "==0.1.0" - }, + } + ], + "d8s-dates": [ { "advisory": "D8s-dates 0.1.0 includes a potential code-execution backdoor inserted by a third party: the democritus-timezones package.", "cve": "CVE-2022-44052", @@ -26344,6 +26555,16 @@ "==0.1.0" ], "v": "==0.1.0" + }, + { + "advisory": "D8s-dates 0.1.0 is vulnerable to CVE-2022-40808: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-hypothesis package.", + "cve": "CVE-2022-40808", + "id": "pyup.io-51141", + "more_info_path": "/vulnerabilities/CVE-2022-40808/51141", + "specs": [ + "==0.1.0" + ], + "v": "==0.1.0" } ], "d8s-dicts": [ @@ -26416,20 +26637,20 @@ ], "d8s-html": [ { - "advisory": "D8s-html 0.1.0 is vulnerable to CVE-2022-40425: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-networking package.", - "cve": "CVE-2022-40425", - "id": "pyup.io-51130", - "more_info_path": "/vulnerabilities/CVE-2022-40425/51130", + "advisory": "D8s-html 0.1.0 is vulnerable to CVE-2022-41385: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-urls package.", + "cve": "CVE-2022-41385", + "id": "pyup.io-51406", + "more_info_path": "/vulnerabilities/CVE-2022-41385/51406", "specs": [ "==0.1.0" ], "v": "==0.1.0" }, { - "advisory": "D8s-html 0.1.0 is vulnerable to CVE-2022-41385: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-urls package.", - "cve": "CVE-2022-41385", - "id": "pyup.io-51406", - "more_info_path": "/vulnerabilities/CVE-2022-41385/51406", + "advisory": "D8s-html 0.1.0 is vulnerable to CVE-2022-40425: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-networking package.", + "cve": "CVE-2022-40425", + "id": "pyup.io-51130", + "more_info_path": "/vulnerabilities/CVE-2022-40425/51130", "specs": [ "==0.1.0" ], @@ -26438,10 +26659,10 @@ ], "d8s-ip-addresses": [ { - "advisory": "D8s-ip-addresses 0.1.0 is vulnerable to CVE-2022-40429: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-networking package.", - "cve": "CVE-2022-40429", - "id": "pyup.io-51134", - "more_info_path": "/vulnerabilities/CVE-2022-40429/51134", + "advisory": "D8s-ip-addresses 0.1.0 is vulnerable to CVE-2022-42038: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-csv package.", + "cve": "CVE-2022-42038", + "id": "pyup.io-51411", + "more_info_path": "/vulnerabilities/CVE-2022-42038/51411", "specs": [ "==0.1.0" ], @@ -26458,10 +26679,10 @@ "v": "==0.1.0" }, { - "advisory": "D8s-ip-addresses 0.1.0 is vulnerable to CVE-2022-42038: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-csv package.", - "cve": "CVE-2022-42038", - "id": "pyup.io-51411", - "more_info_path": "/vulnerabilities/CVE-2022-42038/51411", + "advisory": "D8s-ip-addresses 0.1.0 is vulnerable to CVE-2022-40429: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-networking package.", + "cve": "CVE-2022-40429", + "id": "pyup.io-51134", + "more_info_path": "/vulnerabilities/CVE-2022-40429/51134", "specs": [ "==0.1.0" ], @@ -26539,6 +26760,16 @@ } ], "d8s-networking": [ + { + "advisory": "D8s-networking 0.1.0 includes a potential code-execution backdoor inserted by a third party: the democritus-user-agents package.", + "cve": "CVE-2022-44053", + "id": "pyup.io-51733", + "more_info_path": "/vulnerabilities/CVE-2022-44053/51733", + "specs": [ + "==0.1.0" + ], + "v": "==0.1.0" + }, { "advisory": "D8s-networking 0.1.0 includes a potential code-execution backdoor inserted by a third party: the democritus-json package.", "cve": "CVE-2022-44050", @@ -26558,16 +26789,6 @@ "==0.1.0" ], "v": "==0.1.0" - }, - { - "advisory": "D8s-networking 0.1.0 includes a potential code-execution backdoor inserted by a third party: the democritus-user-agents package.", - "cve": "CVE-2022-44053", - "id": "pyup.io-51733", - "more_info_path": "/vulnerabilities/CVE-2022-44053/51733", - "specs": [ - "==0.1.0" - ], - "v": "==0.1.0" } ], "d8s-pdfs": [ @@ -26648,20 +26869,20 @@ ], "d8s-strings": [ { - "advisory": "D8s-strings 0.1.0 is vulnerable to CVE-2022-40432: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-hypothesis package.", - "cve": "CVE-2022-40432", - "id": "pyup.io-51137", - "more_info_path": "/vulnerabilities/CVE-2022-40432/51137", + "advisory": "D8s-strings 0.1.0 includes a potential code-execution backdoor inserted by a third party: the democritus-uuids package.", + "cve": "CVE-2022-43303", + "id": "pyup.io-51724", + "more_info_path": "/vulnerabilities/CVE-2022-43303/51724", "specs": [ "==0.1.0" ], "v": "==0.1.0" }, { - "advisory": "D8s-strings 0.1.0 includes a potential code-execution backdoor inserted by a third party: the democritus-uuids package.", - "cve": "CVE-2022-43303", - "id": "pyup.io-51724", - "more_info_path": "/vulnerabilities/CVE-2022-43303/51724", + "advisory": "D8s-strings 0.1.0 is vulnerable to CVE-2022-40432: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-hypothesis package.", + "cve": "CVE-2022-40432", + "id": "pyup.io-51137", + "more_info_path": "/vulnerabilities/CVE-2022-40432/51137", "specs": [ "==0.1.0" ], @@ -26692,10 +26913,10 @@ ], "d8s-urls": [ { - "advisory": "D8s-urls 0.1.0 is vulnerable to CVE-2022-40811: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-file-system package.", - "cve": "CVE-2022-40811", - "id": "pyup.io-51144", - "more_info_path": "/vulnerabilities/CVE-2022-40811/51144", + "advisory": "D8s-urls 0.1.0 is vulnerable to CVE-2022-40424: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-networking package.", + "cve": "CVE-2022-40424", + "id": "pyup.io-51129", + "more_info_path": "/vulnerabilities/CVE-2022-40424/51129", "specs": [ "==0.1.0" ], @@ -26712,30 +26933,30 @@ "v": "==0.1.0" }, { - "advisory": "D8s-urls 0.1.0 is vulnerable to CVE-2022-40424: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-networking package.", - "cve": "CVE-2022-40424", - "id": "pyup.io-51129", - "more_info_path": "/vulnerabilities/CVE-2022-40424/51129", + "advisory": "D8s-urls 0.1.0 is vulnerable to CVE-2022-40811: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-file-system package.", + "cve": "CVE-2022-40811", + "id": "pyup.io-51144", + "more_info_path": "/vulnerabilities/CVE-2022-40811/51144", "specs": [ "==0.1.0" ], "v": "==0.1.0" }, { - "advisory": "D8s-urls 0.1.0 is vulnerable to CVE-2022-38880: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-strings package.", - "cve": "CVE-2022-38880", - "id": "pyup.io-51121", - "more_info_path": "/vulnerabilities/CVE-2022-38880/51121", + "advisory": "D8s-urls 0.1.0 is vulnerable to CVE-2022-40805: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-hypothesis package.", + "cve": "CVE-2022-40805", + "id": "pyup.io-51138", + "more_info_path": "/vulnerabilities/CVE-2022-40805/51138", "specs": [ "==0.1.0" ], "v": "==0.1.0" }, { - "advisory": "D8s-urls 0.1.0 is vulnerable to CVE-2022-40805: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-hypothesis package.", - "cve": "CVE-2022-40805", - "id": "pyup.io-51138", - "more_info_path": "/vulnerabilities/CVE-2022-40805/51138", + "advisory": "D8s-urls 0.1.0 is vulnerable to CVE-2022-38880: It included a potential code-execution backdoor inserted by a third party. The backdoor is the democritus-strings package.", + "cve": "CVE-2022-38880", + "id": "pyup.io-51121", + "more_info_path": "/vulnerabilities/CVE-2022-38880/51121", "specs": [ "==0.1.0" ], @@ -26907,9 +27128,9 @@ "dagster-cloud": [ { "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2022-0778", - "id": "pyup.io-52165", - "more_info_path": "/vulnerabilities/CVE-2022-0778/52165", + "cve": "CVE-2021-33574", + "id": "pyup.io-52153", + "more_info_path": "/vulnerabilities/CVE-2021-33574/52153", "specs": [ "<1.1.4" ], @@ -26917,9 +27138,9 @@ }, { "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2021-46828", - "id": "pyup.io-52164", - "more_info_path": "/vulnerabilities/CVE-2021-46828/52164", + "cve": "CVE-2022-2509", + "id": "pyup.io-52163", + "more_info_path": "/vulnerabilities/CVE-2022-2509/52163", "specs": [ "<1.1.4" ], @@ -26927,9 +27148,9 @@ }, { "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2022-1664", - "id": "pyup.io-52146", - "more_info_path": "/vulnerabilities/CVE-2022-1664/52146", + "cve": "CVE-2021-4209", + "id": "pyup.io-52168", + "more_info_path": "/vulnerabilities/CVE-2021-4209/52168", "specs": [ "<1.1.4" ], @@ -26937,9 +27158,9 @@ }, { "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2018-25032", - "id": "pyup.io-52166", - "more_info_path": "/vulnerabilities/CVE-2018-25032/52166", + "cve": "CVE-2021-3999", + "id": "pyup.io-52160", + "more_info_path": "/vulnerabilities/CVE-2021-3999/52160", "specs": [ "<1.1.4" ], @@ -26947,9 +27168,9 @@ }, { "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2022-2509", - "id": "pyup.io-52163", - "more_info_path": "/vulnerabilities/CVE-2022-2509/52163", + "cve": "CVE-2022-37434", + "id": "pyup.io-52156", + "more_info_path": "/vulnerabilities/CVE-2022-37434/52156", "specs": [ "<1.1.4" ], @@ -26967,19 +27188,9 @@ }, { "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2021-33574", - "id": "pyup.io-52153", - "more_info_path": "/vulnerabilities/CVE-2021-33574/52153", - "specs": [ - "<1.1.4" - ], - "v": "<1.1.4" - }, - { - "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2021-3999", - "id": "pyup.io-52160", - "more_info_path": "/vulnerabilities/CVE-2021-3999/52160", + "cve": "CVE-2022-2068", + "id": "pyup.io-52155", + "more_info_path": "/vulnerabilities/CVE-2022-2068/52155", "specs": [ "<1.1.4" ], @@ -26997,9 +27208,9 @@ }, { "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2021-3997", - "id": "pyup.io-52170", - "more_info_path": "/vulnerabilities/CVE-2021-3997/52170", + "cve": "CVE-2022-1292", + "id": "pyup.io-52154", + "more_info_path": "/vulnerabilities/CVE-2022-1292/52154", "specs": [ "<1.1.4" ], @@ -27007,9 +27218,9 @@ }, { "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2022-37434", - "id": "pyup.io-52156", - "more_info_path": "/vulnerabilities/CVE-2022-37434/52156", + "cve": "CVE-2022-1664", + "id": "pyup.io-52146", + "more_info_path": "/vulnerabilities/CVE-2022-1664/52146", "specs": [ "<1.1.4" ], @@ -27017,9 +27228,9 @@ }, { "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2022-1292", - "id": "pyup.io-52154", - "more_info_path": "/vulnerabilities/CVE-2022-1292/52154", + "cve": "CVE-2018-25032", + "id": "pyup.io-52166", + "more_info_path": "/vulnerabilities/CVE-2018-25032/52166", "specs": [ "<1.1.4" ], @@ -27027,9 +27238,9 @@ }, { "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2022-1586", - "id": "pyup.io-52158", - "more_info_path": "/vulnerabilities/CVE-2022-1586/52158", + "cve": "CVE-2022-0778", + "id": "pyup.io-52165", + "more_info_path": "/vulnerabilities/CVE-2022-0778/52165", "specs": [ "<1.1.4" ], @@ -27037,9 +27248,9 @@ }, { "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2022-1271", - "id": "pyup.io-52159", - "more_info_path": "/vulnerabilities/CVE-2022-1271/52159", + "cve": "CVE-2021-3997", + "id": "pyup.io-52170", + "more_info_path": "/vulnerabilities/CVE-2021-3997/52170", "specs": [ "<1.1.4" ], @@ -27057,9 +27268,9 @@ }, { "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2022-40674", - "id": "pyup.io-52150", - "more_info_path": "/vulnerabilities/CVE-2022-40674/52150", + "cve": "CVE-2022-1586", + "id": "pyup.io-52158", + "more_info_path": "/vulnerabilities/CVE-2022-1586/52158", "specs": [ "<1.1.4" ], @@ -27067,9 +27278,9 @@ }, { "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2022-23218", - "id": "pyup.io-52152", - "more_info_path": "/vulnerabilities/CVE-2022-23218/52152", + "cve": "CVE-2022-1271", + "id": "pyup.io-52159", + "more_info_path": "/vulnerabilities/CVE-2022-1271/52159", "specs": [ "<1.1.4" ], @@ -27077,9 +27288,9 @@ }, { "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2022-2068", - "id": "pyup.io-52155", - "more_info_path": "/vulnerabilities/CVE-2022-2068/52155", + "cve": "CVE-2022-40674", + "id": "pyup.io-52150", + "more_info_path": "/vulnerabilities/CVE-2022-40674/52150", "specs": [ "<1.1.4" ], @@ -27097,9 +27308,19 @@ }, { "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", - "cve": "CVE-2021-4209", - "id": "pyup.io-52168", - "more_info_path": "/vulnerabilities/CVE-2021-4209/52168", + "cve": "CVE-2021-46828", + "id": "pyup.io-52164", + "more_info_path": "/vulnerabilities/CVE-2021-46828/52164", + "specs": [ + "<1.1.4" + ], + "v": "<1.1.4" + }, + { + "advisory": "Dagster-cloud 1.1.4 updates 'dagster/dagster-cloud-agent' Docker image\u2019s base to 'python:3.8.15-slim' to include security fixes.", + "cve": "CVE-2022-23218", + "id": "pyup.io-52152", + "more_info_path": "/vulnerabilities/CVE-2022-23218/52152", "specs": [ "<1.1.4" ], @@ -27122,19 +27343,9 @@ "daphne": [ { "advisory": "Daphne 4.0.0 updates its dependency 'twisted' to versions '>=22.4' to include security fixes.", - "cve": "CVE-2020-10108", - "id": "pyup.io-51379", - "more_info_path": "/vulnerabilities/CVE-2020-10108/51379", - "specs": [ - "<4.0.0" - ], - "v": "<4.0.0" - }, - { - "advisory": "Daphne 4.0.0 updates its dependency 'twisted' to versions '>=22.4' to include security fixes.", - "cve": "CVE-2022-21712", - "id": "pyup.io-51377", - "more_info_path": "/vulnerabilities/CVE-2022-21712/51377", + "cve": "CVE-2020-10109", + "id": "pyup.io-51378", + "more_info_path": "/vulnerabilities/CVE-2020-10109/51378", "specs": [ "<4.0.0" ], @@ -27152,9 +27363,9 @@ }, { "advisory": "Daphne 4.0.0 updates its dependency 'twisted' to versions '>=22.4' to include security fixes.", - "cve": "CVE-2020-10109", - "id": "pyup.io-51378", - "more_info_path": "/vulnerabilities/CVE-2020-10109/51378", + "cve": "CVE-2020-10108", + "id": "pyup.io-51379", + "more_info_path": "/vulnerabilities/CVE-2020-10108/51379", "specs": [ "<4.0.0" ], @@ -27182,9 +27393,9 @@ }, { "advisory": "Daphne 4.0.0b1 updates its dependency 'twisted' requirement to '>=22.4' to include security fixes.", - "cve": "CVE-2019-12387", - "id": "pyup.io-50818", - "more_info_path": "/vulnerabilities/CVE-2019-12387/50818", + "cve": "CVE-2022-24801", + "id": "pyup.io-50768", + "more_info_path": "/vulnerabilities/CVE-2022-24801/50768", "specs": [ "<4.0.0b1" ], @@ -27202,9 +27413,9 @@ }, { "advisory": "Daphne 4.0.0b1 updates its dependency 'twisted' requirement to '>=22.4' to include security fixes.", - "cve": "CVE-2022-21712", - "id": "pyup.io-50814", - "more_info_path": "/vulnerabilities/CVE-2022-21712/50814", + "cve": "CVE-2019-12387", + "id": "pyup.io-50818", + "more_info_path": "/vulnerabilities/CVE-2019-12387/50818", "specs": [ "<4.0.0b1" ], @@ -27212,9 +27423,9 @@ }, { "advisory": "Daphne 4.0.0b1 updates its dependency 'twisted' requirement to '>=22.4' to include security fixes.", - "cve": "CVE-2022-24801", - "id": "pyup.io-50768", - "more_info_path": "/vulnerabilities/CVE-2022-24801/50768", + "cve": "CVE-2022-21712", + "id": "pyup.io-50814", + "more_info_path": "/vulnerabilities/CVE-2022-21712/50814", "specs": [ "<4.0.0b1" ], @@ -27224,9 +27435,9 @@ "dapla-toolbelt-pseudo": [ { "advisory": "Dapla-toolbelt-pseudo 0.2.1 updates its dependency 'cryptography' to v39.0.1 to include security fixes.", - "cve": "CVE-2023-0286", - "id": "pyup.io-53733", - "more_info_path": "/vulnerabilities/CVE-2023-0286/53733", + "cve": "CVE-2022-4450", + "id": "pyup.io-53735", + "more_info_path": "/vulnerabilities/CVE-2022-4450/53735", "specs": [ "<0.2.1" ], @@ -27234,9 +27445,9 @@ }, { "advisory": "Dapla-toolbelt-pseudo 0.2.1 updates its dependency 'cryptography' to v39.0.1 to include security fixes.", - "cve": "CVE-2022-4203", - "id": "pyup.io-53736", - "more_info_path": "/vulnerabilities/CVE-2022-4203/53736", + "cve": "CVE-2023-0286", + "id": "pyup.io-53733", + "more_info_path": "/vulnerabilities/CVE-2023-0286/53733", "specs": [ "<0.2.1" ], @@ -27254,9 +27465,9 @@ }, { "advisory": "Dapla-toolbelt-pseudo 0.2.1 updates its dependency 'cryptography' to v39.0.1 to include security fixes.", - "cve": "CVE-2023-0215", - "id": "pyup.io-53731", - "more_info_path": "/vulnerabilities/CVE-2023-0215/53731", + "cve": "CVE-2022-4304", + "id": "pyup.io-53734", + "more_info_path": "/vulnerabilities/CVE-2022-4304/53734", "specs": [ "<0.2.1" ], @@ -27264,9 +27475,9 @@ }, { "advisory": "Dapla-toolbelt-pseudo 0.2.1 updates its dependency 'cryptography' to v39.0.1 to include security fixes.", - "cve": "CVE-2022-4304", - "id": "pyup.io-53734", - "more_info_path": "/vulnerabilities/CVE-2022-4304/53734", + "cve": "CVE-2023-0215", + "id": "pyup.io-53731", + "more_info_path": "/vulnerabilities/CVE-2023-0215/53731", "specs": [ "<0.2.1" ], @@ -27274,9 +27485,9 @@ }, { "advisory": "Dapla-toolbelt-pseudo 0.2.1 updates its dependency 'cryptography' to v39.0.1 to include security fixes.", - "cve": "CVE-2023-0217", - "id": "pyup.io-53732", - "more_info_path": "/vulnerabilities/CVE-2023-0217/53732", + "cve": "CVE-2022-4203", + "id": "pyup.io-53736", + "more_info_path": "/vulnerabilities/CVE-2022-4203/53736", "specs": [ "<0.2.1" ], @@ -27284,9 +27495,9 @@ }, { "advisory": "Dapla-toolbelt-pseudo 0.2.1 updates its dependency 'cryptography' to v39.0.1 to include security fixes.", - "cve": "CVE-2022-4450", - "id": "pyup.io-53735", - "more_info_path": "/vulnerabilities/CVE-2022-4450/53735", + "cve": "CVE-2023-0217", + "id": "pyup.io-53732", + "more_info_path": "/vulnerabilities/CVE-2023-0217/53732", "specs": [ "<0.2.1" ], @@ -27407,6 +27618,16 @@ ], "v": "<0.1.1" }, + { + "advisory": "Dash-extensions 0.1.1 updates its NPM dependency 'minimist' to v1.2.6 to include a security fix.", + "cve": "CVE-2021-44906", + "id": "pyup.io-48546", + "more_info_path": "/vulnerabilities/CVE-2021-44906/48546", + "specs": [ + "<0.1.1" + ], + "v": "<0.1.1" + }, { "advisory": "Dash-extensions 0.1.1 updates its NPM dependency 'mermaid' to v9.0.1 to include a security fix.", "cve": "CVE-2021-43861", @@ -27418,14 +27639,14 @@ "v": "<0.1.1" }, { - "advisory": "Dash-extensions 0.1.1 updates its NPM dependency 'minimist' to v1.2.6 to include a security fix.", - "cve": "CVE-2021-44906", - "id": "pyup.io-48546", - "more_info_path": "/vulnerabilities/CVE-2021-44906/48546", + "advisory": "Dash-extensions 0.1.8 updates its NPM dependency 'loader-utils' to v3.2.1 to include security fixes.", + "cve": "CVE-2022-37601", + "id": "pyup.io-52351", + "more_info_path": "/vulnerabilities/CVE-2022-37601/52351", "specs": [ - "<0.1.1" + "<0.1.8" ], - "v": "<0.1.1" + "v": "<0.1.8" }, { "advisory": "Dash-extensions 0.1.8 updates its dependency 'cryptography' to v 38.0.3 to include security fixes.", @@ -27439,39 +27660,29 @@ }, { "advisory": "Dash-extensions 0.1.8 updates its NPM dependency 'loader-utils' to v3.2.1 to include security fixes.", - "cve": "CVE-2022-37603", - "id": "pyup.io-52353", - "more_info_path": "/vulnerabilities/CVE-2022-37603/52353", - "specs": [ - "<0.1.8" - ], - "v": "<0.1.8" - }, - { - "advisory": "Dash-extensions 0.1.8 updates its dependency 'cryptography' to v 38.0.3 to include security fixes.", - "cve": "CVE-2022-3786", - "id": "pyup.io-52355", - "more_info_path": "/vulnerabilities/CVE-2022-3786/52355", + "cve": "CVE-2022-37599", + "id": "pyup.io-52352", + "more_info_path": "/vulnerabilities/CVE-2022-37599/52352", "specs": [ "<0.1.8" ], "v": "<0.1.8" }, { - "advisory": "Dash-extensions 0.1.8 updates its NPM dependency \"mermaid\" requirement to \"^9.2.2\" to include a security fix.", - "cve": "CVE-2022-31108", - "id": "pyup.io-52354", - "more_info_path": "/vulnerabilities/CVE-2022-31108/52354", + "advisory": "Dash-extensions 0.1.8 updates its NPM dependency 'minimatch' to v3.1.2 to include a security fix.", + "cve": "CVE-2022-3517", + "id": "pyup.io-52303", + "more_info_path": "/vulnerabilities/CVE-2022-3517/52303", "specs": [ "<0.1.8" ], "v": "<0.1.8" }, { - "advisory": "Dash-extensions 0.1.8 updates its NPM dependency 'loader-utils' to v3.2.1 to include security fixes.", - "cve": "CVE-2022-37601", - "id": "pyup.io-52351", - "more_info_path": "/vulnerabilities/CVE-2022-37601/52351", + "advisory": "Dash-extensions 0.1.8 updates its dependency 'cryptography' to v 38.0.3 to include security fixes.", + "cve": "CVE-2022-3786", + "id": "pyup.io-52355", + "more_info_path": "/vulnerabilities/CVE-2022-3786/52355", "specs": [ "<0.1.8" ], @@ -27479,19 +27690,19 @@ }, { "advisory": "Dash-extensions 0.1.8 updates its NPM dependency 'loader-utils' to v3.2.1 to include security fixes.", - "cve": "CVE-2022-37599", - "id": "pyup.io-52352", - "more_info_path": "/vulnerabilities/CVE-2022-37599/52352", + "cve": "CVE-2022-37603", + "id": "pyup.io-52353", + "more_info_path": "/vulnerabilities/CVE-2022-37603/52353", "specs": [ "<0.1.8" ], "v": "<0.1.8" }, { - "advisory": "Dash-extensions 0.1.8 updates its NPM dependency 'minimatch' to v3.1.2 to include a security fix.", - "cve": "CVE-2022-3517", - "id": "pyup.io-52303", - "more_info_path": "/vulnerabilities/CVE-2022-3517/52303", + "advisory": "Dash-extensions 0.1.8 updates its NPM dependency \"mermaid\" requirement to \"^9.2.2\" to include a security fix.", + "cve": "CVE-2022-31108", + "id": "pyup.io-52354", + "more_info_path": "/vulnerabilities/CVE-2022-31108/52354", "specs": [ "<0.1.8" ], @@ -27508,20 +27719,20 @@ "v": "<0.1.9" }, { - "advisory": "Dash-extensions 0.1.9 updates its NPM dependency 'loader-utils' requirement to '>=3.2.1' to include security fixes.", - "cve": "CVE-2022-37599", - "id": "pyup.io-52653", - "more_info_path": "/vulnerabilities/CVE-2022-37599/52653", + "advisory": "Dash-extensions 0.1.9 updates its dependency 'certifi' to v2022.12.7 to include a security fix.", + "cve": "CVE-2022-23491", + "id": "pyup.io-52654", + "more_info_path": "/vulnerabilities/CVE-2022-23491/52654", "specs": [ "<0.1.9" ], "v": "<0.1.9" }, { - "advisory": "Dash-extensions 0.1.9 updates its dependency 'certifi' to v2022.12.7 to include a security fix.", - "cve": "CVE-2022-23491", - "id": "pyup.io-52654", - "more_info_path": "/vulnerabilities/CVE-2022-23491/52654", + "advisory": "Dash-extensions 0.1.9 updates its NPM dependency 'loader-utils' requirement to '>=3.2.1' to include security fixes.", + "cve": "CVE-2022-37599", + "id": "pyup.io-52653", + "more_info_path": "/vulnerabilities/CVE-2022-37599/52653", "specs": [ "<0.1.9" ], @@ -27743,9 +27954,9 @@ }, { "advisory": "Datagristle 0.1.7 updates its dependency 'werkzeug' to v1.0 to include security fixes.", - "cve": "CVE-2020-28724", - "id": "pyup.io-49137", - "more_info_path": "/vulnerabilities/CVE-2020-28724/49137", + "cve": "CVE-2016-10516", + "id": "pyup.io-49135", + "more_info_path": "/vulnerabilities/CVE-2016-10516/49135", "specs": [ "<0.1.7" ], @@ -27753,9 +27964,9 @@ }, { "advisory": "Datagristle 0.1.7 updates its dependency 'werkzeug' to v1.0 to include security fixes.", - "cve": "CVE-2016-10516", - "id": "pyup.io-49135", - "more_info_path": "/vulnerabilities/CVE-2016-10516/49135", + "cve": "CVE-2019-14806", + "id": "pyup.io-49136", + "more_info_path": "/vulnerabilities/CVE-2019-14806/49136", "specs": [ "<0.1.7" ], @@ -27763,9 +27974,9 @@ }, { "advisory": "Datagristle 0.1.7 updates its dependency 'werkzeug' to v1.0 to include security fixes.", - "cve": "CVE-2019-14806", - "id": "pyup.io-49136", - "more_info_path": "/vulnerabilities/CVE-2019-14806/49136", + "cve": "CVE-2020-28724", + "id": "pyup.io-49137", + "more_info_path": "/vulnerabilities/CVE-2020-28724/49137", "specs": [ "<0.1.7" ], @@ -27793,16 +28004,6 @@ } ], "datahub": [ - { - "advisory": "DataHub is an open-source metadata platform. DataHub Frontend's sessions are configured using Play Framework's default settings for stateless session which do not set an expiration time for a cookie. Due to this, if a session cookie were ever leaked, it would be valid forever. DataHub uses a stateless session cookie that is not invalidated on logout, it is just removed from the browser forcing the user to login again. However, if an attacker extracted a cookie from an authenticated user it would continue to be valid as there is no validation on a time window the session token is valid for due to a combination of the usage of LegacyCookiesModule from Play Framework and using default settings which do not set an expiration time. All DataHub instances prior to the patch that have removed the datahub user, but not the default policies applying to that user are affected. Users are advised to update to version 0.12.1 which addresses the issue. There are no known workarounds for this vulnerability.", - "cve": "CVE-2023-47628", - "id": "pyup.io-70896", - "more_info_path": "/vulnerabilities/CVE-2023-47628/70896", - "specs": [ - "<0.12.1" - ], - "v": "<0.12.1" - }, { "advisory": "DataHub is an open-source metadata platform. In affected versions sign-up through an invite link does not properly restrict users from signing up as privileged accounts. If a user is given an email sign-up link they can potentially create an admin account given certain preconditions. If the default datahub user has been removed, then the user can sign up for an account that leverages the default policies giving admin privileges to the datahub user. All DataHub instances prior to the patch that have removed the datahub user, but not the default policies applying to that user are affected. Users are advised to update to version 0.12.1 which addresses the issue. There are no known workarounds for this vulnerability.", "cve": "CVE-2023-47629", @@ -27814,14 +28015,14 @@ "v": "<0.12.1" }, { - "advisory": "DataHub's AuthServiceClient, specifically versions prior to 0.8.45, creates JSON strings using format strings containing user-controlled data. This method enables potential attackers to manipulate these JSON strings and forward them to the backend, leading to potential misuse and authentication bypasses. Such misuse could result in the generation of system accounts, potentially leading to full system compromise. This vulnerability was identified and reported by the GitHub Security lab and is being tracked under GHSL-2022-080.\r\nhttps://github.com/datahub-project/datahub/security/advisories/GHSA-6rpf-5cfg-h8f3", - "cve": "CVE-2023-25560", - "id": "pyup.io-63335", - "more_info_path": "/vulnerabilities/CVE-2023-25560/63335", + "advisory": "DataHub is an open-source metadata platform. DataHub Frontend's sessions are configured using Play Framework's default settings for stateless session which do not set an expiration time for a cookie. Due to this, if a session cookie were ever leaked, it would be valid forever. DataHub uses a stateless session cookie that is not invalidated on logout, it is just removed from the browser forcing the user to login again. However, if an attacker extracted a cookie from an authenticated user it would continue to be valid as there is no validation on a time window the session token is valid for due to a combination of the usage of LegacyCookiesModule from Play Framework and using default settings which do not set an expiration time. All DataHub instances prior to the patch that have removed the datahub user, but not the default policies applying to that user are affected. Users are advised to update to version 0.12.1 which addresses the issue. There are no known workarounds for this vulnerability.", + "cve": "CVE-2023-47628", + "id": "pyup.io-70896", + "more_info_path": "/vulnerabilities/CVE-2023-47628/70896", "specs": [ - "<0.8.45" + "<0.12.1" ], - "v": "<0.8.45" + "v": "<0.12.1" }, { "advisory": "In DataHub versions prior to 0.8.45, session cookies are only cleared upon new sign-ins, not during logouts. This allows potential attackers to bypass authentication checks using the AuthUtils.hasValidSessionCookie() method by using a cookie from a logged-out session. Consequently, any logged-out session cookie might be considered valid, leading to an authentication bypass. Users are advised to upgrade to version 0.8.45 to rectify this vulnerability. Currently, there are no known workarounds. This vulnerability was identified and reported by the GitHub Security lab and is being tracked under GHSL-2022-083.\r\nhttps://github.com/datahub-project/datahub/security/advisories/GHSA-3974-hxjh-m3jj\r\nhttps://github.com/datahub-project/datahub/blob/aa146db611e3a4ca3aa17bb740783f789d4444d3/datahub-frontend/app/auth/AuthUtils.java#L78", @@ -27833,6 +28034,16 @@ ], "v": "<0.8.45" }, + { + "advisory": "DataHub's AuthServiceClient, specifically versions prior to 0.8.45, creates JSON strings using format strings containing user-controlled data. This method enables potential attackers to manipulate these JSON strings and forward them to the backend, leading to potential misuse and authentication bypasses. Such misuse could result in the generation of system accounts, potentially leading to full system compromise. This vulnerability was identified and reported by the GitHub Security lab and is being tracked under GHSL-2022-080.\r\nhttps://github.com/datahub-project/datahub/security/advisories/GHSA-6rpf-5cfg-h8f3", + "cve": "CVE-2023-25560", + "id": "pyup.io-63335", + "more_info_path": "/vulnerabilities/CVE-2023-25560/63335", + "specs": [ + "<0.8.45" + ], + "v": "<0.8.45" + }, { "advisory": "DataHub under 0.8.45 uses the X-DataHub-Actor HTTP header to identify the user making requests without authentication. However, this can be exploited by attackers who can manipulate the case of the header (e.g., X-DATAHUB-ACTOR), leading to potential authorization bypass and unauthorized actions. This issue, identified and reported by GitHub Security Lab, is known as GHSL-2022-079.\r\nhttps://github.com/datahub-project/datahub/security/advisories/GHSA-qgp2-qr66-j8r8", "cve": "CVE-2023-25559", @@ -28174,16 +28385,6 @@ } ], "datum": [ - { - "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23587", - "id": "pyup.io-50393", - "more_info_path": "/vulnerabilities/CVE-2022-23587/50393", - "specs": [ - "<1.5.0" - ], - "v": "<1.5.0" - }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", "cve": "CVE-2022-23589", @@ -28196,9 +28397,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-21727", - "id": "pyup.io-50347", - "more_info_path": "/vulnerabilities/CVE-2022-21727/50347", + "cve": "CVE-2022-23587", + "id": "pyup.io-50393", + "more_info_path": "/vulnerabilities/CVE-2022-23587/50393", "specs": [ "<1.5.0" ], @@ -28214,16 +28415,6 @@ ], "v": "<1.5.0" }, - { - "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-27779", - "id": "pyup.io-50404", - "more_info_path": "/vulnerabilities/CVE-2022-27779/50404", - "specs": [ - "<1.5.0" - ], - "v": "<1.5.0" - }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", "cve": "CVE-2022-23571", @@ -28234,46 +28425,6 @@ ], "v": "<1.5.0" }, - { - "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23573", - "id": "pyup.io-50379", - "more_info_path": "/vulnerabilities/CVE-2022-23573/50379", - "specs": [ - "<1.5.0" - ], - "v": "<1.5.0" - }, - { - "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-21728", - "id": "pyup.io-50348", - "more_info_path": "/vulnerabilities/CVE-2022-21728/50348", - "specs": [ - "<1.5.0" - ], - "v": "<1.5.0" - }, - { - "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23568", - "id": "pyup.io-50374", - "more_info_path": "/vulnerabilities/CVE-2022-23568/50374", - "specs": [ - "<1.5.0" - ], - "v": "<1.5.0" - }, - { - "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-21738", - "id": "pyup.io-50358", - "more_info_path": "/vulnerabilities/CVE-2022-21738/50358", - "specs": [ - "<1.5.0" - ], - "v": "<1.5.0" - }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", "cve": "CVE-2022-21735", @@ -28286,19 +28437,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-21736", - "id": "pyup.io-50356", - "more_info_path": "/vulnerabilities/CVE-2022-21736/50356", - "specs": [ - "<1.5.0" - ], - "v": "<1.5.0" - }, - { - "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-21730", - "id": "pyup.io-50350", - "more_info_path": "/vulnerabilities/CVE-2022-21730/50350", + "cve": "CVE-2022-21728", + "id": "pyup.io-50348", + "more_info_path": "/vulnerabilities/CVE-2022-21728/50348", "specs": [ "<1.5.0" ], @@ -28316,49 +28457,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-21740", - "id": "pyup.io-50360", - "more_info_path": "/vulnerabilities/CVE-2022-21740/50360", - "specs": [ - "<1.5.0" - ], - "v": "<1.5.0" - }, - { - "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23566", - "id": "pyup.io-50372", - "more_info_path": "/vulnerabilities/CVE-2022-23566/50372", - "specs": [ - "<1.5.0" - ], - "v": "<1.5.0" - }, - { - "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23572", - "id": "pyup.io-50378", - "more_info_path": "/vulnerabilities/CVE-2022-23572/50378", - "specs": [ - "<1.5.0" - ], - "v": "<1.5.0" - }, - { - "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23567", - "id": "pyup.io-50373", - "more_info_path": "/vulnerabilities/CVE-2022-23567/50373", - "specs": [ - "<1.5.0" - ], - "v": "<1.5.0" - }, - { - "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23584", - "id": "pyup.io-50390", - "more_info_path": "/vulnerabilities/CVE-2022-23584/50390", + "cve": "CVE-2022-27779", + "id": "pyup.io-50404", + "more_info_path": "/vulnerabilities/CVE-2022-27779/50404", "specs": [ "<1.5.0" ], @@ -28376,19 +28477,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23594", - "id": "pyup.io-50398", - "more_info_path": "/vulnerabilities/CVE-2022-23594/50398", - "specs": [ - "<1.5.0" - ], - "v": "<1.5.0" - }, - { - "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23595", - "id": "pyup.io-50399", - "more_info_path": "/vulnerabilities/CVE-2022-23595/50399", + "cve": "CVE-2022-23566", + "id": "pyup.io-50372", + "more_info_path": "/vulnerabilities/CVE-2022-23566/50372", "specs": [ "<1.5.0" ], @@ -28396,9 +28487,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29193", - "id": "pyup.io-50410", - "more_info_path": "/vulnerabilities/CVE-2022-29193/50410", + "cve": "CVE-2022-23584", + "id": "pyup.io-50390", + "more_info_path": "/vulnerabilities/CVE-2022-23584/50390", "specs": [ "<1.5.0" ], @@ -28414,16 +28505,6 @@ ], "v": "<1.5.0" }, - { - "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23574", - "id": "pyup.io-50380", - "more_info_path": "/vulnerabilities/CVE-2022-23574/50380", - "specs": [ - "<1.5.0" - ], - "v": "<1.5.0" - }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", "cve": "CVE-2022-23562", @@ -28434,16 +28515,6 @@ ], "v": "<1.5.0" }, - { - "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23591", - "id": "pyup.io-50397", - "more_info_path": "/vulnerabilities/CVE-2022-23591/50397", - "specs": [ - "<1.5.0" - ], - "v": "<1.5.0" - }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", "cve": "CVE-2022-23588", @@ -28456,9 +28527,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23575", - "id": "pyup.io-50381", - "more_info_path": "/vulnerabilities/CVE-2022-23575/50381", + "cve": "CVE-2022-29216", + "id": "pyup.io-50430", + "more_info_path": "/vulnerabilities/CVE-2022-29216/50430", "specs": [ "<1.5.0" ], @@ -28466,9 +28537,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29216", - "id": "pyup.io-50430", - "more_info_path": "/vulnerabilities/CVE-2022-29216/50430", + "cve": "CVE-2022-23579", + "id": "pyup.io-50385", + "more_info_path": "/vulnerabilities/CVE-2022-23579/50385", "specs": [ "<1.5.0" ], @@ -28476,9 +28547,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23579", - "id": "pyup.io-50385", - "more_info_path": "/vulnerabilities/CVE-2022-23579/50385", + "cve": "CVE-2022-27775", + "id": "pyup.io-50401", + "more_info_path": "/vulnerabilities/CVE-2022-27775/50401", "specs": [ "<1.5.0" ], @@ -28486,9 +28557,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29192", - "id": "pyup.io-50409", - "more_info_path": "/vulnerabilities/CVE-2022-29192/50409", + "cve": "CVE-2022-27774", + "id": "pyup.io-50400", + "more_info_path": "/vulnerabilities/CVE-2022-27774/50400", "specs": [ "<1.5.0" ], @@ -28516,9 +28587,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-27774", - "id": "pyup.io-50400", - "more_info_path": "/vulnerabilities/CVE-2022-27774/50400", + "cve": "CVE-2022-29213", + "id": "pyup.io-50429", + "more_info_path": "/vulnerabilities/CVE-2022-29213/50429", "specs": [ "<1.5.0" ], @@ -28526,9 +28597,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29212", - "id": "pyup.io-50428", - "more_info_path": "/vulnerabilities/CVE-2022-29212/50428", + "cve": "CVE-2022-23564", + "id": "pyup.io-50370", + "more_info_path": "/vulnerabilities/CVE-2022-23564/50370", "specs": [ "<1.5.0" ], @@ -28536,9 +28607,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29213", - "id": "pyup.io-50429", - "more_info_path": "/vulnerabilities/CVE-2022-29213/50429", + "cve": "CVE-2022-23559", + "id": "pyup.io-50365", + "more_info_path": "/vulnerabilities/CVE-2022-23559/50365", "specs": [ "<1.5.0" ], @@ -28546,9 +28617,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29201", - "id": "pyup.io-50418", - "more_info_path": "/vulnerabilities/CVE-2022-29201/50418", + "cve": "CVE-2022-21739", + "id": "pyup.io-50359", + "more_info_path": "/vulnerabilities/CVE-2022-21739/50359", "specs": [ "<1.5.0" ], @@ -28556,9 +28627,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-21729", - "id": "pyup.io-50349", - "more_info_path": "/vulnerabilities/CVE-2022-21729/50349", + "cve": "CVE-2022-29198", + "id": "pyup.io-50415", + "more_info_path": "/vulnerabilities/CVE-2022-29198/50415", "specs": [ "<1.5.0" ], @@ -28566,9 +28637,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-21739", - "id": "pyup.io-50359", - "more_info_path": "/vulnerabilities/CVE-2022-21739/50359", + "cve": "CVE-2022-23578", + "id": "pyup.io-50384", + "more_info_path": "/vulnerabilities/CVE-2022-23578/50384", "specs": [ "<1.5.0" ], @@ -28576,9 +28647,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23559", - "id": "pyup.io-50365", - "more_info_path": "/vulnerabilities/CVE-2022-23559/50365", + "cve": "CVE-2020-10531", + "id": "pyup.io-50344", + "more_info_path": "/vulnerabilities/CVE-2020-10531/50344", "specs": [ "<1.5.0" ], @@ -28586,9 +28657,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23564", - "id": "pyup.io-50370", - "more_info_path": "/vulnerabilities/CVE-2022-23564/50370", + "cve": "CVE-2022-29196", + "id": "pyup.io-50413", + "more_info_path": "/vulnerabilities/CVE-2022-29196/50413", "specs": [ "<1.5.0" ], @@ -28596,9 +28667,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23578", - "id": "pyup.io-50384", - "more_info_path": "/vulnerabilities/CVE-2022-23578/50384", + "cve": "CVE-2022-29197", + "id": "pyup.io-50414", + "more_info_path": "/vulnerabilities/CVE-2022-29197/50414", "specs": [ "<1.5.0" ], @@ -28606,9 +28677,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23583", - "id": "pyup.io-50389", - "more_info_path": "/vulnerabilities/CVE-2022-23583/50389", + "cve": "CVE-2022-21734", + "id": "pyup.io-50354", + "more_info_path": "/vulnerabilities/CVE-2022-21734/50354", "specs": [ "<1.5.0" ], @@ -28616,9 +28687,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29196", - "id": "pyup.io-50413", - "more_info_path": "/vulnerabilities/CVE-2022-29196/50413", + "cve": "CVE-2022-21737", + "id": "pyup.io-50357", + "more_info_path": "/vulnerabilities/CVE-2022-21737/50357", "specs": [ "<1.5.0" ], @@ -28626,9 +28697,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29198", - "id": "pyup.io-50415", - "more_info_path": "/vulnerabilities/CVE-2022-29198/50415", + "cve": "CVE-2022-23582", + "id": "pyup.io-50388", + "more_info_path": "/vulnerabilities/CVE-2022-23582/50388", "specs": [ "<1.5.0" ], @@ -28636,9 +28707,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2020-10531", - "id": "pyup.io-50344", - "more_info_path": "/vulnerabilities/CVE-2020-10531/50344", + "cve": "CVE-2022-23577", + "id": "pyup.io-50383", + "more_info_path": "/vulnerabilities/CVE-2022-23577/50383", "specs": [ "<1.5.0" ], @@ -28646,9 +28717,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-27782", - "id": "pyup.io-50407", - "more_info_path": "/vulnerabilities/CVE-2022-27782/50407", + "cve": "CVE-2022-23581", + "id": "pyup.io-50387", + "more_info_path": "/vulnerabilities/CVE-2022-23581/50387", "specs": [ "<1.5.0" ], @@ -28656,9 +28727,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29197", - "id": "pyup.io-50414", - "more_info_path": "/vulnerabilities/CVE-2022-29197/50414", + "cve": "CVE-2022-23586", + "id": "pyup.io-50392", + "more_info_path": "/vulnerabilities/CVE-2022-23586/50392", "specs": [ "<1.5.0" ], @@ -28666,9 +28737,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-21732", - "id": "pyup.io-50352", - "more_info_path": "/vulnerabilities/CVE-2022-21732/50352", + "cve": "CVE-2022-29194", + "id": "pyup.io-50411", + "more_info_path": "/vulnerabilities/CVE-2022-29194/50411", "specs": [ "<1.5.0" ], @@ -28676,9 +28747,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-21733", - "id": "pyup.io-50353", - "more_info_path": "/vulnerabilities/CVE-2022-21733/50353", + "cve": "CVE-2022-29195", + "id": "pyup.io-50412", + "more_info_path": "/vulnerabilities/CVE-2022-29195/50412", "specs": [ "<1.5.0" ], @@ -28686,9 +28757,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-21734", - "id": "pyup.io-50354", - "more_info_path": "/vulnerabilities/CVE-2022-21734/50354", + "cve": "CVE-2022-29199", + "id": "pyup.io-50416", + "more_info_path": "/vulnerabilities/CVE-2022-29199/50416", "specs": [ "<1.5.0" ], @@ -28696,9 +28767,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-21737", - "id": "pyup.io-50357", - "more_info_path": "/vulnerabilities/CVE-2022-21737/50357", + "cve": "CVE-2022-29209", + "id": "pyup.io-50426", + "more_info_path": "/vulnerabilities/CVE-2022-29209/50426", "specs": [ "<1.5.0" ], @@ -28706,9 +28777,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-21741", - "id": "pyup.io-50361", - "more_info_path": "/vulnerabilities/CVE-2022-21741/50361", + "cve": "CVE-2022-23563", + "id": "pyup.io-50369", + "more_info_path": "/vulnerabilities/CVE-2022-23563/50369", "specs": [ "<1.5.0" ], @@ -28716,9 +28787,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23560", - "id": "pyup.io-50366", - "more_info_path": "/vulnerabilities/CVE-2022-23560/50366", + "cve": "CVE-2022-27778", + "id": "pyup.io-50403", + "more_info_path": "/vulnerabilities/CVE-2022-27778/50403", "specs": [ "<1.5.0" ], @@ -28726,9 +28797,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23565", - "id": "pyup.io-50371", - "more_info_path": "/vulnerabilities/CVE-2022-23565/50371", + "cve": "CVE-2022-27780", + "id": "pyup.io-50405", + "more_info_path": "/vulnerabilities/CVE-2022-27780/50405", "specs": [ "<1.5.0" ], @@ -28736,9 +28807,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23582", - "id": "pyup.io-50388", - "more_info_path": "/vulnerabilities/CVE-2022-23582/50388", + "cve": "CVE-2022-22576", + "id": "pyup.io-50362", + "more_info_path": "/vulnerabilities/CVE-2022-22576/50362", "specs": [ "<1.5.0" ], @@ -28746,9 +28817,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23576", - "id": "pyup.io-50382", - "more_info_path": "/vulnerabilities/CVE-2022-23576/50382", + "cve": "CVE-2022-23590", + "id": "pyup.io-50396", + "more_info_path": "/vulnerabilities/CVE-2022-23590/50396", "specs": [ "<1.5.0" ], @@ -28756,9 +28827,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23577", - "id": "pyup.io-50383", - "more_info_path": "/vulnerabilities/CVE-2022-23577/50383", + "cve": "CVE-2022-29207", + "id": "pyup.io-50424", + "more_info_path": "/vulnerabilities/CVE-2022-29207/50424", "specs": [ "<1.5.0" ], @@ -28766,9 +28837,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23581", - "id": "pyup.io-50387", - "more_info_path": "/vulnerabilities/CVE-2022-23581/50387", + "cve": "CVE-2022-30115", + "id": "pyup.io-50431", + "more_info_path": "/vulnerabilities/CVE-2022-30115/50431", "specs": [ "<1.5.0" ], @@ -28776,9 +28847,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23586", - "id": "pyup.io-50392", - "more_info_path": "/vulnerabilities/CVE-2022-23586/50392", + "cve": "CVE-2022-27782", + "id": "pyup.io-50407", + "more_info_path": "/vulnerabilities/CVE-2022-27782/50407", "specs": [ "<1.5.0" ], @@ -28786,9 +28857,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23585", - "id": "pyup.io-50391", - "more_info_path": "/vulnerabilities/CVE-2022-23585/50391", + "cve": "CVE-2022-27781", + "id": "pyup.io-50406", + "more_info_path": "/vulnerabilities/CVE-2022-27781/50406", "specs": [ "<1.5.0" ], @@ -28796,9 +28867,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-27775", - "id": "pyup.io-50401", - "more_info_path": "/vulnerabilities/CVE-2022-27775/50401", + "cve": "CVE-2018-25032", + "id": "pyup.io-50343", + "more_info_path": "/vulnerabilities/CVE-2018-25032/50343", "specs": [ "<1.5.0" ], @@ -28806,9 +28877,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29194", - "id": "pyup.io-50411", - "more_info_path": "/vulnerabilities/CVE-2022-29194/50411", + "cve": "CVE-2022-29212", + "id": "pyup.io-50428", + "more_info_path": "/vulnerabilities/CVE-2022-29212/50428", "specs": [ "<1.5.0" ], @@ -28816,9 +28887,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29195", - "id": "pyup.io-50412", - "more_info_path": "/vulnerabilities/CVE-2022-29195/50412", + "cve": "CVE-2022-29208", + "id": "pyup.io-50425", + "more_info_path": "/vulnerabilities/CVE-2022-29208/50425", "specs": [ "<1.5.0" ], @@ -28826,9 +28897,29 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29199", - "id": "pyup.io-50416", - "more_info_path": "/vulnerabilities/CVE-2022-29199/50416", + "cve": "CVE-2022-29206", + "id": "pyup.io-50423", + "more_info_path": "/vulnerabilities/CVE-2022-29206/50423", + "specs": [ + "<1.5.0" + ], + "v": "<1.5.0" + }, + { + "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", + "cve": "CVE-2022-29205", + "id": "pyup.io-50422", + "more_info_path": "/vulnerabilities/CVE-2022-29205/50422", + "specs": [ + "<1.5.0" + ], + "v": "<1.5.0" + }, + { + "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", + "cve": "CVE-2022-29204", + "id": "pyup.io-50421", + "more_info_path": "/vulnerabilities/CVE-2022-29204/50421", "specs": [ "<1.5.0" ], @@ -28846,9 +28937,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29205", - "id": "pyup.io-50422", - "more_info_path": "/vulnerabilities/CVE-2022-29205/50422", + "cve": "CVE-2022-29202", + "id": "pyup.io-50419", + "more_info_path": "/vulnerabilities/CVE-2022-29202/50419", "specs": [ "<1.5.0" ], @@ -28856,9 +28947,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29209", - "id": "pyup.io-50426", - "more_info_path": "/vulnerabilities/CVE-2022-29209/50426", + "cve": "CVE-2022-29201", + "id": "pyup.io-50418", + "more_info_path": "/vulnerabilities/CVE-2022-29201/50418", "specs": [ "<1.5.0" ], @@ -28866,9 +28957,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-30115", - "id": "pyup.io-50431", - "more_info_path": "/vulnerabilities/CVE-2022-30115/50431", + "cve": "CVE-2022-29200", + "id": "pyup.io-50417", + "more_info_path": "/vulnerabilities/CVE-2022-29200/50417", "specs": [ "<1.5.0" ], @@ -28876,9 +28967,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-22576", - "id": "pyup.io-50362", - "more_info_path": "/vulnerabilities/CVE-2022-22576/50362", + "cve": "CVE-2022-29193", + "id": "pyup.io-50410", + "more_info_path": "/vulnerabilities/CVE-2022-29193/50410", "specs": [ "<1.5.0" ], @@ -28886,9 +28977,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29208", - "id": "pyup.io-50425", - "more_info_path": "/vulnerabilities/CVE-2022-29208/50425", + "cve": "CVE-2022-29192", + "id": "pyup.io-50409", + "more_info_path": "/vulnerabilities/CVE-2022-29192/50409", "specs": [ "<1.5.0" ], @@ -28896,9 +28987,119 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23563", - "id": "pyup.io-50369", - "more_info_path": "/vulnerabilities/CVE-2022-23563/50369", + "cve": "CVE-2022-29191", + "id": "pyup.io-50408", + "more_info_path": "/vulnerabilities/CVE-2022-29191/50408", + "specs": [ + "<1.5.0" + ], + "v": "<1.5.0" + }, + { + "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", + "cve": "CVE-2022-27776", + "id": "pyup.io-50402", + "more_info_path": "/vulnerabilities/CVE-2022-27776/50402", + "specs": [ + "<1.5.0" + ], + "v": "<1.5.0" + }, + { + "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", + "cve": "CVE-2022-23595", + "id": "pyup.io-50399", + "more_info_path": "/vulnerabilities/CVE-2022-23595/50399", + "specs": [ + "<1.5.0" + ], + "v": "<1.5.0" + }, + { + "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", + "cve": "CVE-2022-23594", + "id": "pyup.io-50398", + "more_info_path": "/vulnerabilities/CVE-2022-23594/50398", + "specs": [ + "<1.5.0" + ], + "v": "<1.5.0" + }, + { + "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", + "cve": "CVE-2022-23591", + "id": "pyup.io-50397", + "more_info_path": "/vulnerabilities/CVE-2022-23591/50397", + "specs": [ + "<1.5.0" + ], + "v": "<1.5.0" + }, + { + "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", + "cve": "CVE-2022-23585", + "id": "pyup.io-50391", + "more_info_path": "/vulnerabilities/CVE-2022-23585/50391", + "specs": [ + "<1.5.0" + ], + "v": "<1.5.0" + }, + { + "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", + "cve": "CVE-2022-23583", + "id": "pyup.io-50389", + "more_info_path": "/vulnerabilities/CVE-2022-23583/50389", + "specs": [ + "<1.5.0" + ], + "v": "<1.5.0" + }, + { + "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", + "cve": "CVE-2022-23576", + "id": "pyup.io-50382", + "more_info_path": "/vulnerabilities/CVE-2022-23576/50382", + "specs": [ + "<1.5.0" + ], + "v": "<1.5.0" + }, + { + "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", + "cve": "CVE-2022-23575", + "id": "pyup.io-50381", + "more_info_path": "/vulnerabilities/CVE-2022-23575/50381", + "specs": [ + "<1.5.0" + ], + "v": "<1.5.0" + }, + { + "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", + "cve": "CVE-2022-23574", + "id": "pyup.io-50380", + "more_info_path": "/vulnerabilities/CVE-2022-23574/50380", + "specs": [ + "<1.5.0" + ], + "v": "<1.5.0" + }, + { + "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", + "cve": "CVE-2022-23573", + "id": "pyup.io-50379", + "more_info_path": "/vulnerabilities/CVE-2022-23573/50379", + "specs": [ + "<1.5.0" + ], + "v": "<1.5.0" + }, + { + "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", + "cve": "CVE-2022-23572", + "id": "pyup.io-50378", + "more_info_path": "/vulnerabilities/CVE-2022-23572/50378", "specs": [ "<1.5.0" ], @@ -28916,9 +29117,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-27778", - "id": "pyup.io-50403", - "more_info_path": "/vulnerabilities/CVE-2022-27778/50403", + "cve": "CVE-2022-23565", + "id": "pyup.io-50371", + "more_info_path": "/vulnerabilities/CVE-2022-23565/50371", "specs": [ "<1.5.0" ], @@ -28926,9 +29127,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-27780", - "id": "pyup.io-50405", - "more_info_path": "/vulnerabilities/CVE-2022-27780/50405", + "cve": "CVE-2022-23560", + "id": "pyup.io-50366", + "more_info_path": "/vulnerabilities/CVE-2022-23560/50366", "specs": [ "<1.5.0" ], @@ -28936,9 +29137,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29202", - "id": "pyup.io-50419", - "more_info_path": "/vulnerabilities/CVE-2022-29202/50419", + "cve": "CVE-2022-23558", + "id": "pyup.io-50364", + "more_info_path": "/vulnerabilities/CVE-2022-23558/50364", "specs": [ "<1.5.0" ], @@ -28946,9 +29147,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-27781", - "id": "pyup.io-50406", - "more_info_path": "/vulnerabilities/CVE-2022-27781/50406", + "cve": "CVE-2022-23557", + "id": "pyup.io-50363", + "more_info_path": "/vulnerabilities/CVE-2022-23557/50363", "specs": [ "<1.5.0" ], @@ -28956,9 +29157,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29200", - "id": "pyup.io-50417", - "more_info_path": "/vulnerabilities/CVE-2022-29200/50417", + "cve": "CVE-2022-21741", + "id": "pyup.io-50361", + "more_info_path": "/vulnerabilities/CVE-2022-21741/50361", "specs": [ "<1.5.0" ], @@ -28966,9 +29167,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29206", - "id": "pyup.io-50423", - "more_info_path": "/vulnerabilities/CVE-2022-29206/50423", + "cve": "CVE-2022-21740", + "id": "pyup.io-50360", + "more_info_path": "/vulnerabilities/CVE-2022-21740/50360", "specs": [ "<1.5.0" ], @@ -28976,9 +29177,19 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29204", - "id": "pyup.io-50421", - "more_info_path": "/vulnerabilities/CVE-2022-29204/50421", + "cve": "CVE-2022-21738", + "id": "pyup.io-50358", + "more_info_path": "/vulnerabilities/CVE-2022-21738/50358", + "specs": [ + "<1.5.0" + ], + "v": "<1.5.0" + }, + { + "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", + "cve": "CVE-2022-21729", + "id": "pyup.io-50349", + "more_info_path": "/vulnerabilities/CVE-2022-21729/50349", "specs": [ "<1.5.0" ], @@ -28996,9 +29207,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29207", - "id": "pyup.io-50424", - "more_info_path": "/vulnerabilities/CVE-2022-29207/50424", + "cve": "CVE-2022-23568", + "id": "pyup.io-50374", + "more_info_path": "/vulnerabilities/CVE-2022-23568/50374", "specs": [ "<1.5.0" ], @@ -29006,9 +29217,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23590", - "id": "pyup.io-50396", - "more_info_path": "/vulnerabilities/CVE-2022-23590/50396", + "cve": "CVE-2022-23567", + "id": "pyup.io-50373", + "more_info_path": "/vulnerabilities/CVE-2022-23567/50373", "specs": [ "<1.5.0" ], @@ -29016,9 +29227,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-29191", - "id": "pyup.io-50408", - "more_info_path": "/vulnerabilities/CVE-2022-29191/50408", + "cve": "CVE-2022-21736", + "id": "pyup.io-50356", + "more_info_path": "/vulnerabilities/CVE-2022-21736/50356", "specs": [ "<1.5.0" ], @@ -29026,9 +29237,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23557", - "id": "pyup.io-50363", - "more_info_path": "/vulnerabilities/CVE-2022-23557/50363", + "cve": "CVE-2022-21733", + "id": "pyup.io-50353", + "more_info_path": "/vulnerabilities/CVE-2022-21733/50353", "specs": [ "<1.5.0" ], @@ -29036,9 +29247,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-23558", - "id": "pyup.io-50364", - "more_info_path": "/vulnerabilities/CVE-2022-23558/50364", + "cve": "CVE-2022-21732", + "id": "pyup.io-50352", + "more_info_path": "/vulnerabilities/CVE-2022-21732/50352", "specs": [ "<1.5.0" ], @@ -29046,9 +29257,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2018-25032", - "id": "pyup.io-50343", - "more_info_path": "/vulnerabilities/CVE-2018-25032/50343", + "cve": "CVE-2022-21730", + "id": "pyup.io-50350", + "more_info_path": "/vulnerabilities/CVE-2022-21730/50350", "specs": [ "<1.5.0" ], @@ -29056,9 +29267,9 @@ }, { "advisory": "Datum 1.5.0 updates its dependency 'TensorFlow' to v2.8.1 to include security fixes.", - "cve": "CVE-2022-27776", - "id": "pyup.io-50402", - "more_info_path": "/vulnerabilities/CVE-2022-27776/50402", + "cve": "CVE-2022-21727", + "id": "pyup.io-50347", + "more_info_path": "/vulnerabilities/CVE-2022-21727/50347", "specs": [ "<1.5.0" ], @@ -29350,20 +29561,20 @@ ], "dbx": [ { - "advisory": "Dbx 0.8.16 updates its dependency 'cookiecutter' to version '2.1.1' to include a security fix.\r\nhttps://github.com/databrickslabs/dbx/pull/798", - "cve": "CVE-2022-24065", - "id": "pyup.io-59103", - "more_info_path": "/vulnerabilities/CVE-2022-24065/59103", + "advisory": "Dbx 0.8.16 updates its dependency 'cryptography' to version '41.0.1' to include a security fix.\r\nhttps://github.com/databrickslabs/dbx/pull/798", + "cve": "CVE-2023-2650", + "id": "pyup.io-59093", + "more_info_path": "/vulnerabilities/CVE-2023-2650/59093", "specs": [ "<0.8.16" ], "v": "<0.8.16" }, { - "advisory": "Dbx 0.8.16 updates its dependency 'cryptography' to version '41.0.1' to include a security fix.\r\nhttps://github.com/databrickslabs/dbx/pull/798", - "cve": "CVE-2023-2650", - "id": "pyup.io-59093", - "more_info_path": "/vulnerabilities/CVE-2023-2650/59093", + "advisory": "Dbx 0.8.16 updates its dependency 'cookiecutter' to version '2.1.1' to include a security fix.\r\nhttps://github.com/databrickslabs/dbx/pull/798", + "cve": "CVE-2022-24065", + "id": "pyup.io-59103", + "more_info_path": "/vulnerabilities/CVE-2022-24065/59103", "specs": [ "<0.8.16" ], @@ -29429,9 +29640,9 @@ }, { "advisory": "Ddataflow 1.1.8 updates its dependency 'urllib3' to v1.26.12 to include security fixes.", - "cve": "CVE-2019-11236", - "id": "pyup.io-53835", - "more_info_path": "/vulnerabilities/CVE-2019-11236/53835", + "cve": "CVE-2021-33503", + "id": "pyup.io-53822", + "more_info_path": "/vulnerabilities/CVE-2021-33503/53822", "specs": [ "<1.1.8" ], @@ -29439,9 +29650,9 @@ }, { "advisory": "Ddataflow 1.1.8 updates its dependency 'urllib3' to v1.26.12 to include security fixes.", - "cve": "CVE-2019-11324", - "id": "pyup.io-53834", - "more_info_path": "/vulnerabilities/CVE-2019-11324/53834", + "cve": "CVE-2020-26137", + "id": "pyup.io-53833", + "more_info_path": "/vulnerabilities/CVE-2020-26137/53833", "specs": [ "<1.1.8" ], @@ -29449,9 +29660,9 @@ }, { "advisory": "Ddataflow 1.1.8 updates its dependency 'urllib3' to v1.26.12 to include security fixes.", - "cve": "CVE-2021-33503", - "id": "pyup.io-53822", - "more_info_path": "/vulnerabilities/CVE-2021-33503/53822", + "cve": "CVE-2019-11236", + "id": "pyup.io-53835", + "more_info_path": "/vulnerabilities/CVE-2019-11236/53835", "specs": [ "<1.1.8" ], @@ -29459,9 +29670,9 @@ }, { "advisory": "Ddataflow 1.1.8 updates its dependency 'urllib3' to v1.26.12 to include security fixes.", - "cve": "CVE-2020-26137", - "id": "pyup.io-53833", - "more_info_path": "/vulnerabilities/CVE-2020-26137/53833", + "cve": "CVE-2019-11324", + "id": "pyup.io-53834", + "more_info_path": "/vulnerabilities/CVE-2019-11324/53834", "specs": [ "<1.1.8" ], @@ -29552,6 +29763,19 @@ "v": "<0.41" } ], + "deadiff": [ + { + "advisory": "The OpenSSF Package Analysis project has identified the PyPI package 'deadiff' version 99.6 as malicious because it communicates with a domain associated with malicious activity, indicating potential security risks.", + "cve": "PVE-2024-74238", + "id": "pyup.io-74238", + "more_info_path": "/vulnerabilities/PVE-2024-74238/74238", + "specs": [ + ">=0", + "<=0" + ], + "v": ">=0,<=0" + } + ], "debianized-jupyterhub": [ { "advisory": "Debianized-jupyterhub 0.9.5.1 updates its dependency 'notebook' to 5.7.7 to include a security fix.", @@ -29753,9 +29977,9 @@ "deepcell": [ { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37684", - "id": "pyup.io-48892", - "more_info_path": "/vulnerabilities/CVE-2021-37684/48892", + "cve": "CVE-2021-37658", + "id": "pyup.io-48866", + "more_info_path": "/vulnerabilities/CVE-2021-37658/48866", "specs": [ "<0.10.0rc1" ], @@ -29763,9 +29987,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29573", - "id": "pyup.io-48797", - "more_info_path": "/vulnerabilities/CVE-2021-29573/48797", + "cve": "CVE-2021-29512", + "id": "pyup.io-48736", + "more_info_path": "/vulnerabilities/CVE-2021-29512/48736", "specs": [ "<0.10.0rc1" ], @@ -29773,9 +29997,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29614", - "id": "pyup.io-48838", - "more_info_path": "/vulnerabilities/CVE-2021-29614/48838", + "cve": "CVE-2021-29533", + "id": "pyup.io-48757", + "more_info_path": "/vulnerabilities/CVE-2021-29533/48757", "specs": [ "<0.10.0rc1" ], @@ -29783,9 +30007,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29590", - "id": "pyup.io-48814", - "more_info_path": "/vulnerabilities/CVE-2021-29590/48814", + "cve": "CVE-2021-29532", + "id": "pyup.io-48756", + "more_info_path": "/vulnerabilities/CVE-2021-29532/48756", "specs": [ "<0.10.0rc1" ], @@ -29793,9 +30017,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29539", - "id": "pyup.io-48763", - "more_info_path": "/vulnerabilities/CVE-2021-29539/48763", + "cve": "CVE-2021-29586", + "id": "pyup.io-48810", + "more_info_path": "/vulnerabilities/CVE-2021-29586/48810", "specs": [ "<0.10.0rc1" ], @@ -29803,9 +30027,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29593", - "id": "pyup.io-48817", - "more_info_path": "/vulnerabilities/CVE-2021-29593/48817", + "cve": "CVE-2021-29581", + "id": "pyup.io-48805", + "more_info_path": "/vulnerabilities/CVE-2021-29581/48805", "specs": [ "<0.10.0rc1" ], @@ -29813,9 +30037,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29586", - "id": "pyup.io-48810", - "more_info_path": "/vulnerabilities/CVE-2021-29586/48810", + "cve": "CVE-2021-29575", + "id": "pyup.io-48799", + "more_info_path": "/vulnerabilities/CVE-2021-29575/48799", "specs": [ "<0.10.0rc1" ], @@ -29823,9 +30047,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29541", - "id": "pyup.io-48765", - "more_info_path": "/vulnerabilities/CVE-2021-29541/48765", + "cve": "CVE-2021-37649", + "id": "pyup.io-48857", + "more_info_path": "/vulnerabilities/CVE-2021-37649/48857", "specs": [ "<0.10.0rc1" ], @@ -29833,9 +30057,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29596", - "id": "pyup.io-48820", - "more_info_path": "/vulnerabilities/CVE-2021-29596/48820", + "cve": "CVE-2021-29524", + "id": "pyup.io-48748", + "more_info_path": "/vulnerabilities/CVE-2021-29524/48748", "specs": [ "<0.10.0rc1" ], @@ -29843,9 +30067,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37638", - "id": "pyup.io-48847", - "more_info_path": "/vulnerabilities/CVE-2021-37638/48847", + "cve": "CVE-2021-37654", + "id": "pyup.io-48862", + "more_info_path": "/vulnerabilities/CVE-2021-37654/48862", "specs": [ "<0.10.0rc1" ], @@ -29853,9 +30077,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29579", - "id": "pyup.io-48803", - "more_info_path": "/vulnerabilities/CVE-2021-29579/48803", + "cve": "CVE-2021-29614", + "id": "pyup.io-48838", + "more_info_path": "/vulnerabilities/CVE-2021-29614/48838", "specs": [ "<0.10.0rc1" ], @@ -29863,9 +30087,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37669", - "id": "pyup.io-48877", - "more_info_path": "/vulnerabilities/CVE-2021-37669/48877", + "cve": "CVE-2021-29608", + "id": "pyup.io-48832", + "more_info_path": "/vulnerabilities/CVE-2021-29608/48832", "specs": [ "<0.10.0rc1" ], @@ -29873,9 +30097,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29597", - "id": "pyup.io-48821", - "more_info_path": "/vulnerabilities/CVE-2021-29597/48821", + "cve": "CVE-2021-37668", + "id": "pyup.io-48876", + "more_info_path": "/vulnerabilities/CVE-2021-37668/48876", "specs": [ "<0.10.0rc1" ], @@ -29883,9 +30107,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29567", - "id": "pyup.io-48791", - "more_info_path": "/vulnerabilities/CVE-2021-29567/48791", + "cve": "CVE-2021-29599", + "id": "pyup.io-48823", + "more_info_path": "/vulnerabilities/CVE-2021-29599/48823", "specs": [ "<0.10.0rc1" ], @@ -29893,9 +30117,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37673", - "id": "pyup.io-48881", - "more_info_path": "/vulnerabilities/CVE-2021-37673/48881", + "cve": "CVE-2021-29550", + "id": "pyup.io-48774", + "more_info_path": "/vulnerabilities/CVE-2021-29550/48774", "specs": [ "<0.10.0rc1" ], @@ -29903,9 +30127,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29604", - "id": "pyup.io-48828", - "more_info_path": "/vulnerabilities/CVE-2021-29604/48828", + "cve": "CVE-2021-29549", + "id": "pyup.io-48773", + "more_info_path": "/vulnerabilities/CVE-2021-29549/48773", "specs": [ "<0.10.0rc1" ], @@ -29913,9 +30137,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37679", - "id": "pyup.io-48887", - "more_info_path": "/vulnerabilities/CVE-2021-37679/48887", + "cve": "CVE-2021-29574", + "id": "pyup.io-48798", + "more_info_path": "/vulnerabilities/CVE-2021-29574/48798", "specs": [ "<0.10.0rc1" ], @@ -29923,9 +30147,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37658", - "id": "pyup.io-48866", - "more_info_path": "/vulnerabilities/CVE-2021-37658/48866", + "cve": "CVE-2021-29560", + "id": "pyup.io-48784", + "more_info_path": "/vulnerabilities/CVE-2021-29560/48784", "specs": [ "<0.10.0rc1" ], @@ -29933,9 +30157,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29553", - "id": "pyup.io-48777", - "more_info_path": "/vulnerabilities/CVE-2021-29553/48777", + "cve": "CVE-2021-29596", + "id": "pyup.io-48820", + "more_info_path": "/vulnerabilities/CVE-2021-29596/48820", "specs": [ "<0.10.0rc1" ], @@ -29943,9 +30167,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29558", - "id": "pyup.io-48782", - "more_info_path": "/vulnerabilities/CVE-2021-29558/48782", + "cve": "CVE-2021-37691", + "id": "pyup.io-48899", + "more_info_path": "/vulnerabilities/CVE-2021-37691/48899", "specs": [ "<0.10.0rc1" ], @@ -29953,9 +30177,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37644", - "id": "pyup.io-48852", - "more_info_path": "/vulnerabilities/CVE-2021-37644/48852", + "cve": "CVE-2021-37636", + "id": "pyup.io-48845", + "more_info_path": "/vulnerabilities/CVE-2021-37636/48845", "specs": [ "<0.10.0rc1" ], @@ -29963,9 +30187,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29603", - "id": "pyup.io-48827", - "more_info_path": "/vulnerabilities/CVE-2021-29603/48827", + "cve": "CVE-2021-29552", + "id": "pyup.io-48776", + "more_info_path": "/vulnerabilities/CVE-2021-29552/48776", "specs": [ "<0.10.0rc1" ], @@ -29973,9 +30197,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37637", - "id": "pyup.io-48846", - "more_info_path": "/vulnerabilities/CVE-2021-37637/48846", + "cve": "CVE-2021-37643", + "id": "pyup.io-48851", + "more_info_path": "/vulnerabilities/CVE-2021-37643/48851", "specs": [ "<0.10.0rc1" ], @@ -29983,9 +30207,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29551", - "id": "pyup.io-48775", - "more_info_path": "/vulnerabilities/CVE-2021-29551/48775", + "cve": "CVE-2021-29589", + "id": "pyup.io-48813", + "more_info_path": "/vulnerabilities/CVE-2021-29589/48813", "specs": [ "<0.10.0rc1" ], @@ -29993,9 +30217,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29616", - "id": "pyup.io-48840", - "more_info_path": "/vulnerabilities/CVE-2021-29616/48840", + "cve": "CVE-2021-29606", + "id": "pyup.io-48830", + "more_info_path": "/vulnerabilities/CVE-2021-29606/48830", "specs": [ "<0.10.0rc1" ], @@ -30003,9 +30227,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29568", - "id": "pyup.io-48792", - "more_info_path": "/vulnerabilities/CVE-2021-29568/48792", + "cve": "CVE-2021-29588", + "id": "pyup.io-48812", + "more_info_path": "/vulnerabilities/CVE-2021-29588/48812", "specs": [ "<0.10.0rc1" ], @@ -30013,9 +30237,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29569", - "id": "pyup.io-48793", - "more_info_path": "/vulnerabilities/CVE-2021-29569/48793", + "cve": "CVE-2021-29619", + "id": "pyup.io-48843", + "more_info_path": "/vulnerabilities/CVE-2021-29619/48843", "specs": [ "<0.10.0rc1" ], @@ -30023,9 +30247,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29529", - "id": "pyup.io-48753", - "more_info_path": "/vulnerabilities/CVE-2021-29529/48753", + "cve": "CVE-2021-29563", + "id": "pyup.io-48787", + "more_info_path": "/vulnerabilities/CVE-2021-29563/48787", "specs": [ "<0.10.0rc1" ], @@ -30033,9 +30257,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29513", - "id": "pyup.io-48737", - "more_info_path": "/vulnerabilities/CVE-2021-29513/48737", + "cve": "CVE-2021-29587", + "id": "pyup.io-48811", + "more_info_path": "/vulnerabilities/CVE-2021-29587/48811", "specs": [ "<0.10.0rc1" ], @@ -30043,9 +30267,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29515", - "id": "pyup.io-48739", - "more_info_path": "/vulnerabilities/CVE-2021-29515/48739", + "cve": "CVE-2021-29605", + "id": "pyup.io-48829", + "more_info_path": "/vulnerabilities/CVE-2021-29605/48829", "specs": [ "<0.10.0rc1" ], @@ -30053,9 +30277,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29520", - "id": "pyup.io-48744", - "more_info_path": "/vulnerabilities/CVE-2021-29520/48744", + "cve": "CVE-2021-29603", + "id": "pyup.io-48827", + "more_info_path": "/vulnerabilities/CVE-2021-29603/48827", "specs": [ "<0.10.0rc1" ], @@ -30063,9 +30287,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29538", - "id": "pyup.io-48762", - "more_info_path": "/vulnerabilities/CVE-2021-29538/48762", + "cve": "CVE-2021-29618", + "id": "pyup.io-48842", + "more_info_path": "/vulnerabilities/CVE-2021-29618/48842", "specs": [ "<0.10.0rc1" ], @@ -30073,9 +30297,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29581", - "id": "pyup.io-48805", - "more_info_path": "/vulnerabilities/CVE-2021-29581/48805", + "cve": "CVE-2021-29616", + "id": "pyup.io-48840", + "more_info_path": "/vulnerabilities/CVE-2021-29616/48840", "specs": [ "<0.10.0rc1" ], @@ -30083,9 +30307,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29577", - "id": "pyup.io-48801", - "more_info_path": "/vulnerabilities/CVE-2021-29577/48801", + "cve": "CVE-2021-29610", + "id": "pyup.io-48834", + "more_info_path": "/vulnerabilities/CVE-2021-29610/48834", "specs": [ "<0.10.0rc1" ], @@ -30093,9 +30317,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29580", - "id": "pyup.io-48804", - "more_info_path": "/vulnerabilities/CVE-2021-29580/48804", + "cve": "CVE-2021-29598", + "id": "pyup.io-48822", + "more_info_path": "/vulnerabilities/CVE-2021-29598/48822", "specs": [ "<0.10.0rc1" ], @@ -30103,9 +30327,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29583", - "id": "pyup.io-48807", - "more_info_path": "/vulnerabilities/CVE-2021-29583/48807", + "cve": "CVE-2021-29604", + "id": "pyup.io-48828", + "more_info_path": "/vulnerabilities/CVE-2021-29604/48828", "specs": [ "<0.10.0rc1" ], @@ -30113,9 +30337,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29587", - "id": "pyup.io-48811", - "more_info_path": "/vulnerabilities/CVE-2021-29587/48811", + "cve": "CVE-2021-29515", + "id": "pyup.io-48739", + "more_info_path": "/vulnerabilities/CVE-2021-29515/48739", "specs": [ "<0.10.0rc1" ], @@ -30123,9 +30347,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29592", - "id": "pyup.io-48816", - "more_info_path": "/vulnerabilities/CVE-2021-29592/48816", + "cve": "CVE-2021-29577", + "id": "pyup.io-48801", + "more_info_path": "/vulnerabilities/CVE-2021-29577/48801", "specs": [ "<0.10.0rc1" ], @@ -30133,9 +30357,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37645", - "id": "pyup.io-48853", - "more_info_path": "/vulnerabilities/CVE-2021-37645/48853", + "cve": "CVE-2021-29617", + "id": "pyup.io-48841", + "more_info_path": "/vulnerabilities/CVE-2021-29617/48841", "specs": [ "<0.10.0rc1" ], @@ -30143,9 +30367,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29606", - "id": "pyup.io-48830", - "more_info_path": "/vulnerabilities/CVE-2021-29606/48830", + "cve": "CVE-2021-29611", + "id": "pyup.io-48835", + "more_info_path": "/vulnerabilities/CVE-2021-29611/48835", "specs": [ "<0.10.0rc1" ], @@ -30153,9 +30377,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29617", - "id": "pyup.io-48841", - "more_info_path": "/vulnerabilities/CVE-2021-29617/48841", + "cve": "CVE-2021-29593", + "id": "pyup.io-48817", + "more_info_path": "/vulnerabilities/CVE-2021-29593/48817", "specs": [ "<0.10.0rc1" ], @@ -30163,9 +30387,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29618", - "id": "pyup.io-48842", - "more_info_path": "/vulnerabilities/CVE-2021-29618/48842", + "cve": "CVE-2021-29592", + "id": "pyup.io-48816", + "more_info_path": "/vulnerabilities/CVE-2021-29592/48816", "specs": [ "<0.10.0rc1" ], @@ -30173,9 +30397,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37641", - "id": "pyup.io-48849", - "more_info_path": "/vulnerabilities/CVE-2021-37641/48849", + "cve": "CVE-2021-29590", + "id": "pyup.io-48814", + "more_info_path": "/vulnerabilities/CVE-2021-29590/48814", "specs": [ "<0.10.0rc1" ], @@ -30183,9 +30407,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37649", - "id": "pyup.io-48857", - "more_info_path": "/vulnerabilities/CVE-2021-37649/48857", + "cve": "CVE-2021-37655", + "id": "pyup.io-48863", + "more_info_path": "/vulnerabilities/CVE-2021-37655/48863", "specs": [ "<0.10.0rc1" ], @@ -30193,9 +30417,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37664", - "id": "pyup.io-48872", - "more_info_path": "/vulnerabilities/CVE-2021-37664/48872", + "cve": "CVE-2021-29609", + "id": "pyup.io-48833", + "more_info_path": "/vulnerabilities/CVE-2021-29609/48833", "specs": [ "<0.10.0rc1" ], @@ -30203,9 +30427,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29560", - "id": "pyup.io-48784", - "more_info_path": "/vulnerabilities/CVE-2021-29560/48784", + "cve": "CVE-2021-29597", + "id": "pyup.io-48821", + "more_info_path": "/vulnerabilities/CVE-2021-29597/48821", "specs": [ "<0.10.0rc1" ], @@ -30213,9 +30437,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29574", - "id": "pyup.io-48798", - "more_info_path": "/vulnerabilities/CVE-2021-29574/48798", + "cve": "CVE-2020-8285", + "id": "pyup.io-48730", + "more_info_path": "/vulnerabilities/CVE-2020-8285/48730", "specs": [ "<0.10.0rc1" ], @@ -30223,9 +30447,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29571", - "id": "pyup.io-48795", - "more_info_path": "/vulnerabilities/CVE-2021-29571/48795", + "cve": "CVE-2021-37651", + "id": "pyup.io-48859", + "more_info_path": "/vulnerabilities/CVE-2021-37651/48859", "specs": [ "<0.10.0rc1" ], @@ -30233,9 +30457,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29526", - "id": "pyup.io-48750", - "more_info_path": "/vulnerabilities/CVE-2021-29526/48750", + "cve": "CVE-2021-29566", + "id": "pyup.io-48790", + "more_info_path": "/vulnerabilities/CVE-2021-29566/48790", "specs": [ "<0.10.0rc1" ], @@ -30243,9 +30467,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29517", - "id": "pyup.io-48741", - "more_info_path": "/vulnerabilities/CVE-2021-29517/48741", + "cve": "CVE-2021-29583", + "id": "pyup.io-48807", + "more_info_path": "/vulnerabilities/CVE-2021-29583/48807", "specs": [ "<0.10.0rc1" ], @@ -30253,9 +30477,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29549", - "id": "pyup.io-48773", - "more_info_path": "/vulnerabilities/CVE-2021-29549/48773", + "cve": "CVE-2021-29580", + "id": "pyup.io-48804", + "more_info_path": "/vulnerabilities/CVE-2021-29580/48804", "specs": [ "<0.10.0rc1" ], @@ -30263,9 +30487,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37651", - "id": "pyup.io-48859", - "more_info_path": "/vulnerabilities/CVE-2021-37651/48859", + "cve": "CVE-2021-29579", + "id": "pyup.io-48803", + "more_info_path": "/vulnerabilities/CVE-2021-29579/48803", "specs": [ "<0.10.0rc1" ], @@ -30273,9 +30497,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29547", - "id": "pyup.io-48771", - "more_info_path": "/vulnerabilities/CVE-2021-29547/48771", + "cve": "CVE-2021-29571", + "id": "pyup.io-48795", + "more_info_path": "/vulnerabilities/CVE-2021-29571/48795", "specs": [ "<0.10.0rc1" ], @@ -30283,9 +30507,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37672", - "id": "pyup.io-48880", - "more_info_path": "/vulnerabilities/CVE-2021-37672/48880", + "cve": "CVE-2021-29570", + "id": "pyup.io-48794", + "more_info_path": "/vulnerabilities/CVE-2021-29570/48794", "specs": [ "<0.10.0rc1" ], @@ -30293,9 +30517,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37659", - "id": "pyup.io-48867", - "more_info_path": "/vulnerabilities/CVE-2021-37659/48867", + "cve": "CVE-2021-29569", + "id": "pyup.io-48793", + "more_info_path": "/vulnerabilities/CVE-2021-29569/48793", "specs": [ "<0.10.0rc1" ], @@ -30303,9 +30527,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29559", - "id": "pyup.io-48783", - "more_info_path": "/vulnerabilities/CVE-2021-29559/48783", + "cve": "CVE-2021-29572", + "id": "pyup.io-48796", + "more_info_path": "/vulnerabilities/CVE-2021-29572/48796", "specs": [ "<0.10.0rc1" ], @@ -30313,9 +30537,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2020-8285", - "id": "pyup.io-48730", - "more_info_path": "/vulnerabilities/CVE-2020-8285/48730", + "cve": "CVE-2021-29568", + "id": "pyup.io-48792", + "more_info_path": "/vulnerabilities/CVE-2021-29568/48792", "specs": [ "<0.10.0rc1" ], @@ -30323,9 +30547,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29566", - "id": "pyup.io-48790", - "more_info_path": "/vulnerabilities/CVE-2021-29566/48790", + "cve": "CVE-2021-37681", + "id": "pyup.io-48889", + "more_info_path": "/vulnerabilities/CVE-2021-37681/48889", "specs": [ "<0.10.0rc1" ], @@ -30333,9 +30557,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37661", - "id": "pyup.io-48869", - "more_info_path": "/vulnerabilities/CVE-2021-37661/48869", + "cve": "CVE-2021-29567", + "id": "pyup.io-48791", + "more_info_path": "/vulnerabilities/CVE-2021-29567/48791", "specs": [ "<0.10.0rc1" ], @@ -30343,9 +30567,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29516", - "id": "pyup.io-48740", - "more_info_path": "/vulnerabilities/CVE-2021-29516/48740", + "cve": "CVE-2021-29562", + "id": "pyup.io-48786", + "more_info_path": "/vulnerabilities/CVE-2021-29562/48786", "specs": [ "<0.10.0rc1" ], @@ -30353,9 +30577,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29533", - "id": "pyup.io-48757", - "more_info_path": "/vulnerabilities/CVE-2021-29533/48757", + "cve": "CVE-2021-29559", + "id": "pyup.io-48783", + "more_info_path": "/vulnerabilities/CVE-2021-29559/48783", "specs": [ "<0.10.0rc1" ], @@ -30363,9 +30587,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37674", - "id": "pyup.io-48882", - "more_info_path": "/vulnerabilities/CVE-2021-37674/48882", + "cve": "CVE-2021-29558", + "id": "pyup.io-48782", + "more_info_path": "/vulnerabilities/CVE-2021-29558/48782", "specs": [ "<0.10.0rc1" ], @@ -30373,9 +30597,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29550", - "id": "pyup.io-48774", - "more_info_path": "/vulnerabilities/CVE-2021-29550/48774", + "cve": "CVE-2021-29556", + "id": "pyup.io-48780", + "more_info_path": "/vulnerabilities/CVE-2021-29556/48780", "specs": [ "<0.10.0rc1" ], @@ -30383,9 +30607,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29555", - "id": "pyup.io-48779", - "more_info_path": "/vulnerabilities/CVE-2021-29555/48779", + "cve": "CVE-2021-29551", + "id": "pyup.io-48775", + "more_info_path": "/vulnerabilities/CVE-2021-29551/48775", "specs": [ "<0.10.0rc1" ], @@ -30393,9 +30617,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29605", - "id": "pyup.io-48829", - "more_info_path": "/vulnerabilities/CVE-2021-29605/48829", + "cve": "CVE-2020-8286", + "id": "pyup.io-48731", + "more_info_path": "/vulnerabilities/CVE-2020-8286/48731", "specs": [ "<0.10.0rc1" ], @@ -30403,9 +30627,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37642", - "id": "pyup.io-48850", - "more_info_path": "/vulnerabilities/CVE-2021-37642/48850", + "cve": "CVE-2021-29553", + "id": "pyup.io-48777", + "more_info_path": "/vulnerabilities/CVE-2021-29553/48777", "specs": [ "<0.10.0rc1" ], @@ -30413,9 +30637,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37650", - "id": "pyup.io-48858", - "more_info_path": "/vulnerabilities/CVE-2021-37650/48858", + "cve": "CVE-2021-37663", + "id": "pyup.io-48871", + "more_info_path": "/vulnerabilities/CVE-2021-37663/48871", "specs": [ "<0.10.0rc1" ], @@ -30423,9 +30647,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37675", - "id": "pyup.io-48883", - "more_info_path": "/vulnerabilities/CVE-2021-37675/48883", + "cve": "CVE-2021-29543", + "id": "pyup.io-48767", + "more_info_path": "/vulnerabilities/CVE-2021-29543/48767", "specs": [ "<0.10.0rc1" ], @@ -30433,9 +30657,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37678", - "id": "pyup.io-48886", - "more_info_path": "/vulnerabilities/CVE-2021-37678/48886", + "cve": "CVE-2021-29542", + "id": "pyup.io-48766", + "more_info_path": "/vulnerabilities/CVE-2021-29542/48766", "specs": [ "<0.10.0rc1" ], @@ -30443,9 +30667,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37681", - "id": "pyup.io-48889", - "more_info_path": "/vulnerabilities/CVE-2021-37681/48889", + "cve": "CVE-2021-29541", + "id": "pyup.io-48765", + "more_info_path": "/vulnerabilities/CVE-2021-29541/48765", "specs": [ "<0.10.0rc1" ], @@ -30453,9 +30677,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29531", - "id": "pyup.io-48755", - "more_info_path": "/vulnerabilities/CVE-2021-29531/48755", + "cve": "CVE-2021-29546", + "id": "pyup.io-48770", + "more_info_path": "/vulnerabilities/CVE-2021-29546/48770", "specs": [ "<0.10.0rc1" ], @@ -30463,9 +30687,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29535", - "id": "pyup.io-48759", - "more_info_path": "/vulnerabilities/CVE-2021-29535/48759", + "cve": "CVE-2021-29534", + "id": "pyup.io-48758", + "more_info_path": "/vulnerabilities/CVE-2021-29534/48758", "specs": [ "<0.10.0rc1" ], @@ -30473,9 +30697,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2020-8286", - "id": "pyup.io-48731", - "more_info_path": "/vulnerabilities/CVE-2020-8286/48731", + "cve": "CVE-2021-29535", + "id": "pyup.io-48759", + "more_info_path": "/vulnerabilities/CVE-2021-29535/48759", "specs": [ "<0.10.0rc1" ], @@ -30483,9 +30707,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37663", - "id": "pyup.io-48871", - "more_info_path": "/vulnerabilities/CVE-2021-37663/48871", + "cve": "CVE-2021-29612", + "id": "pyup.io-48836", + "more_info_path": "/vulnerabilities/CVE-2021-29612/48836", "specs": [ "<0.10.0rc1" ], @@ -30493,9 +30717,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37635", - "id": "pyup.io-48844", - "more_info_path": "/vulnerabilities/CVE-2021-37635/48844", + "cve": "CVE-2021-29585", + "id": "pyup.io-48809", + "more_info_path": "/vulnerabilities/CVE-2021-29585/48809", "specs": [ "<0.10.0rc1" ], @@ -30503,9 +30727,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37643", - "id": "pyup.io-48851", - "more_info_path": "/vulnerabilities/CVE-2021-37643/48851", + "cve": "CVE-2021-29600", + "id": "pyup.io-48824", + "more_info_path": "/vulnerabilities/CVE-2021-29600/48824", "specs": [ "<0.10.0rc1" ], @@ -30513,9 +30737,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29575", - "id": "pyup.io-48799", - "more_info_path": "/vulnerabilities/CVE-2021-29575/48799", + "cve": "CVE-2021-37647", + "id": "pyup.io-48855", + "more_info_path": "/vulnerabilities/CVE-2021-37647/48855", "specs": [ "<0.10.0rc1" ], @@ -30523,9 +30747,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37655", - "id": "pyup.io-48863", - "more_info_path": "/vulnerabilities/CVE-2021-37655/48863", + "cve": "CVE-2021-37656", + "id": "pyup.io-48864", + "more_info_path": "/vulnerabilities/CVE-2021-37656/48864", "specs": [ "<0.10.0rc1" ], @@ -30533,9 +30757,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29572", - "id": "pyup.io-48796", - "more_info_path": "/vulnerabilities/CVE-2021-29572/48796", + "cve": "CVE-2021-37689", + "id": "pyup.io-48897", + "more_info_path": "/vulnerabilities/CVE-2021-37689/48897", "specs": [ "<0.10.0rc1" ], @@ -30543,9 +30767,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29612", - "id": "pyup.io-48836", - "more_info_path": "/vulnerabilities/CVE-2021-29612/48836", + "cve": "CVE-2021-29561", + "id": "pyup.io-48785", + "more_info_path": "/vulnerabilities/CVE-2021-29561/48785", "specs": [ "<0.10.0rc1" ], @@ -30553,9 +30777,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37676", - "id": "pyup.io-48884", - "more_info_path": "/vulnerabilities/CVE-2021-37676/48884", + "cve": "CVE-2021-29531", + "id": "pyup.io-48755", + "more_info_path": "/vulnerabilities/CVE-2021-29531/48755", "specs": [ "<0.10.0rc1" ], @@ -30563,9 +30787,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29585", - "id": "pyup.io-48809", - "more_info_path": "/vulnerabilities/CVE-2021-29585/48809", + "cve": "CVE-2021-29530", + "id": "pyup.io-48754", + "more_info_path": "/vulnerabilities/CVE-2021-29530/48754", "specs": [ "<0.10.0rc1" ], @@ -30573,9 +30797,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29528", - "id": "pyup.io-48752", - "more_info_path": "/vulnerabilities/CVE-2021-29528/48752", + "cve": "CVE-2021-29540", + "id": "pyup.io-48764", + "more_info_path": "/vulnerabilities/CVE-2021-29540/48764", "specs": [ "<0.10.0rc1" ], @@ -30583,9 +30807,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29600", - "id": "pyup.io-48824", - "more_info_path": "/vulnerabilities/CVE-2021-29600/48824", + "cve": "CVE-2021-29529", + "id": "pyup.io-48753", + "more_info_path": "/vulnerabilities/CVE-2021-29529/48753", "specs": [ "<0.10.0rc1" ], @@ -30593,9 +30817,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29512", - "id": "pyup.io-48736", - "more_info_path": "/vulnerabilities/CVE-2021-29512/48736", + "cve": "CVE-2021-29528", + "id": "pyup.io-48752", + "more_info_path": "/vulnerabilities/CVE-2021-29528/48752", "specs": [ "<0.10.0rc1" ], @@ -30603,9 +30827,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29609", - "id": "pyup.io-48833", - "more_info_path": "/vulnerabilities/CVE-2021-29609/48833", + "cve": "CVE-2021-29538", + "id": "pyup.io-48762", + "more_info_path": "/vulnerabilities/CVE-2021-29538/48762", "specs": [ "<0.10.0rc1" ], @@ -30613,9 +30837,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29619", - "id": "pyup.io-48843", - "more_info_path": "/vulnerabilities/CVE-2021-29619/48843", + "cve": "CVE-2021-29525", + "id": "pyup.io-48749", + "more_info_path": "/vulnerabilities/CVE-2021-29525/48749", "specs": [ "<0.10.0rc1" ], @@ -30623,9 +30847,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29542", - "id": "pyup.io-48766", - "more_info_path": "/vulnerabilities/CVE-2021-29542/48766", + "cve": "CVE-2021-29522", + "id": "pyup.io-48746", + "more_info_path": "/vulnerabilities/CVE-2021-29522/48746", "specs": [ "<0.10.0rc1" ], @@ -30633,9 +30857,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29552", - "id": "pyup.io-48776", - "more_info_path": "/vulnerabilities/CVE-2021-29552/48776", + "cve": "CVE-2021-29521", + "id": "pyup.io-48745", + "more_info_path": "/vulnerabilities/CVE-2021-29521/48745", "specs": [ "<0.10.0rc1" ], @@ -30653,9 +30877,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29570", - "id": "pyup.io-48794", - "more_info_path": "/vulnerabilities/CVE-2021-29570/48794", + "cve": "CVE-2021-29537", + "id": "pyup.io-48761", + "more_info_path": "/vulnerabilities/CVE-2021-29537/48761", "specs": [ "<0.10.0rc1" ], @@ -30663,9 +30887,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37646", - "id": "pyup.io-48854", - "more_info_path": "/vulnerabilities/CVE-2021-37646/48854", + "cve": "CVE-2021-29514", + "id": "pyup.io-48738", + "more_info_path": "/vulnerabilities/CVE-2021-29514/48738", "specs": [ "<0.10.0rc1" ], @@ -30673,9 +30897,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37647", - "id": "pyup.io-48855", - "more_info_path": "/vulnerabilities/CVE-2021-37647/48855", + "cve": "CVE-2021-29518", + "id": "pyup.io-48742", + "more_info_path": "/vulnerabilities/CVE-2021-29518/48742", "specs": [ "<0.10.0rc1" ], @@ -30683,9 +30907,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37668", - "id": "pyup.io-48876", - "more_info_path": "/vulnerabilities/CVE-2021-37668/48876", + "cve": "CVE-2021-29516", + "id": "pyup.io-48740", + "more_info_path": "/vulnerabilities/CVE-2021-29516/48740", "specs": [ "<0.10.0rc1" ], @@ -30693,9 +30917,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37654", - "id": "pyup.io-48862", - "more_info_path": "/vulnerabilities/CVE-2021-37654/48862", + "cve": "CVE-2021-29526", + "id": "pyup.io-48750", + "more_info_path": "/vulnerabilities/CVE-2021-29526/48750", "specs": [ "<0.10.0rc1" ], @@ -30703,9 +30927,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37656", - "id": "pyup.io-48864", - "more_info_path": "/vulnerabilities/CVE-2021-37656/48864", + "cve": "CVE-2021-29513", + "id": "pyup.io-48737", + "more_info_path": "/vulnerabilities/CVE-2021-29513/48737", "specs": [ "<0.10.0rc1" ], @@ -30713,9 +30937,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37689", - "id": "pyup.io-48897", - "more_info_path": "/vulnerabilities/CVE-2021-37689/48897", + "cve": "CVE-2021-29520", + "id": "pyup.io-48744", + "more_info_path": "/vulnerabilities/CVE-2021-29520/48744", "specs": [ "<0.10.0rc1" ], @@ -30723,9 +30947,19 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37667", - "id": "pyup.io-48875", - "more_info_path": "/vulnerabilities/CVE-2021-37667/48875", + "cve": "CVE-2021-29519", + "id": "pyup.io-48743", + "more_info_path": "/vulnerabilities/CVE-2021-29519/48743", + "specs": [ + "<0.10.0rc1" + ], + "v": "<0.10.0rc1" + }, + { + "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", + "cve": "CVE-2021-29613", + "id": "pyup.io-48837", + "more_info_path": "/vulnerabilities/CVE-2021-29613/48837", "specs": [ "<0.10.0rc1" ], @@ -30743,9 +30977,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37648", - "id": "pyup.io-48856", - "more_info_path": "/vulnerabilities/CVE-2021-37648/48856", + "cve": "CVE-2021-37671", + "id": "pyup.io-48879", + "more_info_path": "/vulnerabilities/CVE-2021-37671/48879", "specs": [ "<0.10.0rc1" ], @@ -30753,9 +30987,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29613", - "id": "pyup.io-48837", - "more_info_path": "/vulnerabilities/CVE-2021-29613/48837", + "cve": "CVE-2021-29555", + "id": "pyup.io-48779", + "more_info_path": "/vulnerabilities/CVE-2021-29555/48779", "specs": [ "<0.10.0rc1" ], @@ -30763,9 +30997,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29537", - "id": "pyup.io-48761", - "more_info_path": "/vulnerabilities/CVE-2021-29537/48761", + "cve": "CVE-2021-37682", + "id": "pyup.io-48890", + "more_info_path": "/vulnerabilities/CVE-2021-37682/48890", "specs": [ "<0.10.0rc1" ], @@ -30773,9 +31007,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29514", - "id": "pyup.io-48738", - "more_info_path": "/vulnerabilities/CVE-2021-29514/48738", + "cve": "CVE-2021-37657", + "id": "pyup.io-48865", + "more_info_path": "/vulnerabilities/CVE-2021-37657/48865", "specs": [ "<0.10.0rc1" ], @@ -30783,9 +31017,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29540", - "id": "pyup.io-48764", - "more_info_path": "/vulnerabilities/CVE-2021-29540/48764", + "cve": "CVE-2021-29557", + "id": "pyup.io-48781", + "more_info_path": "/vulnerabilities/CVE-2021-29557/48781", "specs": [ "<0.10.0rc1" ], @@ -30793,9 +31027,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37671", - "id": "pyup.io-48879", - "more_info_path": "/vulnerabilities/CVE-2021-37671/48879", + "cve": "CVE-2021-37690", + "id": "pyup.io-48898", + "more_info_path": "/vulnerabilities/CVE-2021-37690/48898", "specs": [ "<0.10.0rc1" ], @@ -30803,9 +31037,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29518", - "id": "pyup.io-48742", - "more_info_path": "/vulnerabilities/CVE-2021-29518/48742", + "cve": "CVE-2021-29601", + "id": "pyup.io-48825", + "more_info_path": "/vulnerabilities/CVE-2021-29601/48825", "specs": [ "<0.10.0rc1" ], @@ -30813,9 +31047,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37677", - "id": "pyup.io-48885", - "more_info_path": "/vulnerabilities/CVE-2021-37677/48885", + "cve": "CVE-2021-29536", + "id": "pyup.io-48760", + "more_info_path": "/vulnerabilities/CVE-2021-29536/48760", "specs": [ "<0.10.0rc1" ], @@ -30823,9 +31057,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37657", - "id": "pyup.io-48865", - "more_info_path": "/vulnerabilities/CVE-2021-37657/48865", + "cve": "CVE-2021-29554", + "id": "pyup.io-48778", + "more_info_path": "/vulnerabilities/CVE-2021-29554/48778", "specs": [ "<0.10.0rc1" ], @@ -30833,9 +31067,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29588", - "id": "pyup.io-48812", - "more_info_path": "/vulnerabilities/CVE-2021-29588/48812", + "cve": "CVE-2021-29527", + "id": "pyup.io-48751", + "more_info_path": "/vulnerabilities/CVE-2021-29527/48751", "specs": [ "<0.10.0rc1" ], @@ -30843,9 +31077,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37680", - "id": "pyup.io-48888", - "more_info_path": "/vulnerabilities/CVE-2021-37680/48888", + "cve": "CVE-2021-37639", + "id": "pyup.io-48848", + "more_info_path": "/vulnerabilities/CVE-2021-37639/48848", "specs": [ "<0.10.0rc1" ], @@ -30853,9 +31087,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37686", - "id": "pyup.io-48894", - "more_info_path": "/vulnerabilities/CVE-2021-37686/48894", + "cve": "CVE-2021-37674", + "id": "pyup.io-48882", + "more_info_path": "/vulnerabilities/CVE-2021-37674/48882", "specs": [ "<0.10.0rc1" ], @@ -30863,9 +31097,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29563", - "id": "pyup.io-48787", - "more_info_path": "/vulnerabilities/CVE-2021-29563/48787", + "cve": "CVE-2021-37672", + "id": "pyup.io-48880", + "more_info_path": "/vulnerabilities/CVE-2021-37672/48880", "specs": [ "<0.10.0rc1" ], @@ -30873,9 +31107,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29598", - "id": "pyup.io-48822", - "more_info_path": "/vulnerabilities/CVE-2021-29598/48822", + "cve": "CVE-2021-29595", + "id": "pyup.io-48819", + "more_info_path": "/vulnerabilities/CVE-2021-29595/48819", "specs": [ "<0.10.0rc1" ], @@ -30883,9 +31117,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29557", - "id": "pyup.io-48781", - "more_info_path": "/vulnerabilities/CVE-2021-29557/48781", + "cve": "CVE-2021-29578", + "id": "pyup.io-48802", + "more_info_path": "/vulnerabilities/CVE-2021-29578/48802", "specs": [ "<0.10.0rc1" ], @@ -30893,9 +31127,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29543", - "id": "pyup.io-48767", - "more_info_path": "/vulnerabilities/CVE-2021-29543/48767", + "cve": "CVE-2021-29523", + "id": "pyup.io-48747", + "more_info_path": "/vulnerabilities/CVE-2021-29523/48747", "specs": [ "<0.10.0rc1" ], @@ -30903,9 +31137,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29610", - "id": "pyup.io-48834", - "more_info_path": "/vulnerabilities/CVE-2021-29610/48834", + "cve": "CVE-2021-29584", + "id": "pyup.io-48808", + "more_info_path": "/vulnerabilities/CVE-2021-29584/48808", "specs": [ "<0.10.0rc1" ], @@ -30913,9 +31147,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37682", - "id": "pyup.io-48890", - "more_info_path": "/vulnerabilities/CVE-2021-37682/48890", + "cve": "CVE-2021-29582", + "id": "pyup.io-48806", + "more_info_path": "/vulnerabilities/CVE-2021-29582/48806", "specs": [ "<0.10.0rc1" ], @@ -30923,9 +31157,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37690", - "id": "pyup.io-48898", - "more_info_path": "/vulnerabilities/CVE-2021-37690/48898", + "cve": "CVE-2021-29576", + "id": "pyup.io-48800", + "more_info_path": "/vulnerabilities/CVE-2021-29576/48800", "specs": [ "<0.10.0rc1" ], @@ -30933,9 +31167,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37636", - "id": "pyup.io-48845", - "more_info_path": "/vulnerabilities/CVE-2021-37636/48845", + "cve": "CVE-2021-37662", + "id": "pyup.io-48870", + "more_info_path": "/vulnerabilities/CVE-2021-37662/48870", "specs": [ "<0.10.0rc1" ], @@ -30943,9 +31177,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37691", - "id": "pyup.io-48899", - "more_info_path": "/vulnerabilities/CVE-2021-37691/48899", + "cve": "CVE-2020-8177", + "id": "pyup.io-48727", + "more_info_path": "/vulnerabilities/CVE-2020-8177/48727", "specs": [ "<0.10.0rc1" ], @@ -30953,9 +31187,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29601", - "id": "pyup.io-48825", - "more_info_path": "/vulnerabilities/CVE-2021-29601/48825", + "cve": "CVE-2021-29545", + "id": "pyup.io-48769", + "more_info_path": "/vulnerabilities/CVE-2021-29545/48769", "specs": [ "<0.10.0rc1" ], @@ -30963,9 +31197,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29536", - "id": "pyup.io-48760", - "more_info_path": "/vulnerabilities/CVE-2021-29536/48760", + "cve": "CVE-2021-29607", + "id": "pyup.io-48831", + "more_info_path": "/vulnerabilities/CVE-2021-29607/48831", "specs": [ "<0.10.0rc1" ], @@ -30973,9 +31207,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37687", - "id": "pyup.io-48895", - "more_info_path": "/vulnerabilities/CVE-2021-37687/48895", + "cve": "CVE-2021-37670", + "id": "pyup.io-48878", + "more_info_path": "/vulnerabilities/CVE-2021-37670/48878", "specs": [ "<0.10.0rc1" ], @@ -30983,9 +31217,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29554", - "id": "pyup.io-48778", - "more_info_path": "/vulnerabilities/CVE-2021-29554/48778", + "cve": "CVE-2021-22898", + "id": "pyup.io-48734", + "more_info_path": "/vulnerabilities/CVE-2021-22898/48734", "specs": [ "<0.10.0rc1" ], @@ -30993,9 +31227,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29527", - "id": "pyup.io-48751", - "more_info_path": "/vulnerabilities/CVE-2021-29527/48751", + "cve": "CVE-2021-29548", + "id": "pyup.io-48772", + "more_info_path": "/vulnerabilities/CVE-2021-29548/48772", "specs": [ "<0.10.0rc1" ], @@ -31003,9 +31237,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29611", - "id": "pyup.io-48835", - "more_info_path": "/vulnerabilities/CVE-2021-29611/48835", + "cve": "CVE-2021-29594", + "id": "pyup.io-48818", + "more_info_path": "/vulnerabilities/CVE-2021-29594/48818", "specs": [ "<0.10.0rc1" ], @@ -31013,9 +31247,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37639", - "id": "pyup.io-48848", - "more_info_path": "/vulnerabilities/CVE-2021-37639/48848", + "cve": "CVE-2021-22897", + "id": "pyup.io-48733", + "more_info_path": "/vulnerabilities/CVE-2021-22897/48733", "specs": [ "<0.10.0rc1" ], @@ -31023,9 +31257,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29525", - "id": "pyup.io-48749", - "more_info_path": "/vulnerabilities/CVE-2021-29525/48749", + "cve": "CVE-2021-29615", + "id": "pyup.io-48839", + "more_info_path": "/vulnerabilities/CVE-2021-29615/48839", "specs": [ "<0.10.0rc1" ], @@ -31033,9 +31267,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-22898", - "id": "pyup.io-48734", - "more_info_path": "/vulnerabilities/CVE-2021-22898/48734", + "cve": "CVE-2021-29591", + "id": "pyup.io-48815", + "more_info_path": "/vulnerabilities/CVE-2021-29591/48815", "specs": [ "<0.10.0rc1" ], @@ -31043,9 +31277,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37688", - "id": "pyup.io-48896", - "more_info_path": "/vulnerabilities/CVE-2021-37688/48896", + "cve": "CVE-2021-37660", + "id": "pyup.io-48868", + "more_info_path": "/vulnerabilities/CVE-2021-37660/48868", "specs": [ "<0.10.0rc1" ], @@ -31053,9 +31287,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29595", - "id": "pyup.io-48819", - "more_info_path": "/vulnerabilities/CVE-2021-29595/48819", + "cve": "CVE-2020-8169", + "id": "pyup.io-48723", + "more_info_path": "/vulnerabilities/CVE-2020-8169/48723", "specs": [ "<0.10.0rc1" ], @@ -31063,9 +31297,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29599", - "id": "pyup.io-48823", - "more_info_path": "/vulnerabilities/CVE-2021-29599/48823", + "cve": "CVE-2021-22876", + "id": "pyup.io-48732", + "more_info_path": "/vulnerabilities/CVE-2021-22876/48732", "specs": [ "<0.10.0rc1" ], @@ -31073,9 +31307,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29532", - "id": "pyup.io-48756", - "more_info_path": "/vulnerabilities/CVE-2021-29532/48756", + "cve": "CVE-2021-37686", + "id": "pyup.io-48894", + "more_info_path": "/vulnerabilities/CVE-2021-37686/48894", "specs": [ "<0.10.0rc1" ], @@ -31083,9 +31317,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29548", - "id": "pyup.io-48772", - "more_info_path": "/vulnerabilities/CVE-2021-29548/48772", + "cve": "CVE-2021-37666", + "id": "pyup.io-48874", + "more_info_path": "/vulnerabilities/CVE-2021-37666/48874", "specs": [ "<0.10.0rc1" ], @@ -31093,9 +31327,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29524", - "id": "pyup.io-48748", - "more_info_path": "/vulnerabilities/CVE-2021-29524/48748", + "cve": "CVE-2020-8231", + "id": "pyup.io-48728", + "more_info_path": "/vulnerabilities/CVE-2020-8231/48728", "specs": [ "<0.10.0rc1" ], @@ -31103,9 +31337,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29561", - "id": "pyup.io-48785", - "more_info_path": "/vulnerabilities/CVE-2021-29561/48785", + "cve": "CVE-2021-37646", + "id": "pyup.io-48854", + "more_info_path": "/vulnerabilities/CVE-2021-37646/48854", "specs": [ "<0.10.0rc1" ], @@ -31113,9 +31347,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29608", - "id": "pyup.io-48832", - "more_info_path": "/vulnerabilities/CVE-2021-29608/48832", + "cve": "CVE-2020-8284", + "id": "pyup.io-48729", + "more_info_path": "/vulnerabilities/CVE-2020-8284/48729", "specs": [ "<0.10.0rc1" ], @@ -31123,9 +31357,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29521", - "id": "pyup.io-48745", - "more_info_path": "/vulnerabilities/CVE-2021-29521/48745", + "cve": "CVE-2021-22901", + "id": "pyup.io-48735", + "more_info_path": "/vulnerabilities/CVE-2021-22901/48735", "specs": [ "<0.10.0rc1" ], @@ -31133,9 +31367,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29578", - "id": "pyup.io-48802", - "more_info_path": "/vulnerabilities/CVE-2021-29578/48802", + "cve": "CVE-2021-29564", + "id": "pyup.io-48788", + "more_info_path": "/vulnerabilities/CVE-2021-29564/48788", "specs": [ "<0.10.0rc1" ], @@ -31143,9 +31377,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37683", - "id": "pyup.io-48891", - "more_info_path": "/vulnerabilities/CVE-2021-37683/48891", + "cve": "CVE-2021-37678", + "id": "pyup.io-48886", + "more_info_path": "/vulnerabilities/CVE-2021-37678/48886", "specs": [ "<0.10.0rc1" ], @@ -31153,9 +31387,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29594", - "id": "pyup.io-48818", - "more_info_path": "/vulnerabilities/CVE-2021-29594/48818", + "cve": "CVE-2021-29547", + "id": "pyup.io-48771", + "more_info_path": "/vulnerabilities/CVE-2021-29547/48771", "specs": [ "<0.10.0rc1" ], @@ -31163,9 +31397,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29546", - "id": "pyup.io-48770", - "more_info_path": "/vulnerabilities/CVE-2021-29546/48770", + "cve": "CVE-2021-37669", + "id": "pyup.io-48877", + "more_info_path": "/vulnerabilities/CVE-2021-37669/48877", "specs": [ "<0.10.0rc1" ], @@ -31173,9 +31407,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29564", - "id": "pyup.io-48788", - "more_info_path": "/vulnerabilities/CVE-2021-29564/48788", + "cve": "CVE-2021-29544", + "id": "pyup.io-48768", + "more_info_path": "/vulnerabilities/CVE-2021-29544/48768", "specs": [ "<0.10.0rc1" ], @@ -31183,9 +31417,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29523", - "id": "pyup.io-48747", - "more_info_path": "/vulnerabilities/CVE-2021-29523/48747", + "cve": "CVE-2021-37650", + "id": "pyup.io-48858", + "more_info_path": "/vulnerabilities/CVE-2021-37650/48858", "specs": [ "<0.10.0rc1" ], @@ -31193,9 +31427,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29562", - "id": "pyup.io-48786", - "more_info_path": "/vulnerabilities/CVE-2021-29562/48786", + "cve": "CVE-2021-37677", + "id": "pyup.io-48885", + "more_info_path": "/vulnerabilities/CVE-2021-37677/48885", "specs": [ "<0.10.0rc1" ], @@ -31203,9 +31437,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29582", - "id": "pyup.io-48806", - "more_info_path": "/vulnerabilities/CVE-2021-29582/48806", + "cve": "CVE-2021-29517", + "id": "pyup.io-48741", + "more_info_path": "/vulnerabilities/CVE-2021-29517/48741", "specs": [ "<0.10.0rc1" ], @@ -31213,9 +31447,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29584", - "id": "pyup.io-48808", - "more_info_path": "/vulnerabilities/CVE-2021-29584/48808", + "cve": "CVE-2021-37687", + "id": "pyup.io-48895", + "more_info_path": "/vulnerabilities/CVE-2021-37687/48895", "specs": [ "<0.10.0rc1" ], @@ -31233,9 +31467,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37666", - "id": "pyup.io-48874", - "more_info_path": "/vulnerabilities/CVE-2021-37666/48874", + "cve": "CVE-2021-37642", + "id": "pyup.io-48850", + "more_info_path": "/vulnerabilities/CVE-2021-37642/48850", "specs": [ "<0.10.0rc1" ], @@ -31243,9 +31477,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29519", - "id": "pyup.io-48743", - "more_info_path": "/vulnerabilities/CVE-2021-29519/48743", + "cve": "CVE-2021-37675", + "id": "pyup.io-48883", + "more_info_path": "/vulnerabilities/CVE-2021-37675/48883", "specs": [ "<0.10.0rc1" ], @@ -31253,9 +31487,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29576", - "id": "pyup.io-48800", - "more_info_path": "/vulnerabilities/CVE-2021-29576/48800", + "cve": "CVE-2021-37685", + "id": "pyup.io-48893", + "more_info_path": "/vulnerabilities/CVE-2021-37685/48893", "specs": [ "<0.10.0rc1" ], @@ -31263,9 +31497,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37662", - "id": "pyup.io-48870", - "more_info_path": "/vulnerabilities/CVE-2021-37662/48870", + "cve": "CVE-2021-37683", + "id": "pyup.io-48891", + "more_info_path": "/vulnerabilities/CVE-2021-37683/48891", "specs": [ "<0.10.0rc1" ], @@ -31273,9 +31507,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29522", - "id": "pyup.io-48746", - "more_info_path": "/vulnerabilities/CVE-2021-29522/48746", + "cve": "CVE-2021-37679", + "id": "pyup.io-48887", + "more_info_path": "/vulnerabilities/CVE-2021-37679/48887", "specs": [ "<0.10.0rc1" ], @@ -31283,9 +31517,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2020-8177", - "id": "pyup.io-48727", - "more_info_path": "/vulnerabilities/CVE-2020-8177/48727", + "cve": "CVE-2021-37684", + "id": "pyup.io-48892", + "more_info_path": "/vulnerabilities/CVE-2021-37684/48892", "specs": [ "<0.10.0rc1" ], @@ -31293,9 +31527,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-22901", - "id": "pyup.io-48735", - "more_info_path": "/vulnerabilities/CVE-2021-22901/48735", + "cve": "CVE-2021-37665", + "id": "pyup.io-48873", + "more_info_path": "/vulnerabilities/CVE-2021-37665/48873", "specs": [ "<0.10.0rc1" ], @@ -31303,9 +31537,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-22897", - "id": "pyup.io-48733", - "more_info_path": "/vulnerabilities/CVE-2021-22897/48733", + "cve": "CVE-2021-29573", + "id": "pyup.io-48797", + "more_info_path": "/vulnerabilities/CVE-2021-29573/48797", "specs": [ "<0.10.0rc1" ], @@ -31313,9 +31547,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37652", - "id": "pyup.io-48860", - "more_info_path": "/vulnerabilities/CVE-2021-37652/48860", + "cve": "CVE-2021-29539", + "id": "pyup.io-48763", + "more_info_path": "/vulnerabilities/CVE-2021-29539/48763", "specs": [ "<0.10.0rc1" ], @@ -31323,9 +31557,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29530", - "id": "pyup.io-48754", - "more_info_path": "/vulnerabilities/CVE-2021-29530/48754", + "cve": "CVE-2021-37688", + "id": "pyup.io-48896", + "more_info_path": "/vulnerabilities/CVE-2021-37688/48896", "specs": [ "<0.10.0rc1" ], @@ -31333,9 +31567,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29545", - "id": "pyup.io-48769", - "more_info_path": "/vulnerabilities/CVE-2021-29545/48769", + "cve": "CVE-2021-37680", + "id": "pyup.io-48888", + "more_info_path": "/vulnerabilities/CVE-2021-37680/48888", "specs": [ "<0.10.0rc1" ], @@ -31343,9 +31577,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29607", - "id": "pyup.io-48831", - "more_info_path": "/vulnerabilities/CVE-2021-29607/48831", + "cve": "CVE-2021-37676", + "id": "pyup.io-48884", + "more_info_path": "/vulnerabilities/CVE-2021-37676/48884", "specs": [ "<0.10.0rc1" ], @@ -31353,9 +31587,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37670", - "id": "pyup.io-48878", - "more_info_path": "/vulnerabilities/CVE-2021-37670/48878", + "cve": "CVE-2021-37667", + "id": "pyup.io-48875", + "more_info_path": "/vulnerabilities/CVE-2021-37667/48875", "specs": [ "<0.10.0rc1" ], @@ -31363,9 +31597,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29615", - "id": "pyup.io-48839", - "more_info_path": "/vulnerabilities/CVE-2021-29615/48839", + "cve": "CVE-2021-37652", + "id": "pyup.io-48860", + "more_info_path": "/vulnerabilities/CVE-2021-37652/48860", "specs": [ "<0.10.0rc1" ], @@ -31373,9 +31607,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29591", - "id": "pyup.io-48815", - "more_info_path": "/vulnerabilities/CVE-2021-29591/48815", + "cve": "CVE-2021-37648", + "id": "pyup.io-48856", + "more_info_path": "/vulnerabilities/CVE-2021-37648/48856", "specs": [ "<0.10.0rc1" ], @@ -31383,9 +31617,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37665", - "id": "pyup.io-48873", - "more_info_path": "/vulnerabilities/CVE-2021-37665/48873", + "cve": "CVE-2021-37661", + "id": "pyup.io-48869", + "more_info_path": "/vulnerabilities/CVE-2021-37661/48869", "specs": [ "<0.10.0rc1" ], @@ -31393,9 +31627,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29589", - "id": "pyup.io-48813", - "more_info_path": "/vulnerabilities/CVE-2021-29589/48813", + "cve": "CVE-2021-37659", + "id": "pyup.io-48867", + "more_info_path": "/vulnerabilities/CVE-2021-37659/48867", "specs": [ "<0.10.0rc1" ], @@ -31403,9 +31637,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29556", - "id": "pyup.io-48780", - "more_info_path": "/vulnerabilities/CVE-2021-29556/48780", + "cve": "CVE-2021-37645", + "id": "pyup.io-48853", + "more_info_path": "/vulnerabilities/CVE-2021-37645/48853", "specs": [ "<0.10.0rc1" ], @@ -31413,9 +31647,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29534", - "id": "pyup.io-48758", - "more_info_path": "/vulnerabilities/CVE-2021-29534/48758", + "cve": "CVE-2021-37644", + "id": "pyup.io-48852", + "more_info_path": "/vulnerabilities/CVE-2021-37644/48852", "specs": [ "<0.10.0rc1" ], @@ -31423,9 +31657,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37685", - "id": "pyup.io-48893", - "more_info_path": "/vulnerabilities/CVE-2021-37685/48893", + "cve": "CVE-2021-37641", + "id": "pyup.io-48849", + "more_info_path": "/vulnerabilities/CVE-2021-37641/48849", "specs": [ "<0.10.0rc1" ], @@ -31433,9 +31667,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-37660", - "id": "pyup.io-48868", - "more_info_path": "/vulnerabilities/CVE-2021-37660/48868", + "cve": "CVE-2021-37635", + "id": "pyup.io-48844", + "more_info_path": "/vulnerabilities/CVE-2021-37635/48844", "specs": [ "<0.10.0rc1" ], @@ -31443,9 +31677,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2020-8169", - "id": "pyup.io-48723", - "more_info_path": "/vulnerabilities/CVE-2020-8169/48723", + "cve": "CVE-2021-37638", + "id": "pyup.io-48847", + "more_info_path": "/vulnerabilities/CVE-2021-37638/48847", "specs": [ "<0.10.0rc1" ], @@ -31453,19 +31687,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-22876", - "id": "pyup.io-48732", - "more_info_path": "/vulnerabilities/CVE-2021-22876/48732", - "specs": [ - "<0.10.0rc1" - ], - "v": "<0.10.0rc1" - }, - { - "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2020-8231", - "id": "pyup.io-48728", - "more_info_path": "/vulnerabilities/CVE-2020-8231/48728", + "cve": "CVE-2021-37637", + "id": "pyup.io-48846", + "more_info_path": "/vulnerabilities/CVE-2021-37637/48846", "specs": [ "<0.10.0rc1" ], @@ -31473,9 +31697,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2020-8284", - "id": "pyup.io-48729", - "more_info_path": "/vulnerabilities/CVE-2020-8284/48729", + "cve": "CVE-2021-37673", + "id": "pyup.io-48881", + "more_info_path": "/vulnerabilities/CVE-2021-37673/48881", "specs": [ "<0.10.0rc1" ], @@ -31483,9 +31707,9 @@ }, { "advisory": "Deepcell 0.10.0rc1 updates its dependency 'TensorFlow' to v2.5.1 to include security fixes.", - "cve": "CVE-2021-29544", - "id": "pyup.io-48768", - "more_info_path": "/vulnerabilities/CVE-2021-29544/48768", + "cve": "CVE-2021-37664", + "id": "pyup.io-48872", + "more_info_path": "/vulnerabilities/CVE-2021-37664/48872", "specs": [ "<0.10.0rc1" ], @@ -31493,19 +31717,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23560", - "id": "pyup.io-48959", - "more_info_path": "/vulnerabilities/CVE-2022-23560/48959", - "specs": [ - "<0.12.0rc0" - ], - "v": "<0.12.0rc0" - }, - { - "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41226", - "id": "pyup.io-48936", - "more_info_path": "/vulnerabilities/CVE-2021-41226/48936", + "cve": "CVE-2021-41213", + "id": "pyup.io-48924", + "more_info_path": "/vulnerabilities/CVE-2021-41213/48924", "specs": [ "<0.12.0rc0" ], @@ -31513,9 +31727,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23563", - "id": "pyup.io-48962", - "more_info_path": "/vulnerabilities/CVE-2022-23563/48962", + "cve": "CVE-2022-21732", + "id": "pyup.io-48946", + "more_info_path": "/vulnerabilities/CVE-2022-21732/48946", "specs": [ "<0.12.0rc0" ], @@ -31523,9 +31737,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41199", - "id": "pyup.io-48910", - "more_info_path": "/vulnerabilities/CVE-2021-41199/48910", + "cve": "CVE-2022-23584", + "id": "pyup.io-48983", + "more_info_path": "/vulnerabilities/CVE-2022-23584/48983", "specs": [ "<0.12.0rc0" ], @@ -31533,9 +31747,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21739", - "id": "pyup.io-48953", - "more_info_path": "/vulnerabilities/CVE-2022-21739/48953", + "cve": "CVE-2022-23575", + "id": "pyup.io-48974", + "more_info_path": "/vulnerabilities/CVE-2022-23575/48974", "specs": [ "<0.12.0rc0" ], @@ -31543,9 +31757,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21732", - "id": "pyup.io-48946", - "more_info_path": "/vulnerabilities/CVE-2022-21732/48946", + "cve": "CVE-2021-41206", + "id": "pyup.io-48917", + "more_info_path": "/vulnerabilities/CVE-2021-41206/48917", "specs": [ "<0.12.0rc0" ], @@ -31553,9 +31767,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23584", - "id": "pyup.io-48983", - "more_info_path": "/vulnerabilities/CVE-2022-23584/48983", + "cve": "CVE-2021-41218", + "id": "pyup.io-48929", + "more_info_path": "/vulnerabilities/CVE-2021-41218/48929", "specs": [ "<0.12.0rc0" ], @@ -31563,9 +31777,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21725", - "id": "pyup.io-48939", - "more_info_path": "/vulnerabilities/CVE-2022-21725/48939", + "cve": "CVE-2021-41224", + "id": "pyup.io-48934", + "more_info_path": "/vulnerabilities/CVE-2021-41224/48934", "specs": [ "<0.12.0rc0" ], @@ -31573,9 +31787,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41200", - "id": "pyup.io-48911", - "more_info_path": "/vulnerabilities/CVE-2021-41200/48911", + "cve": "CVE-2021-22925", + "id": "pyup.io-48904", + "more_info_path": "/vulnerabilities/CVE-2021-22925/48904", "specs": [ "<0.12.0rc0" ], @@ -31583,9 +31797,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21736", - "id": "pyup.io-48950", - "more_info_path": "/vulnerabilities/CVE-2022-21736/48950", + "cve": "CVE-2021-22924", + "id": "pyup.io-48903", + "more_info_path": "/vulnerabilities/CVE-2021-22924/48903", "specs": [ "<0.12.0rc0" ], @@ -31593,9 +31807,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41210", - "id": "pyup.io-48921", - "more_info_path": "/vulnerabilities/CVE-2021-41210/48921", + "cve": "CVE-2021-22922", + "id": "pyup.io-48901", + "more_info_path": "/vulnerabilities/CVE-2021-22922/48901", "specs": [ "<0.12.0rc0" ], @@ -31603,9 +31817,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23575", - "id": "pyup.io-48974", - "more_info_path": "/vulnerabilities/CVE-2022-23575/48974", + "cve": "CVE-2021-41200", + "id": "pyup.io-48911", + "more_info_path": "/vulnerabilities/CVE-2021-41200/48911", "specs": [ "<0.12.0rc0" ], @@ -31613,9 +31827,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41212", - "id": "pyup.io-48923", - "more_info_path": "/vulnerabilities/CVE-2021-41212/48923", + "cve": "CVE-2021-41210", + "id": "pyup.io-48921", + "more_info_path": "/vulnerabilities/CVE-2021-41210/48921", "specs": [ "<0.12.0rc0" ], @@ -31623,9 +31837,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23583", - "id": "pyup.io-48982", - "more_info_path": "/vulnerabilities/CVE-2022-23583/48982", + "cve": "CVE-2021-41202", + "id": "pyup.io-48913", + "more_info_path": "/vulnerabilities/CVE-2021-41202/48913", "specs": [ "<0.12.0rc0" ], @@ -31633,9 +31847,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23589", - "id": "pyup.io-48988", - "more_info_path": "/vulnerabilities/CVE-2022-23589/48988", + "cve": "CVE-2022-23569", + "id": "pyup.io-48968", + "more_info_path": "/vulnerabilities/CVE-2022-23569/48968", "specs": [ "<0.12.0rc0" ], @@ -31643,9 +31857,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41216", - "id": "pyup.io-48927", - "more_info_path": "/vulnerabilities/CVE-2021-41216/48927", + "cve": "CVE-2022-23568", + "id": "pyup.io-48967", + "more_info_path": "/vulnerabilities/CVE-2022-23568/48967", "specs": [ "<0.12.0rc0" ], @@ -31653,9 +31867,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41208", - "id": "pyup.io-48919", - "more_info_path": "/vulnerabilities/CVE-2021-41208/48919", + "cve": "CVE-2022-23580", + "id": "pyup.io-48979", + "more_info_path": "/vulnerabilities/CVE-2022-23580/48979", "specs": [ "<0.12.0rc0" ], @@ -31663,9 +31877,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41213", - "id": "pyup.io-48924", - "more_info_path": "/vulnerabilities/CVE-2021-41213/48924", + "cve": "CVE-2021-41227", + "id": "pyup.io-48937", + "more_info_path": "/vulnerabilities/CVE-2021-41227/48937", "specs": [ "<0.12.0rc0" ], @@ -31673,9 +31887,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41209", - "id": "pyup.io-48920", - "more_info_path": "/vulnerabilities/CVE-2021-41209/48920", + "cve": "CVE-2022-21734", + "id": "pyup.io-48948", + "more_info_path": "/vulnerabilities/CVE-2022-21734/48948", "specs": [ "<0.12.0rc0" ], @@ -31683,9 +31897,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23569", - "id": "pyup.io-48968", - "more_info_path": "/vulnerabilities/CVE-2022-23569/48968", + "cve": "CVE-2022-23588", + "id": "pyup.io-48987", + "more_info_path": "/vulnerabilities/CVE-2022-23588/48987", "specs": [ "<0.12.0rc0" ], @@ -31693,9 +31907,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23568", - "id": "pyup.io-48967", - "more_info_path": "/vulnerabilities/CVE-2022-23568/48967", + "cve": "CVE-2022-23557", + "id": "pyup.io-48956", + "more_info_path": "/vulnerabilities/CVE-2022-23557/48956", "specs": [ "<0.12.0rc0" ], @@ -31703,9 +31917,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23567", - "id": "pyup.io-48966", - "more_info_path": "/vulnerabilities/CVE-2022-23567/48966", + "cve": "CVE-2022-23579", + "id": "pyup.io-48978", + "more_info_path": "/vulnerabilities/CVE-2022-23579/48978", "specs": [ "<0.12.0rc0" ], @@ -31713,9 +31927,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23574", - "id": "pyup.io-48973", - "more_info_path": "/vulnerabilities/CVE-2022-23574/48973", + "cve": "CVE-2021-41199", + "id": "pyup.io-48910", + "more_info_path": "/vulnerabilities/CVE-2021-41199/48910", "specs": [ "<0.12.0rc0" ], @@ -31723,9 +31937,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23580", - "id": "pyup.io-48979", - "more_info_path": "/vulnerabilities/CVE-2022-23580/48979", + "cve": "CVE-2022-23571", + "id": "pyup.io-48970", + "more_info_path": "/vulnerabilities/CVE-2022-23571/48970", "specs": [ "<0.12.0rc0" ], @@ -31733,9 +31947,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41227", - "id": "pyup.io-48937", - "more_info_path": "/vulnerabilities/CVE-2021-41227/48937", + "cve": "CVE-2021-41209", + "id": "pyup.io-48920", + "more_info_path": "/vulnerabilities/CVE-2021-41209/48920", "specs": [ "<0.12.0rc0" ], @@ -31743,9 +31957,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21734", - "id": "pyup.io-48948", - "more_info_path": "/vulnerabilities/CVE-2022-21734/48948", + "cve": "CVE-2022-23566", + "id": "pyup.io-48965", + "more_info_path": "/vulnerabilities/CVE-2022-23566/48965", "specs": [ "<0.12.0rc0" ], @@ -31753,9 +31967,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21728", - "id": "pyup.io-48942", - "more_info_path": "/vulnerabilities/CVE-2022-21728/48942", + "cve": "CVE-2022-23578", + "id": "pyup.io-48977", + "more_info_path": "/vulnerabilities/CVE-2022-23578/48977", "specs": [ "<0.12.0rc0" ], @@ -31763,9 +31977,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23591", - "id": "pyup.io-48989", - "more_info_path": "/vulnerabilities/CVE-2022-23591/48989", + "cve": "CVE-2022-23562", + "id": "pyup.io-48961", + "more_info_path": "/vulnerabilities/CVE-2022-23562/48961", "specs": [ "<0.12.0rc0" ], @@ -31773,9 +31987,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23588", - "id": "pyup.io-48987", - "more_info_path": "/vulnerabilities/CVE-2022-23588/48987", + "cve": "CVE-2021-41195", + "id": "pyup.io-48906", + "more_info_path": "/vulnerabilities/CVE-2021-41195/48906", "specs": [ "<0.12.0rc0" ], @@ -31783,9 +31997,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41204", - "id": "pyup.io-48915", - "more_info_path": "/vulnerabilities/CVE-2021-41204/48915", + "cve": "CVE-2021-41216", + "id": "pyup.io-48927", + "more_info_path": "/vulnerabilities/CVE-2021-41216/48927", "specs": [ "<0.12.0rc0" ], @@ -31793,9 +32007,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41225", - "id": "pyup.io-48935", - "more_info_path": "/vulnerabilities/CVE-2021-41225/48935", + "cve": "CVE-2021-41211", + "id": "pyup.io-48922", + "more_info_path": "/vulnerabilities/CVE-2021-41211/48922", "specs": [ "<0.12.0rc0" ], @@ -31803,9 +32017,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23557", - "id": "pyup.io-48956", - "more_info_path": "/vulnerabilities/CVE-2022-23557/48956", + "cve": "CVE-2021-41222", + "id": "pyup.io-48932", + "more_info_path": "/vulnerabilities/CVE-2021-41222/48932", "specs": [ "<0.12.0rc0" ], @@ -31813,9 +32027,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23579", - "id": "pyup.io-48978", - "more_info_path": "/vulnerabilities/CVE-2022-23579/48978", + "cve": "CVE-2021-41205", + "id": "pyup.io-48916", + "more_info_path": "/vulnerabilities/CVE-2021-41205/48916", "specs": [ "<0.12.0rc0" ], @@ -31823,9 +32037,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23571", - "id": "pyup.io-48970", - "more_info_path": "/vulnerabilities/CVE-2022-23571/48970", + "cve": "CVE-2021-41223", + "id": "pyup.io-48933", + "more_info_path": "/vulnerabilities/CVE-2021-41223/48933", "specs": [ "<0.12.0rc0" ], @@ -31833,9 +32047,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23566", - "id": "pyup.io-48965", - "more_info_path": "/vulnerabilities/CVE-2022-23566/48965", + "cve": "CVE-2022-23564", + "id": "pyup.io-48963", + "more_info_path": "/vulnerabilities/CVE-2022-23564/48963", "specs": [ "<0.12.0rc0" ], @@ -31843,9 +32057,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23595", - "id": "pyup.io-48990", - "more_info_path": "/vulnerabilities/CVE-2022-23595/48990", + "cve": "CVE-2022-23565", + "id": "pyup.io-48964", + "more_info_path": "/vulnerabilities/CVE-2022-23565/48964", "specs": [ "<0.12.0rc0" ], @@ -31853,9 +32067,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23578", - "id": "pyup.io-48977", - "more_info_path": "/vulnerabilities/CVE-2022-23578/48977", + "cve": "CVE-2022-23558", + "id": "pyup.io-48957", + "more_info_path": "/vulnerabilities/CVE-2022-23558/48957", "specs": [ "<0.12.0rc0" ], @@ -31863,9 +32077,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21726", - "id": "pyup.io-48940", - "more_info_path": "/vulnerabilities/CVE-2022-21726/48940", + "cve": "CVE-2022-23561", + "id": "pyup.io-48960", + "more_info_path": "/vulnerabilities/CVE-2022-23561/48960", "specs": [ "<0.12.0rc0" ], @@ -31873,9 +32087,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41224", - "id": "pyup.io-48934", - "more_info_path": "/vulnerabilities/CVE-2021-41224/48934", + "cve": "CVE-2022-21733", + "id": "pyup.io-48947", + "more_info_path": "/vulnerabilities/CVE-2022-21733/48947", "specs": [ "<0.12.0rc0" ], @@ -31883,9 +32097,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23562", - "id": "pyup.io-48961", - "more_info_path": "/vulnerabilities/CVE-2022-23562/48961", + "cve": "CVE-2022-21737", + "id": "pyup.io-48951", + "more_info_path": "/vulnerabilities/CVE-2022-21737/48951", "specs": [ "<0.12.0rc0" ], @@ -31893,9 +32107,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23581", - "id": "pyup.io-48980", - "more_info_path": "/vulnerabilities/CVE-2022-23581/48980", + "cve": "CVE-2022-21738", + "id": "pyup.io-48952", + "more_info_path": "/vulnerabilities/CVE-2022-21738/48952", "specs": [ "<0.12.0rc0" ], @@ -31903,9 +32117,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41221", - "id": "pyup.io-48931", - "more_info_path": "/vulnerabilities/CVE-2021-41221/48931", + "cve": "CVE-2020-10531", + "id": "pyup.io-48900", + "more_info_path": "/vulnerabilities/CVE-2020-10531/48900", "specs": [ "<0.12.0rc0" ], @@ -31913,9 +32127,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21735", - "id": "pyup.io-48949", - "more_info_path": "/vulnerabilities/CVE-2022-21735/48949", + "cve": "CVE-2022-23576", + "id": "pyup.io-48975", + "more_info_path": "/vulnerabilities/CVE-2022-23576/48975", "specs": [ "<0.12.0rc0" ], @@ -31923,9 +32137,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41207", - "id": "pyup.io-48918", - "more_info_path": "/vulnerabilities/CVE-2021-41207/48918", + "cve": "CVE-2022-21740", + "id": "pyup.io-48954", + "more_info_path": "/vulnerabilities/CVE-2022-21740/48954", "specs": [ "<0.12.0rc0" ], @@ -31933,9 +32147,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41195", - "id": "pyup.io-48906", - "more_info_path": "/vulnerabilities/CVE-2021-41195/48906", + "cve": "CVE-2021-41212", + "id": "pyup.io-48923", + "more_info_path": "/vulnerabilities/CVE-2021-41212/48923", "specs": [ "<0.12.0rc0" ], @@ -31943,9 +32157,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23587", - "id": "pyup.io-48986", - "more_info_path": "/vulnerabilities/CVE-2022-23587/48986", + "cve": "CVE-2021-41225", + "id": "pyup.io-48935", + "more_info_path": "/vulnerabilities/CVE-2021-41225/48935", "specs": [ "<0.12.0rc0" ], @@ -31953,9 +32167,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41222", - "id": "pyup.io-48932", - "more_info_path": "/vulnerabilities/CVE-2021-41222/48932", + "cve": "CVE-2021-41201", + "id": "pyup.io-48912", + "more_info_path": "/vulnerabilities/CVE-2021-41201/48912", "specs": [ "<0.12.0rc0" ], @@ -31963,9 +32177,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23564", - "id": "pyup.io-48963", - "more_info_path": "/vulnerabilities/CVE-2022-23564/48963", + "cve": "CVE-2021-41196", + "id": "pyup.io-48907", + "more_info_path": "/vulnerabilities/CVE-2021-41196/48907", "specs": [ "<0.12.0rc0" ], @@ -31973,9 +32187,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41205", - "id": "pyup.io-48916", - "more_info_path": "/vulnerabilities/CVE-2021-41205/48916", + "cve": "CVE-2021-41214", + "id": "pyup.io-48925", + "more_info_path": "/vulnerabilities/CVE-2021-41214/48925", "specs": [ "<0.12.0rc0" ], @@ -31983,9 +32197,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23565", - "id": "pyup.io-48964", - "more_info_path": "/vulnerabilities/CVE-2022-23565/48964", + "cve": "CVE-2021-41219", + "id": "pyup.io-48930", + "more_info_path": "/vulnerabilities/CVE-2021-41219/48930", "specs": [ "<0.12.0rc0" ], @@ -31993,9 +32207,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23558", - "id": "pyup.io-48957", - "more_info_path": "/vulnerabilities/CVE-2022-23558/48957", + "cve": "CVE-2021-41228", + "id": "pyup.io-48938", + "more_info_path": "/vulnerabilities/CVE-2021-41228/48938", "specs": [ "<0.12.0rc0" ], @@ -32003,9 +32217,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23585", - "id": "pyup.io-48984", - "more_info_path": "/vulnerabilities/CVE-2022-23585/48984", + "cve": "CVE-2022-23595", + "id": "pyup.io-48990", + "more_info_path": "/vulnerabilities/CVE-2022-23595/48990", "specs": [ "<0.12.0rc0" ], @@ -32013,9 +32227,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41202", - "id": "pyup.io-48913", - "more_info_path": "/vulnerabilities/CVE-2021-41202/48913", + "cve": "CVE-2021-41208", + "id": "pyup.io-48919", + "more_info_path": "/vulnerabilities/CVE-2021-41208/48919", "specs": [ "<0.12.0rc0" ], @@ -32023,9 +32237,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41198", - "id": "pyup.io-48909", - "more_info_path": "/vulnerabilities/CVE-2021-41198/48909", + "cve": "CVE-2022-23591", + "id": "pyup.io-48989", + "more_info_path": "/vulnerabilities/CVE-2022-23591/48989", "specs": [ "<0.12.0rc0" ], @@ -32033,9 +32247,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23559", - "id": "pyup.io-48958", - "more_info_path": "/vulnerabilities/CVE-2022-23559/48958", + "cve": "CVE-2022-23589", + "id": "pyup.io-48988", + "more_info_path": "/vulnerabilities/CVE-2022-23589/48988", "specs": [ "<0.12.0rc0" ], @@ -32043,9 +32257,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23561", - "id": "pyup.io-48960", - "more_info_path": "/vulnerabilities/CVE-2022-23561/48960", + "cve": "CVE-2021-41226", + "id": "pyup.io-48936", + "more_info_path": "/vulnerabilities/CVE-2021-41226/48936", "specs": [ "<0.12.0rc0" ], @@ -32053,9 +32267,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41228", - "id": "pyup.io-48938", - "more_info_path": "/vulnerabilities/CVE-2021-41228/48938", + "cve": "CVE-2022-23587", + "id": "pyup.io-48986", + "more_info_path": "/vulnerabilities/CVE-2022-23587/48986", "specs": [ "<0.12.0rc0" ], @@ -32073,19 +32287,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23577", - "id": "pyup.io-48976", - "more_info_path": "/vulnerabilities/CVE-2022-23577/48976", - "specs": [ - "<0.12.0rc0" - ], - "v": "<0.12.0rc0" - }, - { - "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21731", - "id": "pyup.io-48945", - "more_info_path": "/vulnerabilities/CVE-2022-21731/48945", + "cve": "CVE-2022-23586", + "id": "pyup.io-48985", + "more_info_path": "/vulnerabilities/CVE-2022-23586/48985", "specs": [ "<0.12.0rc0" ], @@ -32093,9 +32297,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41223", - "id": "pyup.io-48933", - "more_info_path": "/vulnerabilities/CVE-2021-41223/48933", + "cve": "CVE-2022-23585", + "id": "pyup.io-48984", + "more_info_path": "/vulnerabilities/CVE-2022-23585/48984", "specs": [ "<0.12.0rc0" ], @@ -32103,9 +32307,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41206", - "id": "pyup.io-48917", - "more_info_path": "/vulnerabilities/CVE-2021-41206/48917", + "cve": "CVE-2022-23583", + "id": "pyup.io-48982", + "more_info_path": "/vulnerabilities/CVE-2022-23583/48982", "specs": [ "<0.12.0rc0" ], @@ -32113,9 +32317,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41201", - "id": "pyup.io-48912", - "more_info_path": "/vulnerabilities/CVE-2021-41201/48912", + "cve": "CVE-2022-23582", + "id": "pyup.io-48981", + "more_info_path": "/vulnerabilities/CVE-2022-23582/48981", "specs": [ "<0.12.0rc0" ], @@ -32123,9 +32327,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23586", - "id": "pyup.io-48985", - "more_info_path": "/vulnerabilities/CVE-2022-23586/48985", + "cve": "CVE-2021-41204", + "id": "pyup.io-48915", + "more_info_path": "/vulnerabilities/CVE-2021-41204/48915", "specs": [ "<0.12.0rc0" ], @@ -32133,9 +32337,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41219", - "id": "pyup.io-48930", - "more_info_path": "/vulnerabilities/CVE-2021-41219/48930", + "cve": "CVE-2021-41203", + "id": "pyup.io-48914", + "more_info_path": "/vulnerabilities/CVE-2021-41203/48914", "specs": [ "<0.12.0rc0" ], @@ -32143,9 +32347,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23582", - "id": "pyup.io-48981", - "more_info_path": "/vulnerabilities/CVE-2022-23582/48981", + "cve": "CVE-2022-23581", + "id": "pyup.io-48980", + "more_info_path": "/vulnerabilities/CVE-2022-23581/48980", "specs": [ "<0.12.0rc0" ], @@ -32153,9 +32357,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41218", - "id": "pyup.io-48929", - "more_info_path": "/vulnerabilities/CVE-2021-41218/48929", + "cve": "CVE-2022-23577", + "id": "pyup.io-48976", + "more_info_path": "/vulnerabilities/CVE-2022-23577/48976", "specs": [ "<0.12.0rc0" ], @@ -32163,9 +32367,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21733", - "id": "pyup.io-48947", - "more_info_path": "/vulnerabilities/CVE-2022-21733/48947", + "cve": "CVE-2021-41198", + "id": "pyup.io-48909", + "more_info_path": "/vulnerabilities/CVE-2021-41198/48909", "specs": [ "<0.12.0rc0" ], @@ -32183,9 +32387,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21737", - "id": "pyup.io-48951", - "more_info_path": "/vulnerabilities/CVE-2022-21737/48951", + "cve": "CVE-2021-22923", + "id": "pyup.io-48902", + "more_info_path": "/vulnerabilities/CVE-2021-22923/48902", "specs": [ "<0.12.0rc0" ], @@ -32193,9 +32397,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41196", - "id": "pyup.io-48907", - "more_info_path": "/vulnerabilities/CVE-2021-41196/48907", + "cve": "CVE-2022-23574", + "id": "pyup.io-48973", + "more_info_path": "/vulnerabilities/CVE-2022-23574/48973", "specs": [ "<0.12.0rc0" ], @@ -32203,9 +32407,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41214", - "id": "pyup.io-48925", - "more_info_path": "/vulnerabilities/CVE-2021-41214/48925", + "cve": "CVE-2022-23573", + "id": "pyup.io-48972", + "more_info_path": "/vulnerabilities/CVE-2022-23573/48972", "specs": [ "<0.12.0rc0" ], @@ -32213,9 +32417,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21729", - "id": "pyup.io-48943", - "more_info_path": "/vulnerabilities/CVE-2022-21729/48943", + "cve": "CVE-2022-23572", + "id": "pyup.io-48971", + "more_info_path": "/vulnerabilities/CVE-2022-23572/48971", "specs": [ "<0.12.0rc0" ], @@ -32223,9 +32427,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21738", - "id": "pyup.io-48952", - "more_info_path": "/vulnerabilities/CVE-2022-21738/48952", + "cve": "CVE-2022-23570", + "id": "pyup.io-48969", + "more_info_path": "/vulnerabilities/CVE-2022-23570/48969", "specs": [ "<0.12.0rc0" ], @@ -32233,9 +32437,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41215", - "id": "pyup.io-48926", - "more_info_path": "/vulnerabilities/CVE-2021-41215/48926", + "cve": "CVE-2022-23563", + "id": "pyup.io-48962", + "more_info_path": "/vulnerabilities/CVE-2022-23563/48962", "specs": [ "<0.12.0rc0" ], @@ -32243,9 +32447,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21727", - "id": "pyup.io-48941", - "more_info_path": "/vulnerabilities/CVE-2022-21727/48941", + "cve": "CVE-2022-23560", + "id": "pyup.io-48959", + "more_info_path": "/vulnerabilities/CVE-2022-23560/48959", "specs": [ "<0.12.0rc0" ], @@ -32253,9 +32457,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2020-10531", - "id": "pyup.io-48900", - "more_info_path": "/vulnerabilities/CVE-2020-10531/48900", + "cve": "CVE-2022-23559", + "id": "pyup.io-48958", + "more_info_path": "/vulnerabilities/CVE-2022-23559/48958", "specs": [ "<0.12.0rc0" ], @@ -32263,9 +32467,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21730", - "id": "pyup.io-48944", - "more_info_path": "/vulnerabilities/CVE-2022-21730/48944", + "cve": "CVE-2022-21741", + "id": "pyup.io-48955", + "more_info_path": "/vulnerabilities/CVE-2022-21741/48955", "specs": [ "<0.12.0rc0" ], @@ -32273,9 +32477,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23572", - "id": "pyup.io-48971", - "more_info_path": "/vulnerabilities/CVE-2022-23572/48971", + "cve": "CVE-2022-21739", + "id": "pyup.io-48953", + "more_info_path": "/vulnerabilities/CVE-2022-21739/48953", "specs": [ "<0.12.0rc0" ], @@ -32283,9 +32487,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23576", - "id": "pyup.io-48975", - "more_info_path": "/vulnerabilities/CVE-2022-23576/48975", + "cve": "CVE-2021-22926", + "id": "pyup.io-48905", + "more_info_path": "/vulnerabilities/CVE-2021-22926/48905", "specs": [ "<0.12.0rc0" ], @@ -32293,9 +32497,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-22924", - "id": "pyup.io-48903", - "more_info_path": "/vulnerabilities/CVE-2021-22924/48903", + "cve": "CVE-2022-21735", + "id": "pyup.io-48949", + "more_info_path": "/vulnerabilities/CVE-2022-21735/48949", "specs": [ "<0.12.0rc0" ], @@ -32303,9 +32507,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21740", - "id": "pyup.io-48954", - "more_info_path": "/vulnerabilities/CVE-2022-21740/48954", + "cve": "CVE-2022-21729", + "id": "pyup.io-48943", + "more_info_path": "/vulnerabilities/CVE-2022-21729/48943", "specs": [ "<0.12.0rc0" ], @@ -32313,9 +32517,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41211", - "id": "pyup.io-48922", - "more_info_path": "/vulnerabilities/CVE-2021-41211/48922", + "cve": "CVE-2022-21725", + "id": "pyup.io-48939", + "more_info_path": "/vulnerabilities/CVE-2022-21725/48939", "specs": [ "<0.12.0rc0" ], @@ -32323,9 +32527,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23573", - "id": "pyup.io-48972", - "more_info_path": "/vulnerabilities/CVE-2022-23573/48972", + "cve": "CVE-2022-23567", + "id": "pyup.io-48966", + "more_info_path": "/vulnerabilities/CVE-2022-23567/48966", "specs": [ "<0.12.0rc0" ], @@ -32333,9 +32537,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41203", - "id": "pyup.io-48914", - "more_info_path": "/vulnerabilities/CVE-2021-41203/48914", + "cve": "CVE-2022-21736", + "id": "pyup.io-48950", + "more_info_path": "/vulnerabilities/CVE-2022-21736/48950", "specs": [ "<0.12.0rc0" ], @@ -32343,9 +32547,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21741", - "id": "pyup.io-48955", - "more_info_path": "/vulnerabilities/CVE-2022-21741/48955", + "cve": "CVE-2022-21731", + "id": "pyup.io-48945", + "more_info_path": "/vulnerabilities/CVE-2022-21731/48945", "specs": [ "<0.12.0rc0" ], @@ -32353,9 +32557,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23570", - "id": "pyup.io-48969", - "more_info_path": "/vulnerabilities/CVE-2022-23570/48969", + "cve": "CVE-2022-21730", + "id": "pyup.io-48944", + "more_info_path": "/vulnerabilities/CVE-2022-21730/48944", "specs": [ "<0.12.0rc0" ], @@ -32363,9 +32567,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-22922", - "id": "pyup.io-48901", - "more_info_path": "/vulnerabilities/CVE-2021-22922/48901", + "cve": "CVE-2022-21728", + "id": "pyup.io-48942", + "more_info_path": "/vulnerabilities/CVE-2022-21728/48942", "specs": [ "<0.12.0rc0" ], @@ -32373,9 +32577,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-22923", - "id": "pyup.io-48902", - "more_info_path": "/vulnerabilities/CVE-2021-22923/48902", + "cve": "CVE-2022-21727", + "id": "pyup.io-48941", + "more_info_path": "/vulnerabilities/CVE-2022-21727/48941", "specs": [ "<0.12.0rc0" ], @@ -32383,9 +32587,9 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-22926", - "id": "pyup.io-48905", - "more_info_path": "/vulnerabilities/CVE-2021-22926/48905", + "cve": "CVE-2022-21726", + "id": "pyup.io-48940", + "more_info_path": "/vulnerabilities/CVE-2022-21726/48940", "specs": [ "<0.12.0rc0" ], @@ -32393,49 +32597,39 @@ }, { "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-22925", - "id": "pyup.io-48904", - "more_info_path": "/vulnerabilities/CVE-2021-22925/48904", + "cve": "CVE-2021-41207", + "id": "pyup.io-48918", + "more_info_path": "/vulnerabilities/CVE-2021-41207/48918", "specs": [ "<0.12.0rc0" ], "v": "<0.12.0rc0" }, { - "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-13434", - "id": "pyup.io-48684", - "more_info_path": "/vulnerabilities/CVE-2020-13434/48684", - "specs": [ - "<0.8" - ], - "v": "<0.8" - }, - { - "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2018-20330", - "id": "pyup.io-48669", - "more_info_path": "/vulnerabilities/CVE-2018-20330/48669", + "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", + "cve": "CVE-2021-41215", + "id": "pyup.io-48926", + "more_info_path": "/vulnerabilities/CVE-2021-41215/48926", "specs": [ - "<0.8" + "<0.12.0rc0" ], - "v": "<0.8" + "v": "<0.12.0rc0" }, { - "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-15195", - "id": "pyup.io-48693", - "more_info_path": "/vulnerabilities/CVE-2020-15195/48693", + "advisory": "Deepcell 0.12.0rc0 updates its dependency 'TensorFlow' to v2.8.0 to include security fixes.", + "cve": "CVE-2021-41221", + "id": "pyup.io-48931", + "more_info_path": "/vulnerabilities/CVE-2021-41221/48931", "specs": [ - "<0.8" + "<0.12.0rc0" ], - "v": "<0.8" + "v": "<0.12.0rc0" }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-15206", - "id": "pyup.io-48698", - "more_info_path": "/vulnerabilities/CVE-2020-15206/48698", + "cve": "CVE-2020-26267", + "id": "pyup.io-48706", + "more_info_path": "/vulnerabilities/CVE-2020-26267/48706", "specs": [ "<0.8" ], @@ -32443,9 +32637,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-15209", - "id": "pyup.io-48701", - "more_info_path": "/vulnerabilities/CVE-2020-15209/48701", + "cve": "CVE-2020-15210", + "id": "pyup.io-48702", + "more_info_path": "/vulnerabilities/CVE-2020-15210/48702", "specs": [ "<0.8" ], @@ -32453,9 +32647,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-26266", - "id": "pyup.io-48705", - "more_info_path": "/vulnerabilities/CVE-2020-26266/48705", + "cve": "CVE-2020-13434", + "id": "pyup.io-48684", + "more_info_path": "/vulnerabilities/CVE-2020-13434/48684", "specs": [ "<0.8" ], @@ -32463,9 +32657,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-15211", - "id": "pyup.io-48703", - "more_info_path": "/vulnerabilities/CVE-2020-15211/48703", + "cve": "CVE-2018-20330", + "id": "pyup.io-48669", + "more_info_path": "/vulnerabilities/CVE-2018-20330/48669", "specs": [ "<0.8" ], @@ -32473,9 +32667,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-15194", - "id": "pyup.io-48692", - "more_info_path": "/vulnerabilities/CVE-2020-15194/48692", + "cve": "CVE-2020-15202", + "id": "pyup.io-48694", + "more_info_path": "/vulnerabilities/CVE-2020-15202/48694", "specs": [ "<0.8" ], @@ -32483,9 +32677,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-26267", - "id": "pyup.io-48706", - "more_info_path": "/vulnerabilities/CVE-2020-26267/48706", + "cve": "CVE-2019-19244", + "id": "pyup.io-48675", + "more_info_path": "/vulnerabilities/CVE-2019-19244/48675", "specs": [ "<0.8" ], @@ -32493,9 +32687,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2019-19244", - "id": "pyup.io-48675", - "more_info_path": "/vulnerabilities/CVE-2019-19244/48675", + "cve": "CVE-2020-15205", + "id": "pyup.io-48697", + "more_info_path": "/vulnerabilities/CVE-2020-15205/48697", "specs": [ "<0.8" ], @@ -32543,29 +32737,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-15202", - "id": "pyup.io-48694", - "more_info_path": "/vulnerabilities/CVE-2020-15202/48694", - "specs": [ - "<0.8" - ], - "v": "<0.8" - }, - { - "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-15210", - "id": "pyup.io-48702", - "more_info_path": "/vulnerabilities/CVE-2020-15210/48702", - "specs": [ - "<0.8" - ], - "v": "<0.8" - }, - { - "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-26271", - "id": "pyup.io-48709", - "more_info_path": "/vulnerabilities/CVE-2020-26271/48709", + "cve": "CVE-2020-15195", + "id": "pyup.io-48693", + "more_info_path": "/vulnerabilities/CVE-2020-15195/48693", "specs": [ "<0.8" ], @@ -32573,9 +32747,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-13631", - "id": "pyup.io-48687", - "more_info_path": "/vulnerabilities/CVE-2020-13631/48687", + "cve": "CVE-2020-15206", + "id": "pyup.io-48698", + "more_info_path": "/vulnerabilities/CVE-2020-15206/48698", "specs": [ "<0.8" ], @@ -32603,9 +32777,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2019-5482", - "id": "pyup.io-48681", - "more_info_path": "/vulnerabilities/CVE-2019-5482/48681", + "cve": "CVE-2020-15207", + "id": "pyup.io-48699", + "more_info_path": "/vulnerabilities/CVE-2020-15207/48699", "specs": [ "<0.8" ], @@ -32613,9 +32787,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-15207", - "id": "pyup.io-48699", - "more_info_path": "/vulnerabilities/CVE-2020-15207/48699", + "cve": "CVE-2020-26270", + "id": "pyup.io-48708", + "more_info_path": "/vulnerabilities/CVE-2020-26270/48708", "specs": [ "<0.8" ], @@ -32623,9 +32797,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-15204", - "id": "pyup.io-48696", - "more_info_path": "/vulnerabilities/CVE-2020-15204/48696", + "cve": "CVE-2019-5482", + "id": "pyup.io-48681", + "more_info_path": "/vulnerabilities/CVE-2019-5482/48681", "specs": [ "<0.8" ], @@ -32633,9 +32807,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-15208", - "id": "pyup.io-48700", - "more_info_path": "/vulnerabilities/CVE-2020-15208/48700", + "cve": "CVE-2020-15204", + "id": "pyup.io-48696", + "more_info_path": "/vulnerabilities/CVE-2020-15204/48696", "specs": [ "<0.8" ], @@ -32663,9 +32837,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-15203", - "id": "pyup.io-48695", - "more_info_path": "/vulnerabilities/CVE-2020-15203/48695", + "cve": "CVE-2019-19645", + "id": "pyup.io-48676", + "more_info_path": "/vulnerabilities/CVE-2019-19645/48676", "specs": [ "<0.8" ], @@ -32673,9 +32847,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2019-19645", - "id": "pyup.io-48676", - "more_info_path": "/vulnerabilities/CVE-2019-19645/48676", + "cve": "CVE-2020-26268", + "id": "pyup.io-48707", + "more_info_path": "/vulnerabilities/CVE-2020-26268/48707", "specs": [ "<0.8" ], @@ -32683,9 +32857,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-15205", - "id": "pyup.io-48697", - "more_info_path": "/vulnerabilities/CVE-2020-15205/48697", + "cve": "CVE-2019-19880", + "id": "pyup.io-48678", + "more_info_path": "/vulnerabilities/CVE-2019-19880/48678", "specs": [ "<0.8" ], @@ -32693,9 +32867,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2019-19880", - "id": "pyup.io-48678", - "more_info_path": "/vulnerabilities/CVE-2019-19880/48678", + "cve": "CVE-2019-19646", + "id": "pyup.io-48677", + "more_info_path": "/vulnerabilities/CVE-2019-19646/48677", "specs": [ "<0.8" ], @@ -32703,9 +32877,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-9327", - "id": "pyup.io-48711", - "more_info_path": "/vulnerabilities/CVE-2020-9327/48711", + "cve": "CVE-2020-15250", + "id": "pyup.io-48704", + "more_info_path": "/vulnerabilities/CVE-2020-15250/48704", "specs": [ "<0.8" ], @@ -32713,9 +32887,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2019-19646", - "id": "pyup.io-48677", - "more_info_path": "/vulnerabilities/CVE-2019-19646/48677", + "cve": "CVE-2020-14155", + "id": "pyup.io-48690", + "more_info_path": "/vulnerabilities/CVE-2020-14155/48690", "specs": [ "<0.8" ], @@ -32723,9 +32897,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-26270", - "id": "pyup.io-48708", - "more_info_path": "/vulnerabilities/CVE-2020-26270/48708", + "cve": "CVE-2020-13871", + "id": "pyup.io-48689", + "more_info_path": "/vulnerabilities/CVE-2020-13871/48689", "specs": [ "<0.8" ], @@ -32733,9 +32907,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-26268", - "id": "pyup.io-48707", - "more_info_path": "/vulnerabilities/CVE-2020-26268/48707", + "cve": "CVE-2019-5481", + "id": "pyup.io-48680", + "more_info_path": "/vulnerabilities/CVE-2019-5481/48680", "specs": [ "<0.8" ], @@ -32743,9 +32917,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-14155", - "id": "pyup.io-48690", - "more_info_path": "/vulnerabilities/CVE-2020-14155/48690", + "cve": "CVE-2019-20838", + "id": "pyup.io-48679", + "more_info_path": "/vulnerabilities/CVE-2019-20838/48679", "specs": [ "<0.8" ], @@ -32753,9 +32927,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-13871", - "id": "pyup.io-48689", - "more_info_path": "/vulnerabilities/CVE-2020-13871/48689", + "cve": "CVE-2020-15211", + "id": "pyup.io-48703", + "more_info_path": "/vulnerabilities/CVE-2020-15211/48703", "specs": [ "<0.8" ], @@ -32763,9 +32937,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-15250", - "id": "pyup.io-48704", - "more_info_path": "/vulnerabilities/CVE-2020-15250/48704", + "cve": "CVE-2020-26266", + "id": "pyup.io-48705", + "more_info_path": "/vulnerabilities/CVE-2020-26266/48705", "specs": [ "<0.8" ], @@ -32773,9 +32947,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2019-5481", - "id": "pyup.io-48680", - "more_info_path": "/vulnerabilities/CVE-2019-5481/48680", + "cve": "CVE-2020-15208", + "id": "pyup.io-48700", + "more_info_path": "/vulnerabilities/CVE-2020-15208/48700", "specs": [ "<0.8" ], @@ -32783,9 +32957,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2019-20838", - "id": "pyup.io-48679", - "more_info_path": "/vulnerabilities/CVE-2019-20838/48679", + "cve": "CVE-2020-26271", + "id": "pyup.io-48709", + "more_info_path": "/vulnerabilities/CVE-2020-26271/48709", "specs": [ "<0.8" ], @@ -32803,9 +32977,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-13630", - "id": "pyup.io-48686", - "more_info_path": "/vulnerabilities/CVE-2020-13630/48686", + "cve": "CVE-2020-13790", + "id": "pyup.io-48688", + "more_info_path": "/vulnerabilities/CVE-2020-13790/48688", "specs": [ "<0.8" ], @@ -32813,9 +32987,9 @@ }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", - "cve": "CVE-2020-13790", - "id": "pyup.io-48688", - "more_info_path": "/vulnerabilities/CVE-2020-13790/48688", + "cve": "CVE-2020-15194", + "id": "pyup.io-48692", + "more_info_path": "/vulnerabilities/CVE-2020-15194/48692", "specs": [ "<0.8" ], @@ -32831,6 +33005,36 @@ ], "v": "<0.8" }, + { + "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", + "cve": "CVE-2020-15203", + "id": "pyup.io-48695", + "more_info_path": "/vulnerabilities/CVE-2020-15203/48695", + "specs": [ + "<0.8" + ], + "v": "<0.8" + }, + { + "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", + "cve": "CVE-2020-13630", + "id": "pyup.io-48686", + "more_info_path": "/vulnerabilities/CVE-2020-13630/48686", + "specs": [ + "<0.8" + ], + "v": "<0.8" + }, + { + "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", + "cve": "CVE-2020-9327", + "id": "pyup.io-48711", + "more_info_path": "/vulnerabilities/CVE-2020-9327/48711", + "specs": [ + "<0.8" + ], + "v": "<0.8" + }, { "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", "cve": "CVE-2019-16168", @@ -32851,11 +33055,31 @@ ], "v": "<0.8" }, + { + "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", + "cve": "CVE-2020-15209", + "id": "pyup.io-48701", + "more_info_path": "/vulnerabilities/CVE-2020-15209/48701", + "specs": [ + "<0.8" + ], + "v": "<0.8" + }, + { + "advisory": "Deepcell 0.8 updates its dependency 'TensorFlow' to v2.3.1 to include security fixes.", + "cve": "CVE-2020-13631", + "id": "pyup.io-48687", + "more_info_path": "/vulnerabilities/CVE-2020-13631/48687", + "specs": [ + "<0.8" + ], + "v": "<0.8" + }, { "advisory": "Deepcell 0.9 updates its dependency 'TensorFlow' to v2.4.1 to include security fixes.", - "cve": "CVE-2020-26266", - "id": "pyup.io-48718", - "more_info_path": "/vulnerabilities/CVE-2020-26266/48718", + "cve": "CVE-2020-26271", + "id": "pyup.io-48722", + "more_info_path": "/vulnerabilities/CVE-2020-26271/48722", "specs": [ "<0.9" ], @@ -32863,9 +33087,9 @@ }, { "advisory": "Deepcell 0.9 updates its dependency 'TensorFlow' to v2.4.1 to include security fixes.", - "cve": "CVE-2020-26267", - "id": "pyup.io-48719", - "more_info_path": "/vulnerabilities/CVE-2020-26267/48719", + "cve": "CVE-2020-26268", + "id": "pyup.io-48720", + "more_info_path": "/vulnerabilities/CVE-2020-26268/48720", "specs": [ "<0.9" ], @@ -32873,9 +33097,9 @@ }, { "advisory": "Deepcell 0.9 updates its dependency 'TensorFlow' to v2.4.1 to include security fixes.", - "cve": "CVE-2020-14155", - "id": "pyup.io-48714", - "more_info_path": "/vulnerabilities/CVE-2020-14155/48714", + "cve": "CVE-2020-26267", + "id": "pyup.io-48719", + "more_info_path": "/vulnerabilities/CVE-2020-26267/48719", "specs": [ "<0.9" ], @@ -32913,9 +33137,9 @@ }, { "advisory": "Deepcell 0.9 updates its dependency 'TensorFlow' to v2.4.1 to include security fixes.", - "cve": "CVE-2020-26268", - "id": "pyup.io-48720", - "more_info_path": "/vulnerabilities/CVE-2020-26268/48720", + "cve": "CVE-2020-13790", + "id": "pyup.io-48713", + "more_info_path": "/vulnerabilities/CVE-2020-13790/48713", "specs": [ "<0.9" ], @@ -32923,9 +33147,9 @@ }, { "advisory": "Deepcell 0.9 updates its dependency 'TensorFlow' to v2.4.1 to include security fixes.", - "cve": "CVE-2020-26271", - "id": "pyup.io-48722", - "more_info_path": "/vulnerabilities/CVE-2020-26271/48722", + "cve": "CVE-2020-15250", + "id": "pyup.io-48715", + "more_info_path": "/vulnerabilities/CVE-2020-15250/48715", "specs": [ "<0.9" ], @@ -32933,9 +33157,9 @@ }, { "advisory": "Deepcell 0.9 updates its dependency 'TensorFlow' to v2.4.1 to include security fixes.", - "cve": "CVE-2020-13790", - "id": "pyup.io-48713", - "more_info_path": "/vulnerabilities/CVE-2020-13790/48713", + "cve": "CVE-2020-14155", + "id": "pyup.io-48714", + "more_info_path": "/vulnerabilities/CVE-2020-14155/48714", "specs": [ "<0.9" ], @@ -32943,9 +33167,9 @@ }, { "advisory": "Deepcell 0.9 updates its dependency 'TensorFlow' to v2.4.1 to include security fixes.", - "cve": "CVE-2020-15250", - "id": "pyup.io-48715", - "more_info_path": "/vulnerabilities/CVE-2020-15250/48715", + "cve": "CVE-2019-20838", + "id": "pyup.io-48712", + "more_info_path": "/vulnerabilities/CVE-2019-20838/48712", "specs": [ "<0.9" ], @@ -32953,9 +33177,9 @@ }, { "advisory": "Deepcell 0.9 updates its dependency 'TensorFlow' to v2.4.1 to include security fixes.", - "cve": "CVE-2019-20838", - "id": "pyup.io-48712", - "more_info_path": "/vulnerabilities/CVE-2019-20838/48712", + "cve": "CVE-2020-26266", + "id": "pyup.io-48718", + "more_info_path": "/vulnerabilities/CVE-2020-26266/48718", "specs": [ "<0.9" ], @@ -32983,6 +33207,16 @@ ], "v": "<0.18.0" }, + { + "advisory": "Deepchecks version 0.18.0 updates its dependency on pillow to version 10.0.1 from 9.5.0 addressing security vulnerability CVE-2023-39968.\r\nhttps://github.com/deepchecks/deepchecks/pull/2683", + "cve": "CVE-2023-4863", + "id": "pyup.io-64766", + "more_info_path": "/vulnerabilities/CVE-2023-4863/64766", + "specs": [ + "<0.18.0" + ], + "v": "<0.18.0" + }, { "advisory": "Deepchecks version 0.18.0 updates its dependency on jupyter-server to version 2.7.2 from 1.24.0, addressing security vulnerability CVE-2023-40170.\r\nhttps://github.com/deepchecks/deepchecks/pull/2683", "cve": "CVE-2023-40170", @@ -33002,16 +33236,6 @@ "<0.18.0" ], "v": "<0.18.0" - }, - { - "advisory": "Deepchecks version 0.18.0 updates its dependency on pillow to version 10.0.1 from 9.5.0 addressing security vulnerability CVE-2023-39968.\r\nhttps://github.com/deepchecks/deepchecks/pull/2683", - "cve": "CVE-2023-4863", - "id": "pyup.io-64766", - "more_info_path": "/vulnerabilities/CVE-2023-4863/64766", - "specs": [ - "<0.18.0" - ], - "v": "<0.18.0" } ], "deepdataspace": [ @@ -33025,6 +33249,16 @@ ], "v": "<0.11.0" }, + { + "advisory": "Deepdataspace 0.5.0 updates its dependency 'django' to version '4.1.10' to include a fix for a ReDoS vulnerability.", + "cve": "CVE-2023-36053", + "id": "pyup.io-60633", + "more_info_path": "/vulnerabilities/CVE-2023-36053/60633", + "specs": [ + "<0.5.0" + ], + "v": "<0.5.0" + }, { "advisory": "Deepdataspace 0.5.0 updates its dependency 'cryptography' to version '41.0.2' to include a fix for a Denial of Service vulnerability.\r\nhttps://github.com/IDEA-Research/deepdataspace/commit/4ddc986c8d12be1f2d805bc1085b336c40f4a5c1", "cve": "CVE-2023-2650", @@ -33044,16 +33278,6 @@ "<0.5.0" ], "v": "<0.5.0" - }, - { - "advisory": "Deepdataspace 0.5.0 updates its dependency 'django' to version '4.1.10' to include a fix for a ReDoS vulnerability.", - "cve": "CVE-2023-36053", - "id": "pyup.io-60633", - "more_info_path": "/vulnerabilities/CVE-2023-36053/60633", - "specs": [ - "<0.5.0" - ], - "v": "<0.5.0" } ], "deephaven-core": [ @@ -33212,6 +33436,19 @@ "v": "<0.6.2" } ], + "deepsolid": [ + { + "advisory": "The OpenSSF Package Analysis project has identified the PyPI package 'deepsolid' version 99.6 as malicious because it communicates with a domain associated with malicious activity, indicating potential security risks.", + "cve": "PVE-2024-74239", + "id": "pyup.io-74239", + "more_info_path": "/vulnerabilities/PVE-2024-74239/74239", + "specs": [ + ">=0", + "<=0" + ], + "v": ">=0,<=0" + } + ], "deepspeed": [ { "advisory": "Affected versions of DeepSpeed are vulnerable to Command Injection \u2014 CWE-78. The attack can be performed by injecting malicious input into parameters that are passed to subprocess calls with shell=True. Vulnerable functions include multiple instances where subprocess.run() and subprocess.check_output() are called with unsanitized input and shell=True. To exploit this vulnerability, an attacker would need to supply specially crafted input to these functions, which could be possible in environments where user input is processed. To mitigate this issue, users should update to the version of DeepSpeed where these subprocess calls have been secured by removing shell=True and properly handling command arguments.", @@ -33466,20 +33703,20 @@ "v": "<3.0.0" }, { - "advisory": "Descarteslabs version 3.0.2 has upgraded its pyarrow dependency to require a minimum of version 14.0.1, moving from the earlier stipulation of version 13.0.0 or newer. This update is in response to addressing security concerns highlighted by CVE-2019-12410.\r\nhttps://github.com/descarteslabs/descarteslabs-python/commit/bc51d674b7245c708e49080f3819d66ecc88fab5", - "cve": "CVE-2019-12410", - "id": "pyup.io-65085", - "more_info_path": "/vulnerabilities/CVE-2019-12410/65085", + "advisory": "Descarteslabs version 3.0.2 has updated its minimum required version of the requests library to 2.31.0, previously set at 2.28.1 or higher. This upgrade addresses the security issue identified as CVE-2023-32681.\r\nhttps://github.com/descarteslabs/descarteslabs-python/commit/bc51d674b7245c708e49080f3819d66ecc88fab5", + "cve": "CVE-2023-32681", + "id": "pyup.io-65092", + "more_info_path": "/vulnerabilities/CVE-2023-32681/65092", "specs": [ "<3.0.2" ], "v": "<3.0.2" }, { - "advisory": "Descarteslabs version 3.0.2 has updated its minimum required version of the requests library to 2.31.0, previously set at 2.28.1 or higher. This upgrade addresses the security issue identified as CVE-2023-32681.\r\nhttps://github.com/descarteslabs/descarteslabs-python/commit/bc51d674b7245c708e49080f3819d66ecc88fab5", - "cve": "CVE-2023-32681", - "id": "pyup.io-65092", - "more_info_path": "/vulnerabilities/CVE-2023-32681/65092", + "advisory": "Descarteslabs version 3.0.2 has upgraded its pyarrow dependency to require a minimum of version 14.0.1, moving from the earlier stipulation of version 13.0.0 or newer. This update is in response to addressing security concerns highlighted by CVE-2019-12410.\r\nhttps://github.com/descarteslabs/descarteslabs-python/commit/bc51d674b7245c708e49080f3819d66ecc88fab5", + "cve": "CVE-2019-12410", + "id": "pyup.io-65085", + "more_info_path": "/vulnerabilities/CVE-2019-12410/65085", "specs": [ "<3.0.2" ], @@ -33637,9 +33874,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41201", - "id": "pyup.io-43341", - "more_info_path": "/vulnerabilities/CVE-2021-41201/43341", + "cve": "CVE-2021-41222", + "id": "pyup.io-43329", + "more_info_path": "/vulnerabilities/CVE-2021-41222/43329", "specs": [ "<0.17.4rc0" ], @@ -33647,9 +33884,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41217", - "id": "pyup.io-43318", - "more_info_path": "/vulnerabilities/CVE-2021-41217/43318", + "cve": "CVE-2021-41204", + "id": "pyup.io-43327", + "more_info_path": "/vulnerabilities/CVE-2021-41204/43327", "specs": [ "<0.17.4rc0" ], @@ -33657,9 +33894,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41207", - "id": "pyup.io-43339", - "more_info_path": "/vulnerabilities/CVE-2021-41207/43339", + "cve": "CVE-2021-41205", + "id": "pyup.io-43336", + "more_info_path": "/vulnerabilities/CVE-2021-41205/43336", "specs": [ "<0.17.4rc0" ], @@ -33667,9 +33904,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41196", - "id": "pyup.io-43315", - "more_info_path": "/vulnerabilities/CVE-2021-41196/43315", + "cve": "CVE-2021-41212", + "id": "pyup.io-43337", + "more_info_path": "/vulnerabilities/CVE-2021-41212/43337", "specs": [ "<0.17.4rc0" ], @@ -33677,9 +33914,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41197", - "id": "pyup.io-43342", - "more_info_path": "/vulnerabilities/CVE-2021-41197/43342", + "cve": "CVE-2021-41226", + "id": "pyup.io-43322", + "more_info_path": "/vulnerabilities/CVE-2021-41226/43322", "specs": [ "<0.17.4rc0" ], @@ -33687,9 +33924,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41212", - "id": "pyup.io-43337", - "more_info_path": "/vulnerabilities/CVE-2021-41212/43337", + "cve": "CVE-2021-41210", + "id": "pyup.io-43338", + "more_info_path": "/vulnerabilities/CVE-2021-41210/43338", "specs": [ "<0.17.4rc0" ], @@ -33697,9 +33934,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41214", - "id": "pyup.io-43319", - "more_info_path": "/vulnerabilities/CVE-2021-41214/43319", + "cve": "CVE-2021-41224", + "id": "pyup.io-43330", + "more_info_path": "/vulnerabilities/CVE-2021-41224/43330", "specs": [ "<0.17.4rc0" ], @@ -33707,9 +33944,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41210", - "id": "pyup.io-43338", - "more_info_path": "/vulnerabilities/CVE-2021-41210/43338", + "cve": "CVE-2021-41225", + "id": "pyup.io-43321", + "more_info_path": "/vulnerabilities/CVE-2021-41225/43321", "specs": [ "<0.17.4rc0" ], @@ -33717,9 +33954,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41213", - "id": "pyup.io-43326", - "more_info_path": "/vulnerabilities/CVE-2021-41213/43326", + "cve": "CVE-2021-41201", + "id": "pyup.io-43341", + "more_info_path": "/vulnerabilities/CVE-2021-41201/43341", "specs": [ "<0.17.4rc0" ], @@ -33727,9 +33964,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41216", - "id": "pyup.io-43332", - "more_info_path": "/vulnerabilities/CVE-2021-41216/43332", + "cve": "CVE-2021-41202", + "id": "pyup.io-43340", + "more_info_path": "/vulnerabilities/CVE-2021-41202/43340", "specs": [ "<0.17.4rc0" ], @@ -33737,9 +33974,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41226", - "id": "pyup.io-43322", - "more_info_path": "/vulnerabilities/CVE-2021-41226/43322", + "cve": "CVE-2021-41195", + "id": "pyup.io-43343", + "more_info_path": "/vulnerabilities/CVE-2021-41195/43343", "specs": [ "<0.17.4rc0" ], @@ -33747,9 +33984,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41205", - "id": "pyup.io-43336", - "more_info_path": "/vulnerabilities/CVE-2021-41205/43336", + "cve": "CVE-2021-41214", + "id": "pyup.io-43319", + "more_info_path": "/vulnerabilities/CVE-2021-41214/43319", "specs": [ "<0.17.4rc0" ], @@ -33757,9 +33994,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41202", - "id": "pyup.io-43340", - "more_info_path": "/vulnerabilities/CVE-2021-41202/43340", + "cve": "CVE-2021-41209", + "id": "pyup.io-43325", + "more_info_path": "/vulnerabilities/CVE-2021-41209/43325", "specs": [ "<0.17.4rc0" ], @@ -33767,9 +34004,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41228", - "id": "pyup.io-43328", - "more_info_path": "/vulnerabilities/CVE-2021-41228/43328", + "cve": "CVE-2021-41215", + "id": "pyup.io-43333", + "more_info_path": "/vulnerabilities/CVE-2021-41215/43333", "specs": [ "<0.17.4rc0" ], @@ -33777,9 +34014,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41222", - "id": "pyup.io-43329", - "more_info_path": "/vulnerabilities/CVE-2021-41222/43329", + "cve": "CVE-2021-41221", + "id": "pyup.io-43324", + "more_info_path": "/vulnerabilities/CVE-2021-41221/43324", "specs": [ "<0.17.4rc0" ], @@ -33787,9 +34024,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41203", - "id": "pyup.io-43316", - "more_info_path": "/vulnerabilities/CVE-2021-41203/43316", + "cve": "CVE-2021-41219", + "id": "pyup.io-43320", + "more_info_path": "/vulnerabilities/CVE-2021-41219/43320", "specs": [ "<0.17.4rc0" ], @@ -33797,9 +34034,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41209", - "id": "pyup.io-43325", - "more_info_path": "/vulnerabilities/CVE-2021-41209/43325", + "cve": "CVE-2021-41228", + "id": "pyup.io-43328", + "more_info_path": "/vulnerabilities/CVE-2021-41228/43328", "specs": [ "<0.17.4rc0" ], @@ -33807,9 +34044,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41208", - "id": "pyup.io-43334", - "more_info_path": "/vulnerabilities/CVE-2021-41208/43334", + "cve": "CVE-2021-41227", + "id": "pyup.io-43323", + "more_info_path": "/vulnerabilities/CVE-2021-41227/43323", "specs": [ "<0.17.4rc0" ], @@ -33817,9 +34054,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41199", - "id": "pyup.io-42944", - "more_info_path": "/vulnerabilities/CVE-2021-41199/42944", + "cve": "CVE-2021-41216", + "id": "pyup.io-43332", + "more_info_path": "/vulnerabilities/CVE-2021-41216/43332", "specs": [ "<0.17.4rc0" ], @@ -33827,9 +34064,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41225", - "id": "pyup.io-43321", - "more_info_path": "/vulnerabilities/CVE-2021-41225/43321", + "cve": "CVE-2021-41213", + "id": "pyup.io-43326", + "more_info_path": "/vulnerabilities/CVE-2021-41213/43326", "specs": [ "<0.17.4rc0" ], @@ -33837,9 +34074,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41215", - "id": "pyup.io-43333", - "more_info_path": "/vulnerabilities/CVE-2021-41215/43333", + "cve": "CVE-2021-41218", + "id": "pyup.io-43331", + "more_info_path": "/vulnerabilities/CVE-2021-41218/43331", "specs": [ "<0.17.4rc0" ], @@ -33847,9 +34084,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41198", - "id": "pyup.io-43344", - "more_info_path": "/vulnerabilities/CVE-2021-41198/43344", + "cve": "CVE-2021-41208", + "id": "pyup.io-43334", + "more_info_path": "/vulnerabilities/CVE-2021-41208/43334", "specs": [ "<0.17.4rc0" ], @@ -33857,9 +34094,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41204", - "id": "pyup.io-43327", - "more_info_path": "/vulnerabilities/CVE-2021-41204/43327", + "cve": "CVE-2021-41207", + "id": "pyup.io-43339", + "more_info_path": "/vulnerabilities/CVE-2021-41207/43339", "specs": [ "<0.17.4rc0" ], @@ -33877,9 +34114,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41218", - "id": "pyup.io-43331", - "more_info_path": "/vulnerabilities/CVE-2021-41218/43331", + "cve": "CVE-2021-41217", + "id": "pyup.io-43318", + "more_info_path": "/vulnerabilities/CVE-2021-41217/43318", "specs": [ "<0.17.4rc0" ], @@ -33887,9 +34124,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41195", - "id": "pyup.io-43343", - "more_info_path": "/vulnerabilities/CVE-2021-41195/43343", + "cve": "CVE-2021-41203", + "id": "pyup.io-43316", + "more_info_path": "/vulnerabilities/CVE-2021-41203/43316", "specs": [ "<0.17.4rc0" ], @@ -33897,9 +34134,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41221", - "id": "pyup.io-43324", - "more_info_path": "/vulnerabilities/CVE-2021-41221/43324", + "cve": "CVE-2021-41200", + "id": "pyup.io-43317", + "more_info_path": "/vulnerabilities/CVE-2021-41200/43317", "specs": [ "<0.17.4rc0" ], @@ -33907,9 +34144,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41227", - "id": "pyup.io-43323", - "more_info_path": "/vulnerabilities/CVE-2021-41227/43323", + "cve": "CVE-2021-41199", + "id": "pyup.io-42944", + "more_info_path": "/vulnerabilities/CVE-2021-41199/42944", "specs": [ "<0.17.4rc0" ], @@ -33917,9 +34154,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41224", - "id": "pyup.io-43330", - "more_info_path": "/vulnerabilities/CVE-2021-41224/43330", + "cve": "CVE-2021-41198", + "id": "pyup.io-43344", + "more_info_path": "/vulnerabilities/CVE-2021-41198/43344", "specs": [ "<0.17.4rc0" ], @@ -33927,9 +34164,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41200", - "id": "pyup.io-43317", - "more_info_path": "/vulnerabilities/CVE-2021-41200/43317", + "cve": "CVE-2021-41197", + "id": "pyup.io-43342", + "more_info_path": "/vulnerabilities/CVE-2021-41197/43342", "specs": [ "<0.17.4rc0" ], @@ -33937,9 +34174,9 @@ }, { "advisory": "Determined 0.17.4rc0 includes images updates (to Tensorflow v2.4.4, v2.5.2 and v2.6.2) to include security fixes.", - "cve": "CVE-2021-41219", - "id": "pyup.io-43320", - "more_info_path": "/vulnerabilities/CVE-2021-41219/43320", + "cve": "CVE-2021-41196", + "id": "pyup.io-43315", + "more_info_path": "/vulnerabilities/CVE-2021-41196/43315", "specs": [ "<0.17.4rc0" ], @@ -33955,16 +34192,6 @@ ], "v": "<0.17.5" }, - { - "advisory": "Determined 0.17.6 updates env images to include security fixes.\r\nhttps://github.com/determined-ai/determined/pull/3415/commits/18fc5278cd589089dd753f687ec606499117029d", - "cve": "CVE-2020-10108", - "id": "pyup.io-44642", - "more_info_path": "/vulnerabilities/CVE-2020-10108/44642", - "specs": [ - "<0.17.6" - ], - "v": "<0.17.6" - }, { "advisory": "Determined 0.17.6 updates env images to include security fixes.\r\nhttps://github.com/determined-ai/determined/pull/3415/commits/18fc5278cd589089dd753f687ec606499117029d", "cve": "CVE-2020-10109", @@ -33977,9 +34204,9 @@ }, { "advisory": "Determined 0.17.6 updates env images to include security fixes.\r\nhttps://github.com/determined-ai/determined/pull/3415/commits/18fc5278cd589089dd753f687ec606499117029d", - "cve": "CVE-2019-14234", - "id": "pyup.io-54970", - "more_info_path": "/vulnerabilities/CVE-2019-14234/54970", + "cve": "CVE-2020-10108", + "id": "pyup.io-44642", + "more_info_path": "/vulnerabilities/CVE-2020-10108/44642", "specs": [ "<0.17.6" ], @@ -33997,9 +34224,9 @@ }, { "advisory": "Determined 0.17.6 updates env images to include security fixes.\r\nhttps://github.com/determined-ai/determined/pull/3415/commits/18fc5278cd589089dd753f687ec606499117029d", - "cve": "CVE-2019-9512", - "id": "pyup.io-54969", - "more_info_path": "/vulnerabilities/CVE-2019-9512/54969", + "cve": "CVE-2019-14234", + "id": "pyup.io-54970", + "more_info_path": "/vulnerabilities/CVE-2019-14234/54970", "specs": [ "<0.17.6" ], @@ -34015,6 +34242,16 @@ ], "v": "<0.17.6" }, + { + "advisory": "Determined 0.17.6 updates env images to include security fixes.\r\nhttps://github.com/determined-ai/determined/pull/3415/commits/18fc5278cd589089dd753f687ec606499117029d", + "cve": "CVE-2019-9512", + "id": "pyup.io-54969", + "more_info_path": "/vulnerabilities/CVE-2019-9512/54969", + "specs": [ + "<0.17.6" + ], + "v": "<0.17.6" + }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", "cve": "CVE-2022-27777", @@ -34027,9 +34264,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29194", - "id": "pyup.io-49541", - "more_info_path": "/vulnerabilities/CVE-2022-29194/49541", + "cve": "CVE-2022-22576", + "id": "pyup.io-49529", + "more_info_path": "/vulnerabilities/CVE-2022-22576/49529", "specs": [ "<0.18.2" ], @@ -34037,9 +34274,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29196", - "id": "pyup.io-49543", - "more_info_path": "/vulnerabilities/CVE-2022-29196/49543", + "cve": "CVE-2022-30115", + "id": "pyup.io-49561", + "more_info_path": "/vulnerabilities/CVE-2022-30115/49561", "specs": [ "<0.18.2" ], @@ -34047,9 +34284,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29208", - "id": "pyup.io-49555", - "more_info_path": "/vulnerabilities/CVE-2022-29208/49555", + "cve": "CVE-2022-27780", + "id": "pyup.io-49536", + "more_info_path": "/vulnerabilities/CVE-2022-27780/49536", "specs": [ "<0.18.2" ], @@ -34067,9 +34304,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-22576", - "id": "pyup.io-49529", - "more_info_path": "/vulnerabilities/CVE-2022-22576/49529", + "cve": "CVE-2022-29211", + "id": "pyup.io-49557", + "more_info_path": "/vulnerabilities/CVE-2022-29211/49557", "specs": [ "<0.18.2" ], @@ -34077,9 +34314,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29197", - "id": "pyup.io-49544", - "more_info_path": "/vulnerabilities/CVE-2022-29197/49544", + "cve": "CVE-2022-29209", + "id": "pyup.io-49556", + "more_info_path": "/vulnerabilities/CVE-2022-29209/49556", "specs": [ "<0.18.2" ], @@ -34087,9 +34324,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29216", - "id": "pyup.io-49560", - "more_info_path": "/vulnerabilities/CVE-2022-29216/49560", + "cve": "CVE-2022-29206", + "id": "pyup.io-49553", + "more_info_path": "/vulnerabilities/CVE-2022-29206/49553", "specs": [ "<0.18.2" ], @@ -34097,9 +34334,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29193", - "id": "pyup.io-49540", - "more_info_path": "/vulnerabilities/CVE-2022-29193/49540", + "cve": "CVE-2022-29205", + "id": "pyup.io-49552", + "more_info_path": "/vulnerabilities/CVE-2022-29205/49552", "specs": [ "<0.18.2" ], @@ -34107,9 +34344,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-27776", - "id": "pyup.io-49532", - "more_info_path": "/vulnerabilities/CVE-2022-27776/49532", + "cve": "CVE-2022-29203", + "id": "pyup.io-49550", + "more_info_path": "/vulnerabilities/CVE-2022-29203/49550", "specs": [ "<0.18.2" ], @@ -34117,9 +34354,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-30115", - "id": "pyup.io-49561", - "more_info_path": "/vulnerabilities/CVE-2022-30115/49561", + "cve": "CVE-2022-29213", + "id": "pyup.io-49559", + "more_info_path": "/vulnerabilities/CVE-2022-29213/49559", "specs": [ "<0.18.2" ], @@ -34127,9 +34364,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-27774", - "id": "pyup.io-49530", - "more_info_path": "/vulnerabilities/CVE-2022-27774/49530", + "cve": "CVE-2022-29204", + "id": "pyup.io-49551", + "more_info_path": "/vulnerabilities/CVE-2022-29204/49551", "specs": [ "<0.18.2" ], @@ -34137,9 +34374,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29202", - "id": "pyup.io-49549", - "more_info_path": "/vulnerabilities/CVE-2022-29202/49549", + "cve": "CVE-2022-29199", + "id": "pyup.io-49546", + "more_info_path": "/vulnerabilities/CVE-2022-29199/49546", "specs": [ "<0.18.2" ], @@ -34147,9 +34384,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29192", - "id": "pyup.io-49539", - "more_info_path": "/vulnerabilities/CVE-2022-29192/49539", + "cve": "CVE-2022-29197", + "id": "pyup.io-49544", + "more_info_path": "/vulnerabilities/CVE-2022-29197/49544", "specs": [ "<0.18.2" ], @@ -34157,9 +34394,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29191", - "id": "pyup.io-49538", - "more_info_path": "/vulnerabilities/CVE-2022-29191/49538", + "cve": "CVE-2022-29195", + "id": "pyup.io-49542", + "more_info_path": "/vulnerabilities/CVE-2022-29195/49542", "specs": [ "<0.18.2" ], @@ -34167,9 +34404,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-27779", - "id": "pyup.io-49535", - "more_info_path": "/vulnerabilities/CVE-2022-27779/49535", + "cve": "CVE-2022-29201", + "id": "pyup.io-49548", + "more_info_path": "/vulnerabilities/CVE-2022-29201/49548", "specs": [ "<0.18.2" ], @@ -34177,9 +34414,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29195", - "id": "pyup.io-49542", - "more_info_path": "/vulnerabilities/CVE-2022-29195/49542", + "cve": "CVE-2022-29193", + "id": "pyup.io-49540", + "more_info_path": "/vulnerabilities/CVE-2022-29193/49540", "specs": [ "<0.18.2" ], @@ -34187,9 +34424,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29203", - "id": "pyup.io-49550", - "more_info_path": "/vulnerabilities/CVE-2022-29203/49550", + "cve": "CVE-2022-29194", + "id": "pyup.io-49541", + "more_info_path": "/vulnerabilities/CVE-2022-29194/49541", "specs": [ "<0.18.2" ], @@ -34197,9 +34434,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29205", - "id": "pyup.io-49552", - "more_info_path": "/vulnerabilities/CVE-2022-29205/49552", + "cve": "CVE-2022-29191", + "id": "pyup.io-49538", + "more_info_path": "/vulnerabilities/CVE-2022-29191/49538", "specs": [ "<0.18.2" ], @@ -34207,9 +34444,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29209", - "id": "pyup.io-49556", - "more_info_path": "/vulnerabilities/CVE-2022-29209/49556", + "cve": "CVE-2022-29216", + "id": "pyup.io-49560", + "more_info_path": "/vulnerabilities/CVE-2022-29216/49560", "specs": [ "<0.18.2" ], @@ -34217,9 +34454,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29200", - "id": "pyup.io-49547", - "more_info_path": "/vulnerabilities/CVE-2022-29200/49547", + "cve": "CVE-2022-27776", + "id": "pyup.io-49532", + "more_info_path": "/vulnerabilities/CVE-2022-27776/49532", "specs": [ "<0.18.2" ], @@ -34227,9 +34464,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-27781", - "id": "pyup.io-49537", - "more_info_path": "/vulnerabilities/CVE-2022-27781/49537", + "cve": "CVE-2022-29196", + "id": "pyup.io-49543", + "more_info_path": "/vulnerabilities/CVE-2022-29196/49543", "specs": [ "<0.18.2" ], @@ -34237,9 +34474,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29199", - "id": "pyup.io-49546", - "more_info_path": "/vulnerabilities/CVE-2022-29199/49546", + "cve": "CVE-2022-29208", + "id": "pyup.io-49555", + "more_info_path": "/vulnerabilities/CVE-2022-29208/49555", "specs": [ "<0.18.2" ], @@ -34247,9 +34484,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-27780", - "id": "pyup.io-49536", - "more_info_path": "/vulnerabilities/CVE-2022-27780/49536", + "cve": "CVE-2018-25032", + "id": "pyup.io-49422", + "more_info_path": "/vulnerabilities/CVE-2018-25032/49422", "specs": [ "<0.18.2" ], @@ -34257,9 +34494,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-27775", - "id": "pyup.io-49531", - "more_info_path": "/vulnerabilities/CVE-2022-27775/49531", + "cve": "CVE-2022-27781", + "id": "pyup.io-49537", + "more_info_path": "/vulnerabilities/CVE-2022-27781/49537", "specs": [ "<0.18.2" ], @@ -34267,9 +34504,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2018-25032", - "id": "pyup.io-49422", - "more_info_path": "/vulnerabilities/CVE-2018-25032/49422", + "cve": "CVE-2022-27774", + "id": "pyup.io-49530", + "more_info_path": "/vulnerabilities/CVE-2022-27774/49530", "specs": [ "<0.18.2" ], @@ -34277,9 +34514,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29211", - "id": "pyup.io-49557", - "more_info_path": "/vulnerabilities/CVE-2022-29211/49557", + "cve": "CVE-2022-29202", + "id": "pyup.io-49549", + "more_info_path": "/vulnerabilities/CVE-2022-29202/49549", "specs": [ "<0.18.2" ], @@ -34287,9 +34524,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29206", - "id": "pyup.io-49553", - "more_info_path": "/vulnerabilities/CVE-2022-29206/49553", + "cve": "CVE-2022-29192", + "id": "pyup.io-49539", + "more_info_path": "/vulnerabilities/CVE-2022-29192/49539", "specs": [ "<0.18.2" ], @@ -34297,9 +34534,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29204", - "id": "pyup.io-49551", - "more_info_path": "/vulnerabilities/CVE-2022-29204/49551", + "cve": "CVE-2022-27779", + "id": "pyup.io-49535", + "more_info_path": "/vulnerabilities/CVE-2022-27779/49535", "specs": [ "<0.18.2" ], @@ -34307,9 +34544,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29198", - "id": "pyup.io-49545", - "more_info_path": "/vulnerabilities/CVE-2022-29198/49545", + "cve": "CVE-2022-27775", + "id": "pyup.io-49531", + "more_info_path": "/vulnerabilities/CVE-2022-27775/49531", "specs": [ "<0.18.2" ], @@ -34317,9 +34554,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29207", - "id": "pyup.io-49554", - "more_info_path": "/vulnerabilities/CVE-2022-29207/49554", + "cve": "CVE-2022-29198", + "id": "pyup.io-49545", + "more_info_path": "/vulnerabilities/CVE-2022-29198/49545", "specs": [ "<0.18.2" ], @@ -34327,9 +34564,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-27778", - "id": "pyup.io-49534", - "more_info_path": "/vulnerabilities/CVE-2022-27778/49534", + "cve": "CVE-2022-29200", + "id": "pyup.io-49547", + "more_info_path": "/vulnerabilities/CVE-2022-29200/49547", "specs": [ "<0.18.2" ], @@ -34337,9 +34574,9 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29201", - "id": "pyup.io-49548", - "more_info_path": "/vulnerabilities/CVE-2022-29201/49548", + "cve": "CVE-2022-27778", + "id": "pyup.io-49534", + "more_info_path": "/vulnerabilities/CVE-2022-27778/49534", "specs": [ "<0.18.2" ], @@ -34347,59 +34584,59 @@ }, { "advisory": "Determined 0.18.2 updates its dependency 'TensorFlow' supported versions to 2.6.5, 2.7.3 and 2.8.2 to include security fixes.", - "cve": "CVE-2022-29213", - "id": "pyup.io-49559", - "more_info_path": "/vulnerabilities/CVE-2022-29213/49559", + "cve": "CVE-2022-29207", + "id": "pyup.io-49554", + "more_info_path": "/vulnerabilities/CVE-2022-29207/49554", "specs": [ "<0.18.2" ], "v": "<0.18.2" }, { - "advisory": "Determined 0.19.3 updates its NPM dependency 'moment' to v2.29.4 to include a security fix.", - "cve": "CVE-2022-31129", - "id": "pyup.io-50976", - "more_info_path": "/vulnerabilities/CVE-2022-31129/50976", + "advisory": "Determined 0.19.3 updates its NPM dependency 'ansi-regex' to v3.0.1 to include a security fix.", + "cve": "CVE-2021-3807", + "id": "pyup.io-50971", + "more_info_path": "/vulnerabilities/CVE-2021-3807/50971", "specs": [ "<0.19.3" ], "v": "<0.19.3" }, { - "advisory": "Determined 0.19.3 updates its NPM dependency 'eventsource' to v1.1.2 to include a security fix.", - "cve": "CVE-2022-1650", - "id": "pyup.io-50973", - "more_info_path": "/vulnerabilities/CVE-2022-1650/50973", + "advisory": "Determined 0.19.3 updates its NPM dependency 'follow-redirects' to v1.15.1 to include security fixes.", + "cve": "CVE-2022-0155", + "id": "pyup.io-50975", + "more_info_path": "/vulnerabilities/CVE-2022-0155/50975", "specs": [ "<0.19.3" ], "v": "<0.19.3" }, { - "advisory": "Determined 0.19.3 updates its NPM dependency 'ansi-regex' to v3.0.1 to include a security fix.", - "cve": "CVE-2021-3807", - "id": "pyup.io-50971", - "more_info_path": "/vulnerabilities/CVE-2021-3807/50971", + "advisory": "Determined 0.19.3 stops using the NPM package 'trim-newlines', preventing a security issue.", + "cve": "CVE-2021-33623", + "id": "pyup.io-50978", + "more_info_path": "/vulnerabilities/CVE-2021-33623/50978", "specs": [ "<0.19.3" ], "v": "<0.19.3" }, { - "advisory": "Determined 0.19.3 updates its NPM dependency 'terser' to v4.8.1 to include a security fix.", - "cve": "CVE-2022-25858", - "id": "pyup.io-50977", - "more_info_path": "/vulnerabilities/CVE-2022-25858/50977", + "advisory": "Determined 0.19.3 updates its NPM dependency 'eventsource' to v1.1.2 to include a security fix.", + "cve": "CVE-2022-1650", + "id": "pyup.io-50973", + "more_info_path": "/vulnerabilities/CVE-2022-1650/50973", "specs": [ "<0.19.3" ], "v": "<0.19.3" }, { - "advisory": "Determined 0.19.3 stops using the NPM package 'trim-newlines', preventing a security issue.", - "cve": "CVE-2021-33623", - "id": "pyup.io-50978", - "more_info_path": "/vulnerabilities/CVE-2021-33623/50978", + "advisory": "Determined 0.19.3 updates its NPM dependency 'url-parse' to v1.5.10 to include security fixes.", + "cve": "CVE-2022-0691", + "id": "pyup.io-50981", + "more_info_path": "/vulnerabilities/CVE-2022-0691/50981", "specs": [ "<0.19.3" ], @@ -34417,39 +34654,39 @@ }, { "advisory": "Determined 0.19.3 updates its NPM dependency 'url-parse' to v1.5.10 to include security fixes.", - "cve": "CVE-2022-0691", - "id": "pyup.io-50981", - "more_info_path": "/vulnerabilities/CVE-2022-0691/50981", + "cve": "CVE-2022-0639", + "id": "pyup.io-50979", + "more_info_path": "/vulnerabilities/CVE-2022-0639/50979", "specs": [ "<0.19.3" ], "v": "<0.19.3" }, { - "advisory": "Determined 0.19.3 updates its NPM dependency 'follow-redirects' to v1.15.1 to include security fixes.", - "cve": "CVE-2022-0536", - "id": "pyup.io-50974", - "more_info_path": "/vulnerabilities/CVE-2022-0536/50974", + "advisory": "Determined 0.19.3 updates its NPM dependency 'url-parse' to v1.5.10 to include security fixes.", + "cve": "CVE-2022-0512", + "id": "pyup.io-50982", + "more_info_path": "/vulnerabilities/CVE-2022-0512/50982", "specs": [ "<0.19.3" ], "v": "<0.19.3" }, { - "advisory": "Determined 0.19.3 updates its NPM dependency 'async' to v2.6.4 to include a security fix.", - "cve": "CVE-2021-43138", - "id": "pyup.io-50972", - "more_info_path": "/vulnerabilities/CVE-2021-43138/50972", + "advisory": "Determined 0.19.3 updates its NPM dependency 'moment' to v2.29.4 to include a security fix.", + "cve": "CVE-2022-31129", + "id": "pyup.io-50976", + "more_info_path": "/vulnerabilities/CVE-2022-31129/50976", "specs": [ "<0.19.3" ], "v": "<0.19.3" }, { - "advisory": "Determined 0.19.3 updates its NPM dependency 'url-parse' to v1.5.10 to include security fixes.", - "cve": "CVE-2022-0512", - "id": "pyup.io-50982", - "more_info_path": "/vulnerabilities/CVE-2022-0512/50982", + "advisory": "Determined 0.19.3 updates its NPM dependency 'terser' to v4.8.1 to include a security fix.", + "cve": "CVE-2022-25858", + "id": "pyup.io-50977", + "more_info_path": "/vulnerabilities/CVE-2022-25858/50977", "specs": [ "<0.19.3" ], @@ -34457,19 +34694,19 @@ }, { "advisory": "Determined 0.19.3 updates its NPM dependency 'follow-redirects' to v1.15.1 to include security fixes.", - "cve": "CVE-2022-0155", - "id": "pyup.io-50975", - "more_info_path": "/vulnerabilities/CVE-2022-0155/50975", + "cve": "CVE-2022-0536", + "id": "pyup.io-50974", + "more_info_path": "/vulnerabilities/CVE-2022-0536/50974", "specs": [ "<0.19.3" ], "v": "<0.19.3" }, { - "advisory": "Determined 0.19.3 updates its NPM dependency 'url-parse' to v1.5.10 to include security fixes.", - "cve": "CVE-2022-0639", - "id": "pyup.io-50979", - "more_info_path": "/vulnerabilities/CVE-2022-0639/50979", + "advisory": "Determined 0.19.3 updates its NPM dependency 'async' to v2.6.4 to include a security fix.", + "cve": "CVE-2021-43138", + "id": "pyup.io-50972", + "more_info_path": "/vulnerabilities/CVE-2021-43138/50972", "specs": [ "<0.19.3" ], @@ -34757,9 +34994,9 @@ }, { "advisory": "Directory-client-core 7.1.1 updates its dependency 'django' minimum requirement to v3.2.18 to include a security fixes.", - "cve": "CVE-2022-36359", - "id": "pyup.io-58790", - "more_info_path": "/vulnerabilities/CVE-2022-36359/58790", + "cve": "CVE-2023-24580", + "id": "pyup.io-58777", + "more_info_path": "/vulnerabilities/CVE-2023-24580/58777", "specs": [ "<7.1.1" ], @@ -34777,9 +35014,9 @@ }, { "advisory": "Directory-client-core 7.1.1 updates its dependency 'django' minimum requirement to v3.2.18 to include a security fixes.", - "cve": "CVE-2022-34265", - "id": "pyup.io-58788", - "more_info_path": "/vulnerabilities/CVE-2022-34265/58788", + "cve": "CVE-2022-36359", + "id": "pyup.io-58790", + "more_info_path": "/vulnerabilities/CVE-2022-36359/58790", "specs": [ "<7.1.1" ], @@ -34787,9 +35024,9 @@ }, { "advisory": "Directory-client-core 7.1.1 updates its dependency 'django' minimum requirement to v3.2.18 to include a security fixes.", - "cve": "CVE-2021-33203", - "id": "pyup.io-58791", - "more_info_path": "/vulnerabilities/CVE-2021-33203/58791", + "cve": "CVE-2022-34265", + "id": "pyup.io-58788", + "more_info_path": "/vulnerabilities/CVE-2022-34265/58788", "specs": [ "<7.1.1" ], @@ -34797,9 +35034,9 @@ }, { "advisory": "Directory-client-core 7.1.1 updates its dependency 'django' minimum requirement to v3.2.18 to include a security fixes.", - "cve": "CVE-2023-24580", - "id": "pyup.io-58777", - "more_info_path": "/vulnerabilities/CVE-2023-24580/58777", + "cve": "CVE-2021-33203", + "id": "pyup.io-58791", + "more_info_path": "/vulnerabilities/CVE-2021-33203/58791", "specs": [ "<7.1.1" ], @@ -35090,9 +35327,9 @@ "dispatch": [ { "advisory": "Dispatch 1.3.16 updates its dependency 'Django' to v3.1.8 to include security fixes.", - "cve": "CVE-2021-28658", - "id": "pyup.io-43729", - "more_info_path": "/vulnerabilities/CVE-2021-28658/43729", + "cve": "CVE-2021-23336", + "id": "pyup.io-40402", + "more_info_path": "/vulnerabilities/CVE-2021-23336/40402", "specs": [ "<1.3.16" ], @@ -35100,9 +35337,9 @@ }, { "advisory": "Dispatch 1.3.16 updates its dependency 'Django' to v3.1.8 to include security fixes.", - "cve": "CVE-2021-23336", - "id": "pyup.io-40402", - "more_info_path": "/vulnerabilities/CVE-2021-23336/40402", + "cve": "CVE-2021-28658", + "id": "pyup.io-43729", + "more_info_path": "/vulnerabilities/CVE-2021-28658/43729", "specs": [ "<1.3.16" ], @@ -35275,10 +35512,10 @@ "v": "<1.0.4,>=1.1a1,<1.1.1" }, { - "advisory": "The administrative interface in django.contrib.admin in Django before 1.1.3, 1.2.x before 1.2.4, and 1.3.x before 1.3 beta 1 does not properly restrict use of the query string to perform certain object filtering, which allows remote authenticated users to obtain sensitive information via a series of requests containing regular expressions, as demonstrated by a created_by__password__regex parameter.", - "cve": "CVE-2010-4534", - "id": "pyup.io-33058", - "more_info_path": "/vulnerabilities/CVE-2010-4534/33058", + "advisory": "The password reset functionality in django.contrib.auth in Django before 1.1.3, 1.2.x before 1.2.4, and 1.3.x before 1.3 beta 1 does not validate the length of a string representing a base36 timestamp, which allows remote attackers to cause a denial of service (resource consumption) via a URL that specifies a large base36 integer.", + "cve": "CVE-2010-4535", + "id": "pyup.io-33059", + "more_info_path": "/vulnerabilities/CVE-2010-4535/33059", "specs": [ "<1.1.3", ">=1.2a1,<1.2.4" @@ -35286,10 +35523,10 @@ "v": "<1.1.3,>=1.2a1,<1.2.4" }, { - "advisory": "The password reset functionality in django.contrib.auth in Django before 1.1.3, 1.2.x before 1.2.4, and 1.3.x before 1.3 beta 1 does not validate the length of a string representing a base36 timestamp, which allows remote attackers to cause a denial of service (resource consumption) via a URL that specifies a large base36 integer.", - "cve": "CVE-2010-4535", - "id": "pyup.io-33059", - "more_info_path": "/vulnerabilities/CVE-2010-4535/33059", + "advisory": "The administrative interface in django.contrib.admin in Django before 1.1.3, 1.2.x before 1.2.4, and 1.3.x before 1.3 beta 1 does not properly restrict use of the query string to perform certain object filtering, which allows remote authenticated users to obtain sensitive information via a series of requests containing regular expressions, as demonstrated by a created_by__password__regex parameter.", + "cve": "CVE-2010-4534", + "id": "pyup.io-33058", + "more_info_path": "/vulnerabilities/CVE-2010-4534/33058", "specs": [ "<1.1.3", ">=1.2a1,<1.2.4" @@ -35349,28 +35586,6 @@ ], "v": "<1.2.2" }, - { - "advisory": "Django 1.2.7 and 1.3.1 include a fix for CVE-2011-4139: Django before 1.2.7 and 1.3.x before 1.3.1 uses a request's HTTP Host header to construct a full URL in certain circumstances, which allows remote attackers to conduct cache poisoning attacks via a crafted request.\r\nhttps://www.djangoproject.com/weblog/2011/sep/09/security-releases-issued", - "cve": "CVE-2011-4139", - "id": "pyup.io-35348", - "more_info_path": "/vulnerabilities/CVE-2011-4139/35348", - "specs": [ - "<1.2.7", - ">=1.3a1,<1.3.1" - ], - "v": "<1.2.7,>=1.3a1,<1.3.1" - }, - { - "advisory": "The verify_exists functionality in the URLField implementation in Django before 1.2.7 and 1.3.x before 1.3.1 originally tests a URL's validity through a HEAD request, but then uses a GET request for the new target URL in the case of a redirect, which might allow remote attackers to trigger arbitrary GET requests with an unintended source IP address via a crafted Location header.", - "cve": "CVE-2011-4138", - "id": "pyup.io-33065", - "more_info_path": "/vulnerabilities/CVE-2011-4138/33065", - "specs": [ - "<1.2.7", - ">=1.3a1,<1.3.1" - ], - "v": "<1.2.7,>=1.3a1,<1.3.1" - }, { "advisory": "The verify_exists functionality in the URLField implementation in Django before 1.2.7 and 1.3.x before 1.3.1 relies on Python libraries that attempt access to an arbitrary URL with no timeout, which allows remote attackers to cause a denial of service (resource consumption) via a URL associated with (1) a slow response, (2) a completed TCP connection with no application data sent, or (3) a large amount of application data, a related issue to CVE-2011-1521.", "cve": "CVE-2011-4137", @@ -35405,15 +35620,26 @@ "v": "<1.2.7,>=1.3a1,<1.3.1" }, { - "advisory": "The (1) django.http.HttpResponseRedirect and (2) django.http.HttpResponsePermanentRedirect classes in Django before 1.3.2 and 1.4.x before 1.4.1 do not validate the scheme of a redirect target, which might allow remote attackers to conduct cross-site scripting (XSS) attacks via a data: URL.", - "cve": "CVE-2012-3442", - "id": "pyup.io-33067", - "more_info_path": "/vulnerabilities/CVE-2012-3442/33067", + "advisory": "Django 1.2.7 and 1.3.1 include a fix for CVE-2011-4139: Django before 1.2.7 and 1.3.x before 1.3.1 uses a request's HTTP Host header to construct a full URL in certain circumstances, which allows remote attackers to conduct cache poisoning attacks via a crafted request.\r\nhttps://www.djangoproject.com/weblog/2011/sep/09/security-releases-issued", + "cve": "CVE-2011-4139", + "id": "pyup.io-35348", + "more_info_path": "/vulnerabilities/CVE-2011-4139/35348", "specs": [ - "<1.3.2", - ">=1.4a1,<1.4.1" + "<1.2.7", + ">=1.3a1,<1.3.1" ], - "v": "<1.3.2,>=1.4a1,<1.4.1" + "v": "<1.2.7,>=1.3a1,<1.3.1" + }, + { + "advisory": "The verify_exists functionality in the URLField implementation in Django before 1.2.7 and 1.3.x before 1.3.1 originally tests a URL's validity through a HEAD request, but then uses a GET request for the new target URL in the case of a redirect, which might allow remote attackers to trigger arbitrary GET requests with an unintended source IP address via a crafted Location header.", + "cve": "CVE-2011-4138", + "id": "pyup.io-33065", + "more_info_path": "/vulnerabilities/CVE-2011-4138/33065", + "specs": [ + "<1.2.7", + ">=1.3a1,<1.3.1" + ], + "v": "<1.2.7,>=1.3a1,<1.3.1" }, { "advisory": "The django.forms.ImageField class in the form system in Django before 1.3.2 and 1.4.x before 1.4.1 completely decompresses image data during image validation, which allows remote attackers to cause a denial of service (memory consumption) by uploading an image file.", @@ -35437,6 +35663,17 @@ ], "v": "<1.3.2,>=1.4a1,<1.4.1" }, + { + "advisory": "The (1) django.http.HttpResponseRedirect and (2) django.http.HttpResponsePermanentRedirect classes in Django before 1.3.2 and 1.4.x before 1.4.1 do not validate the scheme of a redirect target, which might allow remote attackers to conduct cross-site scripting (XSS) attacks via a data: URL.", + "cve": "CVE-2012-3442", + "id": "pyup.io-33067", + "more_info_path": "/vulnerabilities/CVE-2012-3442/33067", + "specs": [ + "<1.3.2", + ">=1.4a1,<1.4.1" + ], + "v": "<1.3.2,>=1.4a1,<1.4.1" + }, { "advisory": "The django.http.HttpRequest.get_host function in Django 1.3.x before 1.3.4 and 1.4.x before 1.4.2 allows remote attackers to generate and display arbitrary URLs via crafted username and password Host header values.", "cve": "CVE-2012-4520", @@ -35488,10 +35725,10 @@ "v": "<1.4.13,>=1.5a1,<1.5.8,>=1.6a1,<1.6.5,>=1.7a1,<1.7b4" }, { - "advisory": "The administrative interface (contrib.admin) in Django before 1.4.14, 1.5.x before 1.5.9, 1.6.x before 1.6.6, and 1.7 before release candidate 3 does not check if a field represents a relationship between models, which allows remote authenticated users to obtain sensitive information via a to_field parameter in a popup action to an admin change form page, as demonstrated by a /admin/auth/user/?pop=1&t=password URI. See: CVE-2014-0483.", - "cve": "CVE-2014-0483", - "id": "pyup.io-35516", - "more_info_path": "/vulnerabilities/CVE-2014-0483/35516", + "advisory": "Django 1.4.14, 1.5.9, 1.6.6 and 1.7rc3 include a fix for CVE-2014-0481: The default configuration for the file upload handling system in Django before 1.4.14, 1.5.x before 1.5.9, 1.6.x before 1.6.6, and 1.7 before release candidate 3 uses a sequential file name generation process when a file with a conflicting name is uploaded, which allows remote attackers to cause a denial of service (CPU consumption) by unloading a multiple files with the same name.", + "cve": "CVE-2014-0481", + "id": "pyup.io-35514", + "more_info_path": "/vulnerabilities/CVE-2014-0481/35514", "specs": [ "<1.4.14", ">=1.5a1,<1.5.9", @@ -35514,10 +35751,10 @@ "v": "<1.4.14,>=1.5a1,<1.5.9,>=1.6a1,<1.6.6,>=1.7a1,<1.7rc3" }, { - "advisory": "Django 1.4.14, 1.5.9, 1.6.6 and 1.7rc3 include a fix for CVE-2014-0481: The default configuration for the file upload handling system in Django before 1.4.14, 1.5.x before 1.5.9, 1.6.x before 1.6.6, and 1.7 before release candidate 3 uses a sequential file name generation process when a file with a conflicting name is uploaded, which allows remote attackers to cause a denial of service (CPU consumption) by unloading a multiple files with the same name.", - "cve": "CVE-2014-0481", - "id": "pyup.io-35514", - "more_info_path": "/vulnerabilities/CVE-2014-0481/35514", + "advisory": "The administrative interface (contrib.admin) in Django before 1.4.14, 1.5.x before 1.5.9, 1.6.x before 1.6.6, and 1.7 before release candidate 3 does not check if a field represents a relationship between models, which allows remote authenticated users to obtain sensitive information via a to_field parameter in a popup action to an admin change form page, as demonstrated by a /admin/auth/user/?pop=1&t=password URI. See: CVE-2014-0483.", + "cve": "CVE-2014-0483", + "id": "pyup.io-35516", + "more_info_path": "/vulnerabilities/CVE-2014-0483/35516", "specs": [ "<1.4.14", ">=1.5a1,<1.5.9", @@ -35540,10 +35777,10 @@ "v": "<1.4.14,>=1.5a1,<1.5.9,>=1.6a1,<1.6.6,>=1.7a1,<1.7rc3" }, { - "advisory": "The django.views.static.serve view in Django before 1.4.18, 1.6.x before 1.6.10, and 1.7.x before 1.7.3 reads files an entire line at a time, which allows remote attackers to cause a denial of service (memory consumption) via a long line in a file.", - "cve": "CVE-2015-0221", - "id": "pyup.io-33072", - "more_info_path": "/vulnerabilities/CVE-2015-0221/33072", + "advisory": "Django before 1.4.18, 1.6.x before 1.6.10, and 1.7.x before 1.7.3 allows remote attackers to spoof WSGI headers by using an _ (underscore) character instead of a - (dash) character in an HTTP header, as demonstrated by an X-Auth_User header.", + "cve": "CVE-2015-0219", + "id": "pyup.io-33070", + "more_info_path": "/vulnerabilities/CVE-2015-0219/33070", "specs": [ "<1.4.18", ">=1.6a1,<1.6.10", @@ -35552,10 +35789,10 @@ "v": "<1.4.18,>=1.6a1,<1.6.10,>=1.7a1,<1.7.3" }, { - "advisory": "The django.util.http.is_safe_url function in Django before 1.4.18, 1.6.x before 1.6.10, and 1.7.x before 1.7.3 does not properly handle leading whitespaces, which allows remote attackers to conduct cross-site scripting (XSS) attacks via a crafted URL, related to redirect URLs, as demonstrated by a \"\\njavascript:\" URL.", - "cve": "CVE-2015-0220", - "id": "pyup.io-33071", - "more_info_path": "/vulnerabilities/CVE-2015-0220/33071", + "advisory": "The django.views.static.serve view in Django before 1.4.18, 1.6.x before 1.6.10, and 1.7.x before 1.7.3 reads files an entire line at a time, which allows remote attackers to cause a denial of service (memory consumption) via a long line in a file.", + "cve": "CVE-2015-0221", + "id": "pyup.io-33072", + "more_info_path": "/vulnerabilities/CVE-2015-0221/33072", "specs": [ "<1.4.18", ">=1.6a1,<1.6.10", @@ -35564,10 +35801,10 @@ "v": "<1.4.18,>=1.6a1,<1.6.10,>=1.7a1,<1.7.3" }, { - "advisory": "Django before 1.4.18, 1.6.x before 1.6.10, and 1.7.x before 1.7.3 allows remote attackers to spoof WSGI headers by using an _ (underscore) character instead of a - (dash) character in an HTTP header, as demonstrated by an X-Auth_User header.", - "cve": "CVE-2015-0219", - "id": "pyup.io-33070", - "more_info_path": "/vulnerabilities/CVE-2015-0219/33070", + "advisory": "The django.util.http.is_safe_url function in Django before 1.4.18, 1.6.x before 1.6.10, and 1.7.x before 1.7.3 does not properly handle leading whitespaces, which allows remote attackers to conduct cross-site scripting (XSS) attacks via a crafted URL, related to redirect URLs, as demonstrated by a \"\\njavascript:\" URL.", + "cve": "CVE-2015-0220", + "id": "pyup.io-33071", + "more_info_path": "/vulnerabilities/CVE-2015-0220/33071", "specs": [ "<1.4.18", ">=1.6a1,<1.6.10", @@ -35648,10 +35885,10 @@ "v": "<1.7.6,>=1.8a1,<1.8b2" }, { - "advisory": "The password hasher in contrib/auth/hashers.py in Django before 1.8.10 and 1.9.x before 1.9.3 allows remote attackers to enumerate users via a timing attack involving login requests.", - "cve": "CVE-2016-2513", - "id": "pyup.io-33074", - "more_info_path": "/vulnerabilities/CVE-2016-2513/33074", + "advisory": "The utils.http.is_safe_url function in Django before 1.8.10 and 1.9.x before 1.9.3 allows remote attackers to redirect users to arbitrary web sites and conduct phishing attacks or possibly conduct cross-site scripting (XSS) attacks via a URL containing basic authentication, as demonstrated by http://mysite.example.com\\@attacker.com.", + "cve": "CVE-2016-2512", + "id": "pyup.io-33073", + "more_info_path": "/vulnerabilities/CVE-2016-2512/33073", "specs": [ "<1.8.10", ">=1.9a1,<1.9.3" @@ -35659,10 +35896,10 @@ "v": "<1.8.10,>=1.9a1,<1.9.3" }, { - "advisory": "The utils.http.is_safe_url function in Django before 1.8.10 and 1.9.x before 1.9.3 allows remote attackers to redirect users to arbitrary web sites and conduct phishing attacks or possibly conduct cross-site scripting (XSS) attacks via a URL containing basic authentication, as demonstrated by http://mysite.example.com\\@attacker.com.", - "cve": "CVE-2016-2512", - "id": "pyup.io-33073", - "more_info_path": "/vulnerabilities/CVE-2016-2512/33073", + "advisory": "The password hasher in contrib/auth/hashers.py in Django before 1.8.10 and 1.9.x before 1.9.3 allows remote attackers to enumerate users via a timing attack involving login requests.", + "cve": "CVE-2016-2513", + "id": "pyup.io-33074", + "more_info_path": "/vulnerabilities/CVE-2016-2513/33074", "specs": [ "<1.8.10", ">=1.9a1,<1.9.3" @@ -35704,10 +35941,10 @@ "v": "<2.1.9,>=2.2a1,<2.2.2" }, { - "advisory": "Django 2.2.16, 3.0.10 and 3.1.1 include a fix for CVE-2020-24583: An issue was discovered in Django 2.2 before 2.2.16, 3.0 before 3.0.10, and 3.1 before 3.1.1 (when Python 3.7+ is used). FILE_UPLOAD_DIRECTORY_PERMISSIONS mode was not applied to intermediate-level directories created in the process of uploading files. It was also not applied to intermediate-level collected static directories when using the collectstatic management command.\r\n#NOTE: This vulnerability affects only users of Python versions above 3.7.\r\nhttps://www.djangoproject.com/weblog/2020/sep/01/security-releases", - "cve": "CVE-2020-24583", - "id": "pyup.io-38749", - "more_info_path": "/vulnerabilities/CVE-2020-24583/38749", + "advisory": "An issue was discovered in Django 2.2 before 2.2.16, 3.0 before 3.0.10, and 3.1 before 3.1.1 (when Python 3.7+ is used). The intermediate-level directories of the filesystem cache had the system's standard umask rather than 0o077.", + "cve": "CVE-2020-24584", + "id": "pyup.io-38752", + "more_info_path": "/vulnerabilities/CVE-2020-24584/38752", "specs": [ "<2.2.16", ">=3.0a1,<3.0.10", @@ -35716,10 +35953,10 @@ "v": "<2.2.16,>=3.0a1,<3.0.10,>=3.1a1,<3.1.1" }, { - "advisory": "An issue was discovered in Django 2.2 before 2.2.16, 3.0 before 3.0.10, and 3.1 before 3.1.1 (when Python 3.7+ is used). The intermediate-level directories of the filesystem cache had the system's standard umask rather than 0o077.", - "cve": "CVE-2020-24584", - "id": "pyup.io-38752", - "more_info_path": "/vulnerabilities/CVE-2020-24584/38752", + "advisory": "Django 2.2.16, 3.0.10 and 3.1.1 include a fix for CVE-2020-24583: An issue was discovered in Django 2.2 before 2.2.16, 3.0 before 3.0.10, and 3.1 before 3.1.1 (when Python 3.7+ is used). FILE_UPLOAD_DIRECTORY_PERMISSIONS mode was not applied to intermediate-level directories created in the process of uploading files. It was also not applied to intermediate-level collected static directories when using the collectstatic management command.\r\n#NOTE: This vulnerability affects only users of Python versions above 3.7.\r\nhttps://www.djangoproject.com/weblog/2020/sep/01/security-releases", + "cve": "CVE-2020-24583", + "id": "pyup.io-38749", + "more_info_path": "/vulnerabilities/CVE-2020-24583/38749", "specs": [ "<2.2.16", ">=3.0a1,<3.0.10", @@ -35752,10 +35989,10 @@ "v": "<2.2.25,>=3.2a1,<3.2.10,>=3.1a1,<3.1.14" }, { - "advisory": "Django 2.2.26, 3.2.11 and 4.0.1 include a fix for CVE-2021-45116: An issue was discovered in Django 2.2 before 2.2.26, 3.2 before 3.2.11, and 4.0 before 4.0.1. Due to leveraging the Django Template Language's variable resolution logic, the dictsort template filter was potentially vulnerable to information disclosure, or an unintended method call, if passed a suitably crafted key.\r\nhttps://www.djangoproject.com/weblog/2022/jan/04/security-releases", - "cve": "CVE-2021-45116", - "id": "pyup.io-44427", - "more_info_path": "/vulnerabilities/CVE-2021-45116/44427", + "advisory": "Django 2.2.26, 3.2.11 and 4.0.1 include a fix for CVE-2021-45452: Storage.save in Django 2.2 before 2.2.26, 3.2 before 3.2.11, and 4.0 before 4.0.1 allows directory traversal if crafted filenames are directly passed to it.\r\nhttps://www.djangoproject.com/weblog/2022/jan/04/security-releases/", + "cve": "CVE-2021-45452", + "id": "pyup.io-44426", + "more_info_path": "/vulnerabilities/CVE-2021-45452/44426", "specs": [ "<2.2.26", ">=3.0a1,<3.2.11", @@ -35764,10 +36001,10 @@ "v": "<2.2.26,>=3.0a1,<3.2.11,>=4.0a1,<4.0.1" }, { - "advisory": "Django 2.2.26, 3.2.11 and 4.0.1 include a fix for CVE-2021-45452: Storage.save in Django 2.2 before 2.2.26, 3.2 before 3.2.11, and 4.0 before 4.0.1 allows directory traversal if crafted filenames are directly passed to it.\r\nhttps://www.djangoproject.com/weblog/2022/jan/04/security-releases/", - "cve": "CVE-2021-45452", - "id": "pyup.io-44426", - "more_info_path": "/vulnerabilities/CVE-2021-45452/44426", + "advisory": "Django 2.2.26, 3.2.11 and 4.0.1 include a fix for CVE-2021-45116: An issue was discovered in Django 2.2 before 2.2.26, 3.2 before 3.2.11, and 4.0 before 4.0.1. Due to leveraging the Django Template Language's variable resolution logic, the dictsort template filter was potentially vulnerable to information disclosure, or an unintended method call, if passed a suitably crafted key.\r\nhttps://www.djangoproject.com/weblog/2022/jan/04/security-releases", + "cve": "CVE-2021-45116", + "id": "pyup.io-44427", + "more_info_path": "/vulnerabilities/CVE-2021-45116/44427", "specs": [ "<2.2.26", ">=3.0a1,<3.2.11", @@ -35788,10 +36025,10 @@ "v": "<2.2.26,>=3.0a1,<3.2.11,>=4.0a1,<4.0.1" }, { - "advisory": "The {% debug %} template tag in Django 2.2 before 2.2.27, 3.2 before 3.2.12, and 4.0 before 4.0.2 does not properly encode the current context. This may lead to XSS.", - "cve": "CVE-2022-22818", - "id": "pyup.io-44742", - "more_info_path": "/vulnerabilities/CVE-2022-22818/44742", + "advisory": "Django 2.2.27, 3.2.12 and 4.0.2 include a fix for CVE-2022-23833: Denial-of-service possibility in file uploads.\r\nhttps://www.djangoproject.com/weblog/2022/feb/01/security-releases", + "cve": "CVE-2022-23833", + "id": "pyup.io-44741", + "more_info_path": "/vulnerabilities/CVE-2022-23833/44741", "specs": [ "<2.2.27", ">=3.0a1,<3.2.12", @@ -35800,10 +36037,10 @@ "v": "<2.2.27,>=3.0a1,<3.2.12,>=4.0a1,<4.0.2" }, { - "advisory": "Django 2.2.27, 3.2.12 and 4.0.2 include a fix for CVE-2022-23833: Denial-of-service possibility in file uploads.\r\nhttps://www.djangoproject.com/weblog/2022/feb/01/security-releases", - "cve": "CVE-2022-23833", - "id": "pyup.io-44741", - "more_info_path": "/vulnerabilities/CVE-2022-23833/44741", + "advisory": "The {% debug %} template tag in Django 2.2 before 2.2.27, 3.2 before 3.2.12, and 4.0 before 4.0.2 does not properly encode the current context. This may lead to XSS.", + "cve": "CVE-2022-22818", + "id": "pyup.io-44742", + "more_info_path": "/vulnerabilities/CVE-2022-22818/44742", "specs": [ "<2.2.27", ">=3.0a1,<3.2.12", @@ -35812,10 +36049,10 @@ "v": "<2.2.27,>=3.0a1,<3.2.12,>=4.0a1,<4.0.2" }, { - "advisory": "Django 2.2.28, 3.2.13 and 4.0.4 include a fix for CVE-2022-28346: An issue was discovered in Django 2.2 before 2.2.28, 3.2 before 3.2.13, and 4.0 before 4.0.4. QuerySet.annotate(), aggregate(), and extra() methods are subject to SQL injection in column aliases via a crafted dictionary (with dictionary expansion) as the passed **kwargs.\r\nhttps://www.djangoproject.com/weblog/2022/apr/11/security-releases", - "cve": "CVE-2022-28346", - "id": "pyup.io-48041", - "more_info_path": "/vulnerabilities/CVE-2022-28346/48041", + "advisory": "Django 2.2.28, 3.2.13 and 4.0.4 include a fix for CVE-2022-28347: A SQL injection issue was discovered in QuerySet.explain() in Django 2.2 before 2.2.28, 3.2 before 3.2.13, and 4.0 before 4.0.4. This occurs by passing a crafted dictionary (with dictionary expansion) as the **options argument, and placing the injection payload in an option name.\r\nhttps://www.djangoproject.com/weblog/2022/apr/11/security-releases", + "cve": "CVE-2022-28347", + "id": "pyup.io-48040", + "more_info_path": "/vulnerabilities/CVE-2022-28347/48040", "specs": [ "<2.2.28", ">=3.0a1,<3.2.13", @@ -35824,10 +36061,10 @@ "v": "<2.2.28,>=3.0a1,<3.2.13,>=4.0a1,<4.0.4" }, { - "advisory": "Django 2.2.28, 3.2.13 and 4.0.4 include a fix for CVE-2022-28347: A SQL injection issue was discovered in QuerySet.explain() in Django 2.2 before 2.2.28, 3.2 before 3.2.13, and 4.0 before 4.0.4. This occurs by passing a crafted dictionary (with dictionary expansion) as the **options argument, and placing the injection payload in an option name.\r\nhttps://www.djangoproject.com/weblog/2022/apr/11/security-releases", - "cve": "CVE-2022-28347", - "id": "pyup.io-48040", - "more_info_path": "/vulnerabilities/CVE-2022-28347/48040", + "advisory": "Django 2.2.28, 3.2.13 and 4.0.4 include a fix for CVE-2022-28346: An issue was discovered in Django 2.2 before 2.2.28, 3.2 before 3.2.13, and 4.0 before 4.0.4. QuerySet.annotate(), aggregate(), and extra() methods are subject to SQL injection in column aliases via a crafted dictionary (with dictionary expansion) as the passed **kwargs.\r\nhttps://www.djangoproject.com/weblog/2022/apr/11/security-releases", + "cve": "CVE-2022-28346", + "id": "pyup.io-48041", + "more_info_path": "/vulnerabilities/CVE-2022-28346/48041", "specs": [ "<2.2.28", ">=3.0a1,<3.2.13", @@ -35966,10 +36203,10 @@ "v": "<3.2.25,>=4.0a1,<4.2.11,>=5.0a1,<5.0.3" }, { - "advisory": "Affected versions of Django are potentially vulnerable to denial-of-service via the get_supported_language_variant() method. This method was susceptible to a denial-of-service attack when used with very long strings containing specific characters. Exploiting this vulnerability could cause significant delays or crashes in the affected application, potentially leading to service disruption.", - "cve": "CVE-2024-39614", - "id": "pyup.io-72111", - "more_info_path": "/vulnerabilities/CVE-2024-39614/72111", + "advisory": "Affected versions of Django are affected by a username enumeration vulnerability caused by timing differences in the django.contrib.auth.backends.ModelBackend.authenticate() method. This method allowed remote attackers to enumerate users through a timing attack involving login requests for users with unusable passwords. The timing difference in the authentication process exposed whether a username was valid or not, potentially aiding attackers in gaining unauthorized access.", + "cve": "CVE-2024-39329", + "id": "pyup.io-72109", + "more_info_path": "/vulnerabilities/CVE-2024-39329/72109", "specs": [ "<4.2.14", ">=5.0a1,<5.0.7" @@ -35987,17 +36224,6 @@ ], "v": "<4.2.14,>=5.0a1,<5.0.7" }, - { - "advisory": "Affected versions of Django are affected by a username enumeration vulnerability caused by timing differences in the django.contrib.auth.backends.ModelBackend.authenticate() method. This method allowed remote attackers to enumerate users through a timing attack involving login requests for users with unusable passwords. The timing difference in the authentication process exposed whether a username was valid or not, potentially aiding attackers in gaining unauthorized access.", - "cve": "CVE-2024-39329", - "id": "pyup.io-72109", - "more_info_path": "/vulnerabilities/CVE-2024-39329/72109", - "specs": [ - "<4.2.14", - ">=5.0a1,<5.0.7" - ], - "v": "<4.2.14,>=5.0a1,<5.0.7" - }, { "advisory": "Affected versions of Django are affected by a potential denial-of-service vulnerability in the django.utils.html.urlize() function. The urlize and urlizetrunc template filters were susceptible to a denial-of-service attack via certain inputs containing many brackets. An attacker could exploit this vulnerability to cause significant delays or crashes in the affected application.", "cve": "CVE-2024-38875", @@ -36010,15 +36236,15 @@ "v": "<4.2.14,>=5.0a1,<5.0.7" }, { - "advisory": "Django has a potential denial-of-service vulnerability in django.utils.html.urlize() and AdminURLFieldWidget. The urlize and urlizetrunc functions, along with AdminURLFieldWidget, are vulnerable to denial-of-service attacks when handling inputs with a very large number of Unicode characters.", - "cve": "CVE-2024-41991", - "id": "pyup.io-72520", - "more_info_path": "/vulnerabilities/CVE-2024-41991/72520", + "advisory": "Affected versions of Django are potentially vulnerable to denial-of-service via the get_supported_language_variant() method. This method was susceptible to a denial-of-service attack when used with very long strings containing specific characters. Exploiting this vulnerability could cause significant delays or crashes in the affected application, potentially leading to service disruption.", + "cve": "CVE-2024-39614", + "id": "pyup.io-72111", + "more_info_path": "/vulnerabilities/CVE-2024-39614/72111", "specs": [ - "<4.2.15", - ">=5.0a1,<5.0.8" + "<4.2.14", + ">=5.0a1,<5.0.7" ], - "v": "<4.2.15,>=5.0a1,<5.0.8" + "v": "<4.2.14,>=5.0a1,<5.0.7" }, { "advisory": "Django addresses a memory exhaustion issue in django.utils.numberformat.floatformat(). When floatformat receives a string representation of a number in scientific notation with a large exponent, it could lead to excessive memory consumption. To prevent this, decimals with more than 200 digits are now returned as-is.", @@ -36042,6 +36268,17 @@ ], "v": "<4.2.15,>=5.0a1,<5.0.8" }, + { + "advisory": "Django has a potential denial-of-service vulnerability in django.utils.html.urlize() and AdminURLFieldWidget. The urlize and urlizetrunc functions, along with AdminURLFieldWidget, are vulnerable to denial-of-service attacks when handling inputs with a very large number of Unicode characters.", + "cve": "CVE-2024-41991", + "id": "pyup.io-72520", + "more_info_path": "/vulnerabilities/CVE-2024-41991/72520", + "specs": [ + "<4.2.15", + ">=5.0a1,<5.0.8" + ], + "v": "<4.2.15,>=5.0a1,<5.0.8" + }, { "advisory": "A potential denial-of-service vulnerability has been identified in Django's urlize() and urlizetrunc() functions in django.utils.html. This vulnerability can be triggered by inputting huge strings containing a specific sequence of characters.", "cve": "CVE-2024-45230", @@ -36087,10 +36324,10 @@ "v": "<=0.95" }, { - "advisory": "Django versions until 1.3.6 and from 1.4 to 1.4.4 are vulnerable to Denial of Service (DoS) attacks. These attacks exploit a weakness during the deserialization of XML objects, related to CVE-2013-1664. DoS vulnerabilities, including this one, can severely impair system accessibility for legitimate users without necessarily compromising the security of the system. They achieve this by overwhelming the service with an excessive load, either through high CPU/memory consumption or by causing the system to crash.", - "cve": "PVE-2024-99804", - "id": "pyup.io-66011", - "more_info_path": "/vulnerabilities/PVE-2024-99804/66011", + "advisory": "Django versions until 1.3.6 and from 1.4 to 1.4.4 can be compromised through XML External Entity (XXE) attacks. These attacks allow an attacker to read arbitrary files by utilizing an XML external entity declaration along with an entity reference. The vulnerability stems from XML processing systems that, by default, accept external entity specifications. This can lead to unauthorized disclosure of sensitive information, such as passwords or private user data, by accessing local or remote files and possibly impact application availability by overloading the application with data\u2014raising the risk of a Denial of Service (DoS).", + "cve": "PVE-2024-99805", + "id": "pyup.io-66010", + "more_info_path": "/vulnerabilities/PVE-2024-99805/66010", "specs": [ ">=0,<1.3.6", ">=1.4,<1.4.4" @@ -36098,10 +36335,10 @@ "v": ">=0,<1.3.6,>=1.4,<1.4.4" }, { - "advisory": "Django versions until 1.3.6 and from 1.4 to 1.4.4 can be compromised through XML External Entity (XXE) attacks. These attacks allow an attacker to read arbitrary files by utilizing an XML external entity declaration along with an entity reference. The vulnerability stems from XML processing systems that, by default, accept external entity specifications. This can lead to unauthorized disclosure of sensitive information, such as passwords or private user data, by accessing local or remote files and possibly impact application availability by overloading the application with data\u2014raising the risk of a Denial of Service (DoS).", - "cve": "PVE-2024-99805", - "id": "pyup.io-66010", - "more_info_path": "/vulnerabilities/PVE-2024-99805/66010", + "advisory": "Django versions until 1.3.6 and from 1.4 to 1.4.4 are vulnerable to Denial of Service (DoS) attacks. These attacks exploit a weakness during the deserialization of XML objects, related to CVE-2013-1664. DoS vulnerabilities, including this one, can severely impair system accessibility for legitimate users without necessarily compromising the security of the system. They achieve this by overwhelming the service with an excessive load, either through high CPU/memory consumption or by causing the system to crash.", + "cve": "PVE-2024-99804", + "id": "pyup.io-66011", + "more_info_path": "/vulnerabilities/PVE-2024-99804/66011", "specs": [ ">=0,<1.3.6", ">=1.4,<1.4.4" @@ -36179,10 +36416,10 @@ "v": ">=1.11a1,<1.11.22,>=2.2a1,<2.2.3,>=2.1a1,<2.1.10" }, { - "advisory": "Django 1.11.23, 2.1.11 and 2.2.4 include a fix for CVE-2019-14234: Due to an error in shallow key transformation, key and index lookups for django.contrib.postgres.fields.JSONField, and key lookups for django.contrib.postgres.fields.HStoreField, were subject to SQL injection. This could, for example, be exploited via crafted use of \"OR 1=1\" in a key or index name to return all records, using a suitably crafted dictionary, with dictionary expansion, as the **kwargs passed to the QuerySet.filter() function.", - "cve": "CVE-2019-14234", - "id": "pyup.io-39592", - "more_info_path": "/vulnerabilities/CVE-2019-14234/39592", + "advisory": "Django 1.11.23, 2.1.11 and 2.2.4 includes a fix for CVE-2019-14235: If passed certain inputs, django.utils.encoding.uri_to_iri could lead to significant memory usage due to a recursion when repercent-encoding invalid UTF-8 octet sequences.", + "cve": "CVE-2019-14235", + "id": "pyup.io-39591", + "more_info_path": "/vulnerabilities/CVE-2019-14235/39591", "specs": [ ">=1.11a1,<1.11.23", ">=2.0a1,<2.1.11", @@ -36191,10 +36428,10 @@ "v": ">=1.11a1,<1.11.23,>=2.0a1,<2.1.11,>=2.2a1,<2.2.4" }, { - "advisory": "Django 1.11.23, 2.1.11, and 2.2.4 include a fix for CVE-2019-14233: Due to the behavior of the underlying HTMLParser, django.utils.html.strip_tags would be extremely slow to evaluate certain inputs containing large sequences of nested incomplete HTML entities.", - "cve": "CVE-2019-14233", - "id": "pyup.io-39593", - "more_info_path": "/vulnerabilities/CVE-2019-14233/39593", + "advisory": "Django 1.11.23, 2.1.11 and 2.2.4 include a fix for CVE-2019-14234: Due to an error in shallow key transformation, key and index lookups for django.contrib.postgres.fields.JSONField, and key lookups for django.contrib.postgres.fields.HStoreField, were subject to SQL injection. This could, for example, be exploited via crafted use of \"OR 1=1\" in a key or index name to return all records, using a suitably crafted dictionary, with dictionary expansion, as the **kwargs passed to the QuerySet.filter() function.", + "cve": "CVE-2019-14234", + "id": "pyup.io-39592", + "more_info_path": "/vulnerabilities/CVE-2019-14234/39592", "specs": [ ">=1.11a1,<1.11.23", ">=2.0a1,<2.1.11", @@ -36203,10 +36440,10 @@ "v": ">=1.11a1,<1.11.23,>=2.0a1,<2.1.11,>=2.2a1,<2.2.4" }, { - "advisory": "Django 1.11.23, 2.1.11 and 2.2.4 includes a fix for CVE-2019-14235: If passed certain inputs, django.utils.encoding.uri_to_iri could lead to significant memory usage due to a recursion when repercent-encoding invalid UTF-8 octet sequences.", - "cve": "CVE-2019-14235", - "id": "pyup.io-39591", - "more_info_path": "/vulnerabilities/CVE-2019-14235/39591", + "advisory": "Django 1.11.23, 2.1.11, and 2.2.4 include a fix for CVE-2019-14233: Due to the behavior of the underlying HTMLParser, django.utils.html.strip_tags would be extremely slow to evaluate certain inputs containing large sequences of nested incomplete HTML entities.", + "cve": "CVE-2019-14233", + "id": "pyup.io-39593", + "more_info_path": "/vulnerabilities/CVE-2019-14233/39593", "specs": [ ">=1.11a1,<1.11.23", ">=2.0a1,<2.1.11", @@ -36727,19 +36964,19 @@ }, { "advisory": "Django-airplane 1.1.0 and prior depends on an insecure Django version (2.2.10).", - "cve": "CVE-2021-28658", - "id": "pyup.io-43717", - "more_info_path": "/vulnerabilities/CVE-2021-28658/43717", + "cve": "CVE-2020-24584", + "id": "pyup.io-43720", + "more_info_path": "/vulnerabilities/CVE-2020-24584/43720", "specs": [ "<=1.1.0" ], "v": "<=1.1.0" }, { - "advisory": "Django-airplane 1.1.0 and prior depends on an insecure Django version (2.2.10).", - "cve": "CVE-2020-24584", - "id": "pyup.io-43720", - "more_info_path": "/vulnerabilities/CVE-2020-24584/43720", + "advisory": "Django-airplane 1.1.0 and prior versions include a vulnerable version of 'Django' (3.1.7).", + "cve": "CVE-2021-31542", + "id": "pyup.io-43716", + "more_info_path": "/vulnerabilities/CVE-2021-31542/43716", "specs": [ "<=1.1.0" ], @@ -36747,9 +36984,9 @@ }, { "advisory": "Django-airplane 1.1.0 and prior depends on an insecure Django version (2.2.10).", - "cve": "CVE-2021-33203", - "id": "pyup.io-43713", - "more_info_path": "/vulnerabilities/CVE-2021-33203/43713", + "cve": "CVE-2021-28658", + "id": "pyup.io-43717", + "more_info_path": "/vulnerabilities/CVE-2021-28658/43717", "specs": [ "<=1.1.0" ], @@ -36766,20 +37003,20 @@ "v": "<=1.1.0" }, { - "advisory": "Django-airplane 1.1.0 and prior includes a vulnerable version of 'Django' (3.1.7).", - "cve": "CVE-2021-32052", - "id": "pyup.io-43715", - "more_info_path": "/vulnerabilities/CVE-2021-32052/43715", + "advisory": "Django-airplane 1.1.0 and prior depends on an insecure Django version (2.2.10).", + "cve": "CVE-2021-3281", + "id": "pyup.io-43719", + "more_info_path": "/vulnerabilities/CVE-2021-3281/43719", "specs": [ "<=1.1.0" ], "v": "<=1.1.0" }, { - "advisory": "Django-airplane 1.1.0 and prior depends on an insecure Django version (2.2.10).", - "cve": "CVE-2021-3281", - "id": "pyup.io-43719", - "more_info_path": "/vulnerabilities/CVE-2021-3281/43719", + "advisory": "Django-airplane 1.1.0 and prior includes a vulnerable version of 'Django' (3.1.7).", + "cve": "CVE-2021-32052", + "id": "pyup.io-43715", + "more_info_path": "/vulnerabilities/CVE-2021-32052/43715", "specs": [ "<=1.1.0" ], @@ -36797,9 +37034,9 @@ }, { "advisory": "Django-airplane 1.1.0 and prior depends on an insecure Django version (2.2.10).", - "cve": "CVE-2020-9402", - "id": "pyup.io-43724", - "more_info_path": "/vulnerabilities/CVE-2020-9402/43724", + "cve": "CVE-2021-44420", + "id": "pyup.io-43712", + "more_info_path": "/vulnerabilities/CVE-2021-44420/43712", "specs": [ "<=1.1.0" ], @@ -36807,9 +37044,9 @@ }, { "advisory": "Django-airplane 1.1.0 and prior depends on an insecure Django version (2.2.10).", - "cve": "CVE-2020-13596", - "id": "pyup.io-43723", - "more_info_path": "/vulnerabilities/CVE-2020-13596/43723", + "cve": "CVE-2020-24583", + "id": "pyup.io-43721", + "more_info_path": "/vulnerabilities/CVE-2020-24583/43721", "specs": [ "<=1.1.0" ], @@ -36817,9 +37054,9 @@ }, { "advisory": "Django-airplane 1.1.0 and prior depends on an insecure Django version (2.2.10).", - "cve": "CVE-2021-44420", - "id": "pyup.io-43712", - "more_info_path": "/vulnerabilities/CVE-2021-44420/43712", + "cve": "CVE-2020-13596", + "id": "pyup.io-43723", + "more_info_path": "/vulnerabilities/CVE-2020-13596/43723", "specs": [ "<=1.1.0" ], @@ -36827,19 +37064,19 @@ }, { "advisory": "Django-airplane 1.1.0 and prior depends on an insecure Django version (2.2.10).", - "cve": "CVE-2020-24583", - "id": "pyup.io-43721", - "more_info_path": "/vulnerabilities/CVE-2020-24583/43721", + "cve": "CVE-2020-9402", + "id": "pyup.io-43724", + "more_info_path": "/vulnerabilities/CVE-2020-9402/43724", "specs": [ "<=1.1.0" ], "v": "<=1.1.0" }, { - "advisory": "Django-airplane 1.1.0 and prior versions include a vulnerable version of 'Django' (3.1.7).", - "cve": "CVE-2021-31542", - "id": "pyup.io-43716", - "more_info_path": "/vulnerabilities/CVE-2021-31542/43716", + "advisory": "Django-airplane 1.1.0 and prior depends on an insecure Django version (2.2.10).", + "cve": "CVE-2021-33203", + "id": "pyup.io-43713", + "more_info_path": "/vulnerabilities/CVE-2021-33203/43713", "specs": [ "<=1.1.0" ], @@ -37421,6 +37658,17 @@ "<4.0" ], "v": "<4.0" + }, + { + "advisory": "Affected versions of django-cms are vulnerable to Cross-Site Scripting (CWE-79). This vulnerability allows attackers to inject malicious scripts through page attributes, potentially compromising user sessions or executing unauthorized actions. The attack vector involves submitting crafted content to fields like page_title, which were previously not properly sanitized. The vulnerability existed in cms_tags.py where specific page attributes were not correctly escaped. This commit updates the code to use Django's escape function for all non-datetime page attributes, effectively mitigating the risk of XSS attacks.", + "cve": "CVE-2024-11319", + "id": "pyup.io-74253", + "more_info_path": "/vulnerabilities/CVE-2024-11319/74253", + "specs": [ + ">= 3.11.7,<3.11.9", + ">= 4.1.2,<4.1.4" + ], + "v": ">= 3.11.7,<3.11.9,>= 4.1.2,<4.1.4" } ], "django-cms-patched": [ @@ -37630,16 +37878,6 @@ ], "v": "<0.6.2" }, - { - "advisory": "Django-dsfr 0.6.2 updates its dependency 'Django' to v3.2.12 to include security fixes.", - "cve": "CVE-2021-45452", - "id": "pyup.io-45310", - "more_info_path": "/vulnerabilities/CVE-2021-45452/45310", - "specs": [ - "<0.6.2" - ], - "v": "<0.6.2" - }, { "advisory": "Django-dsfr 0.6.2 updates its dependency 'Django' to v3.2.12 to include security fixes.", "cve": "CVE-2021-45116", @@ -37669,6 +37907,16 @@ "<0.6.2" ], "v": "<0.6.2" + }, + { + "advisory": "Django-dsfr 0.6.2 updates its dependency 'Django' to v3.2.12 to include security fixes.", + "cve": "CVE-2021-45452", + "id": "pyup.io-45310", + "more_info_path": "/vulnerabilities/CVE-2021-45452/45310", + "specs": [ + "<0.6.2" + ], + "v": "<0.6.2" } ], "django-dynamic-breadcrumbs": [ @@ -37800,6 +38048,16 @@ } ], "django-filer": [ + { + "advisory": "Django-filer 3.0.0rc1 includes a fix for a Broken Access Control vulnerability. The staff user without proper permissions cannot browse the filer's folder structure, list files in a folder, add files, and move files and folders by this fix. Also, non-root users only see their own files in unsorted uploads and it shows uncategorized files to the owner or superuser if permissions are active.\r\nhttps://github.com/django-cms/django-filer/pull/1352\r\nhttps://github.com/django-cms/django-filer/commit/43434f7c60320dcfa719742ab84fbe2cfcffb6f1", + "cve": "PVE-2023-59514", + "id": "pyup.io-59514", + "more_info_path": "/vulnerabilities/PVE-2023-59514/59514", + "specs": [ + "<3.0.0rc1" + ], + "v": "<3.0.0rc1" + }, { "advisory": "Django-filer 3.0.0rc1 includes a fix for a XSS vulnerability.\r\nhttps://github.com/django-cms/django-filer/pull/1364", "cve": "PVE-2023-59208", @@ -37811,14 +38069,14 @@ "v": "<3.0.0rc1" }, { - "advisory": "Django-filer 3.0.0rc1 includes a fix for a Broken Access Control vulnerability. The staff user without proper permissions cannot browse the filer's folder structure, list files in a folder, add files, and move files and folders by this fix. Also, non-root users only see their own files in unsorted uploads and it shows uncategorized files to the owner or superuser if permissions are active.\r\nhttps://github.com/django-cms/django-filer/pull/1352\r\nhttps://github.com/django-cms/django-filer/commit/43434f7c60320dcfa719742ab84fbe2cfcffb6f1", - "cve": "PVE-2023-59514", - "id": "pyup.io-59514", - "more_info_path": "/vulnerabilities/PVE-2023-59514/59514", + "advisory": "Affected versions of django-filer are vulnerable to Unrestricted Upload of File with Dangerous Type (CWE-434). This vulnerability allows attackers to upload malicious binary files, potentially leading to data breaches or system compromise. The attack vector involves uploading crafted files through the application's upload functionality. The vulnerability exists due to permissive file validators that accept binary uploads without proper checks. To mitigate, upgrade to django-filer version which restricts binary and unknown file uploads by default and requires explicit validation and virus scanning for such files.", + "cve": "CVE-2024-11404", + "id": "pyup.io-74227", + "more_info_path": "/vulnerabilities/CVE-2024-11404/74227", "specs": [ - "<3.0.0rc1" + "<3.3.0" ], - "v": "<3.0.0rc1" + "v": "<3.3.0" } ], "django-filter": [ @@ -37943,20 +38201,20 @@ "v": "<2.15.2" }, { - "advisory": "Django-grappelli version 3.0.4 updates its grunt dependency to version 1.5.3 to address a path traversal vulnerability identified in CVE-2022-0436, which affects versions prior to 1.5.2.", - "cve": "CVE-2022-0436", - "id": "pyup.io-70378", - "more_info_path": "/vulnerabilities/CVE-2022-0436/70378", + "advisory": "Django-grappelli version 3.0.4 has updated its grunt dependency to version 1.5.3. This update addresses a race condition vulnerability identified in CVE-2022-1537, which impacts versions prior to 1.5.2.", + "cve": "CVE-2022-1537", + "id": "pyup.io-70380", + "more_info_path": "/vulnerabilities/CVE-2022-1537/70380", "specs": [ "<3.0.4" ], "v": "<3.0.4" }, { - "advisory": "Django-grappelli version 3.0.4 has updated its grunt dependency to version 1.5.3. This update addresses a race condition vulnerability identified in CVE-2022-1537, which impacts versions prior to 1.5.2.", - "cve": "CVE-2022-1537", - "id": "pyup.io-70380", - "more_info_path": "/vulnerabilities/CVE-2022-1537/70380", + "advisory": "Django-grappelli version 3.0.4 updates its grunt dependency to version 1.5.3 to address a path traversal vulnerability identified in CVE-2022-0436, which affects versions prior to 1.5.2.", + "cve": "CVE-2022-0436", + "id": "pyup.io-70378", + "more_info_path": "/vulnerabilities/CVE-2022-0436/70378", "specs": [ "<3.0.4" ], @@ -38108,16 +38366,6 @@ ], "v": "<1.0.4" }, - { - "advisory": "Django-howl 1.0.5 updates its dependency 'urllib3' to v1.25.8 to include a security fix.", - "cve": "CVE-2020-7212", - "id": "pyup.io-43659", - "more_info_path": "/vulnerabilities/CVE-2020-7212/43659", - "specs": [ - "<1.0.5" - ], - "v": "<1.0.5" - }, { "advisory": "Django-howl 1.0.5 updates its dependency 'Django' to v2.2.11 to include security fixes.", "cve": "CVE-2020-9402", @@ -38128,16 +38376,6 @@ ], "v": "<1.0.5" }, - { - "advisory": "Django-howl 1.0.5 updates its dependency 'Django' to v2.2.11 to include security fixes.", - "cve": "CVE-2020-7471", - "id": "pyup.io-43651", - "more_info_path": "/vulnerabilities/CVE-2020-7471/43651", - "specs": [ - "<1.0.5" - ], - "v": "<1.0.5" - }, { "advisory": "Django-howl 1.0.5 updates its dependency 'Django' to v2.2.11 to include security fixes.", "cve": "CVE-2019-19844", @@ -38198,6 +38436,26 @@ ], "v": "<1.0.5" }, + { + "advisory": "Django-howl 1.0.5 updates its dependency 'urllib3' to v1.25.8 to include a security fix.", + "cve": "CVE-2020-7212", + "id": "pyup.io-43659", + "more_info_path": "/vulnerabilities/CVE-2020-7212/43659", + "specs": [ + "<1.0.5" + ], + "v": "<1.0.5" + }, + { + "advisory": "Django-howl 1.0.5 updates its dependency 'Django' to v2.2.11 to include security fixes.", + "cve": "CVE-2020-7471", + "id": "pyup.io-43651", + "more_info_path": "/vulnerabilities/CVE-2020-7471/43651", + "specs": [ + "<1.0.5" + ], + "v": "<1.0.5" + }, { "advisory": "Django-howl 1.0.5 updates its dependency 'Django' to v2.2.11 to include security fixes.", "cve": "CVE-2019-14232", @@ -38252,16 +38510,6 @@ ], "v": "<1.1.0" }, - { - "advisory": "Django-idempotency-key 1.1.0 drops support for Django 1.x as it arrived to end of life.", - "cve": "CVE-2020-7471", - "id": "pyup.io-42977", - "more_info_path": "/vulnerabilities/CVE-2020-7471/42977", - "specs": [ - "<1.1.0" - ], - "v": "<1.1.0" - }, { "advisory": "Django-idempotency-key 1.1.0 drops support for Django 1.x as it arrived to end of life.", "cve": "CVE-2019-19844", @@ -38332,6 +38580,16 @@ ], "v": "<1.1.0" }, + { + "advisory": "Django-idempotency-key 1.1.0 drops support for Django 1.x as it arrived to end of life.", + "cve": "CVE-2020-7471", + "id": "pyup.io-42977", + "more_info_path": "/vulnerabilities/CVE-2020-7471/42977", + "specs": [ + "<1.1.0" + ], + "v": "<1.1.0" + }, { "advisory": "Django-idempotency-key 1.1.0 drops support for Django 1.x as it arrived to end of life.", "cve": "CVE-2019-14232", @@ -38453,10 +38711,10 @@ ], "django-kaio": [ { - "advisory": "Django-kaio 0.15.0 updates its dependency 'pygments' to v2.7.4 to include security fixes.", - "cve": "CVE-2021-20270", - "id": "pyup.io-48415", - "more_info_path": "/vulnerabilities/CVE-2021-20270/48415", + "advisory": "Django-kaio 0.15.0 updates its dependency 'babel' to v2.9.1 to include security fixes.", + "cve": "CVE-2021-42771", + "id": "pyup.io-48421", + "more_info_path": "/vulnerabilities/CVE-2021-42771/48421", "specs": [ "<0.15.0" ], @@ -38473,80 +38731,80 @@ "v": "<0.15.0" }, { - "advisory": "Django-kaio 0.15.0 updates its dependency 'urllib3' to v1.26.5 to include security fixes.", - "cve": "CVE-2021-33503", - "id": "pyup.io-48419", - "more_info_path": "/vulnerabilities/CVE-2021-33503/48419", + "advisory": "Django-kaio 0.15.0 updates its dependency 'pyyaml' to v5.4 to include security fixes.", + "cve": "CVE-2020-14343", + "id": "pyup.io-48412", + "more_info_path": "/vulnerabilities/CVE-2020-14343/48412", "specs": [ "<0.15.0" ], "v": "<0.15.0" }, { - "advisory": "Django-kaio 0.15.0 updates its dependency 'urllib3' to v1.26.5 to include security fixes.", - "cve": "CVE-2019-11236", - "id": "pyup.io-48417", - "more_info_path": "/vulnerabilities/CVE-2019-11236/48417", + "advisory": "Django-kaio 0.15.0 updates its dependency 'pygments' to v2.7.4 to include security fixes.", + "cve": "CVE-2021-20270", + "id": "pyup.io-48415", + "more_info_path": "/vulnerabilities/CVE-2021-20270/48415", "specs": [ "<0.15.0" ], "v": "<0.15.0" }, { - "advisory": "Django-kaio 0.15.0 updates its dependency 'babel' to v2.9.1 to include security fixes.", - "cve": "CVE-2021-42771", - "id": "pyup.io-48421", - "more_info_path": "/vulnerabilities/CVE-2021-42771/48421", + "advisory": "Django-kaio 0.15.0 updates its dependency 'urllib3' to v1.26.5 to include security fixes.", + "cve": "CVE-2021-33503", + "id": "pyup.io-48419", + "more_info_path": "/vulnerabilities/CVE-2021-33503/48419", "specs": [ "<0.15.0" ], "v": "<0.15.0" }, { - "advisory": "Django-kaio 0.15.0 updates its dependency 'pyyaml' to v5.4 to include security fixes.", - "cve": "CVE-2020-14343", - "id": "pyup.io-48412", - "more_info_path": "/vulnerabilities/CVE-2020-14343/48412", + "advisory": "Django-kaio 0.15.0 updates its dependency 'urllib3' to v1.26.5 to include security fixes.", + "cve": "CVE-2019-11236", + "id": "pyup.io-48417", + "more_info_path": "/vulnerabilities/CVE-2019-11236/48417", "specs": [ "<0.15.0" ], "v": "<0.15.0" }, { - "advisory": "Django-kaio 0.15.0 updates its dependency 'pygments' to v2.7.4 to include security fixes.", - "cve": "CVE-2021-27291", - "id": "pyup.io-48416", - "more_info_path": "/vulnerabilities/CVE-2021-27291/48416", + "advisory": "Django-kaio 0.15.0 updates its dependency 'urllib3' to v1.26.5 to include security fixes.", + "cve": "CVE-2020-26137", + "id": "pyup.io-48418", + "more_info_path": "/vulnerabilities/CVE-2020-26137/48418", "specs": [ "<0.15.0" ], "v": "<0.15.0" }, { - "advisory": "Django-kaio 0.15.0 updates its dependency 'jinja2' to v2.11.3 to include a security fix.", - "cve": "CVE-2020-28493", - "id": "pyup.io-48410", - "more_info_path": "/vulnerabilities/CVE-2020-28493/48410", + "advisory": "Django-kaio 0.15.0 updates its dependency 'pygments' to v2.7.4 to include security fixes.", + "cve": "CVE-2021-27291", + "id": "pyup.io-48416", + "more_info_path": "/vulnerabilities/CVE-2021-27291/48416", "specs": [ "<0.15.0" ], "v": "<0.15.0" }, { - "advisory": "Django-kaio 0.15.0 updates its dependency 'urllib3' to v1.26.5 to include security fixes.", - "cve": "CVE-2020-26137", - "id": "pyup.io-48418", - "more_info_path": "/vulnerabilities/CVE-2020-26137/48418", + "advisory": "Django-kaio 0.15.0 updates its dependency 'pyyaml' to v5.4 to include security fixes.", + "cve": "CVE-2020-1747", + "id": "pyup.io-48413", + "more_info_path": "/vulnerabilities/CVE-2020-1747/48413", "specs": [ "<0.15.0" ], "v": "<0.15.0" }, { - "advisory": "Django-kaio 0.15.0 updates its dependency 'pyyaml' to v5.4 to include security fixes.", - "cve": "CVE-2020-1747", - "id": "pyup.io-48413", - "more_info_path": "/vulnerabilities/CVE-2020-1747/48413", + "advisory": "Django-kaio 0.15.0 updates its dependency 'jinja2' to v2.11.3 to include a security fix.", + "cve": "CVE-2020-28493", + "id": "pyup.io-48410", + "more_info_path": "/vulnerabilities/CVE-2020-28493/48410", "specs": [ "<0.15.0" ], @@ -38580,9 +38838,9 @@ "django-loci": [ { "advisory": "Django-loci 0.4.3 updates its dependency 'Pillow' to versions ~=8.2.0 to include security fixes.", - "cve": "CVE-2021-25288", - "id": "pyup.io-45495", - "more_info_path": "/vulnerabilities/CVE-2021-25288/45495", + "cve": "CVE-2021-28677", + "id": "pyup.io-45492", + "more_info_path": "/vulnerabilities/CVE-2021-28677/45492", "specs": [ "<0.4.3" ], @@ -38590,9 +38848,9 @@ }, { "advisory": "Django-loci 0.4.3 updates its dependency 'Pillow' to versions ~=8.2.0 to include security fixes.", - "cve": "CVE-2021-25289", - "id": "pyup.io-45496", - "more_info_path": "/vulnerabilities/CVE-2021-25289/45496", + "cve": "CVE-2021-25288", + "id": "pyup.io-45495", + "more_info_path": "/vulnerabilities/CVE-2021-25288/45495", "specs": [ "<0.4.3" ], @@ -38600,9 +38858,9 @@ }, { "advisory": "Django-loci 0.4.3 updates its dependency 'Pillow' to versions ~=8.2.0 to include security fixes.", - "cve": "CVE-2021-25290", - "id": "pyup.io-45497", - "more_info_path": "/vulnerabilities/CVE-2021-25290/45497", + "cve": "CVE-2021-25289", + "id": "pyup.io-45496", + "more_info_path": "/vulnerabilities/CVE-2021-25289/45496", "specs": [ "<0.4.3" ], @@ -38610,9 +38868,9 @@ }, { "advisory": "Django-loci 0.4.3 updates its dependency 'Pillow' to versions ~=8.2.0 to include security fixes.", - "cve": "CVE-2021-28676", - "id": "pyup.io-45493", - "more_info_path": "/vulnerabilities/CVE-2021-28676/45493", + "cve": "CVE-2021-27921", + "id": "pyup.io-45500", + "more_info_path": "/vulnerabilities/CVE-2021-27921/45500", "specs": [ "<0.4.3" ], @@ -38620,9 +38878,9 @@ }, { "advisory": "Django-loci 0.4.3 updates its dependency 'Pillow' to versions ~=8.2.0 to include security fixes.", - "cve": "CVE-2021-27922", - "id": "pyup.io-45501", - "more_info_path": "/vulnerabilities/CVE-2021-27922/45501", + "cve": "CVE-2021-25292", + "id": "pyup.io-45499", + "more_info_path": "/vulnerabilities/CVE-2021-25292/45499", "specs": [ "<0.4.3" ], @@ -38630,9 +38888,9 @@ }, { "advisory": "Django-loci 0.4.3 updates its dependency 'Pillow' to versions ~=8.2.0 to include security fixes.", - "cve": "CVE-2021-27923", - "id": "pyup.io-45502", - "more_info_path": "/vulnerabilities/CVE-2021-27923/45502", + "cve": "CVE-2021-28676", + "id": "pyup.io-45493", + "more_info_path": "/vulnerabilities/CVE-2021-28676/45493", "specs": [ "<0.4.3" ], @@ -38640,9 +38898,9 @@ }, { "advisory": "Django-loci 0.4.3 updates its dependency 'Pillow' to versions ~=8.2.0 to include security fixes.", - "cve": "CVE-2021-25292", - "id": "pyup.io-45499", - "more_info_path": "/vulnerabilities/CVE-2021-25292/45499", + "cve": "CVE-2021-28678", + "id": "pyup.io-45404", + "more_info_path": "/vulnerabilities/CVE-2021-28678/45404", "specs": [ "<0.4.3" ], @@ -38660,9 +38918,9 @@ }, { "advisory": "Django-loci 0.4.3 updates its dependency 'Pillow' to versions ~=8.2.0 to include security fixes.", - "cve": "CVE-2021-28677", - "id": "pyup.io-45492", - "more_info_path": "/vulnerabilities/CVE-2021-28677/45492", + "cve": "CVE-2021-25290", + "id": "pyup.io-45497", + "more_info_path": "/vulnerabilities/CVE-2021-25290/45497", "specs": [ "<0.4.3" ], @@ -38670,9 +38928,9 @@ }, { "advisory": "Django-loci 0.4.3 updates its dependency 'Pillow' to versions ~=8.2.0 to include security fixes.", - "cve": "CVE-2021-28678", - "id": "pyup.io-45404", - "more_info_path": "/vulnerabilities/CVE-2021-28678/45404", + "cve": "CVE-2021-27922", + "id": "pyup.io-45501", + "more_info_path": "/vulnerabilities/CVE-2021-27922/45501", "specs": [ "<0.4.3" ], @@ -38680,9 +38938,9 @@ }, { "advisory": "Django-loci 0.4.3 updates its dependency 'Pillow' to versions ~=8.2.0 to include security fixes.", - "cve": "CVE-2021-27921", - "id": "pyup.io-45500", - "more_info_path": "/vulnerabilities/CVE-2021-27921/45500", + "cve": "CVE-2021-27923", + "id": "pyup.io-45502", + "more_info_path": "/vulnerabilities/CVE-2021-27923/45502", "specs": [ "<0.4.3" ], @@ -39203,16 +39461,6 @@ ], "v": "<0.9b1" }, - { - "advisory": "Django-newsletter 0.9b1 updates its dependency 'pillow' to v7.0.0 to include security fixes.", - "cve": "CVE-2020-5311", - "id": "pyup.io-43677", - "more_info_path": "/vulnerabilities/CVE-2020-5311/43677", - "specs": [ - "<0.9b1" - ], - "v": "<0.9b1" - }, { "advisory": "Django-newsletter 0.9b1 updates its dependency 'pillow' to v7.0.0 to include security fixes.", "cve": "CVE-2020-5310", @@ -39292,6 +39540,16 @@ "<0.9b1" ], "v": "<0.9b1" + }, + { + "advisory": "Django-newsletter 0.9b1 updates its dependency 'pillow' to v7.0.0 to include security fixes.", + "cve": "CVE-2020-5311", + "id": "pyup.io-43677", + "more_info_path": "/vulnerabilities/CVE-2020-5311/43677", + "specs": [ + "<0.9b1" + ], + "v": "<0.9b1" } ], "django-ninecms": [ @@ -39411,20 +39669,20 @@ ], "django-perms-provisioner": [ { - "advisory": "Django-perms-provisioner 0.0.4 updates PyYAML to v5.3.1 to include security fixes.", - "cve": "CVE-2020-1747", - "id": "pyup.io-38289", - "more_info_path": "/vulnerabilities/CVE-2020-1747/38289", + "advisory": "Django-perms-provisioner updates its dependency 'pyyaml' to v5.3.1 and code to include security fixes.\r\nhttps://github.com/labd/django-perms-provisioner/commit/1e65b781c47f6ba02805283a3ede56276ae14b44", + "cve": "CVE-2019-20477", + "id": "pyup.io-43456", + "more_info_path": "/vulnerabilities/CVE-2019-20477/43456", "specs": [ "<0.0.4" ], "v": "<0.0.4" }, { - "advisory": "Django-perms-provisioner updates its dependency 'pyyaml' to v5.3.1 and code to include security fixes.\r\nhttps://github.com/labd/django-perms-provisioner/commit/1e65b781c47f6ba02805283a3ede56276ae14b44", - "cve": "CVE-2019-20477", - "id": "pyup.io-43456", - "more_info_path": "/vulnerabilities/CVE-2019-20477/43456", + "advisory": "Django-perms-provisioner 0.0.4 updates PyYAML to v5.3.1 to include security fixes.", + "cve": "CVE-2020-1747", + "id": "pyup.io-38289", + "more_info_path": "/vulnerabilities/CVE-2020-1747/38289", "specs": [ "<0.0.4" ], @@ -39723,20 +39981,20 @@ "v": "<0.3.1" }, { - "advisory": "Django-secured-fields 0.3.1 updates its dependency 'django' to v4.0.2 to include security fixes.", - "cve": "CVE-2022-22818", - "id": "pyup.io-45850", - "more_info_path": "/vulnerabilities/CVE-2022-22818/45850", + "advisory": "Django-secured-fields 0.3.1 updates its dependency 'ipython' to v7.31.1 to include a security fix.", + "cve": "CVE-2022-21699", + "id": "pyup.io-45843", + "more_info_path": "/vulnerabilities/CVE-2022-21699/45843", "specs": [ "<0.3.1" ], "v": "<0.3.1" }, { - "advisory": "Django-secured-fields 0.3.1 updates its dependency 'ipython' to v7.31.1 to include a security fix.", - "cve": "CVE-2022-21699", - "id": "pyup.io-45843", - "more_info_path": "/vulnerabilities/CVE-2022-21699/45843", + "advisory": "Django-secured-fields 0.3.1 updates its dependency 'django' to v4.0.2 to include security fixes.", + "cve": "CVE-2022-22818", + "id": "pyup.io-45850", + "more_info_path": "/vulnerabilities/CVE-2022-22818/45850", "specs": [ "<0.3.1" ], @@ -39969,9 +40227,9 @@ }, { "advisory": "Django-spectator 12.0.1 updates its dependency 'pillow' to v9.0.1 to include security fixes.", - "cve": "CVE-2022-22816", - "id": "pyup.io-47779", - "more_info_path": "/vulnerabilities/CVE-2022-22816/47779", + "cve": "CVE-2022-24303", + "id": "pyup.io-47772", + "more_info_path": "/vulnerabilities/CVE-2022-24303/47772", "specs": [ "<12.0.1" ], @@ -39979,9 +40237,9 @@ }, { "advisory": "Django-spectator 12.0.1 updates its dependency 'pillow' to v9.0.1 to include security fixes.", - "cve": "CVE-2022-24303", - "id": "pyup.io-47772", - "more_info_path": "/vulnerabilities/CVE-2022-24303/47772", + "cve": "PVE-2021-44525", + "id": "pyup.io-47777", + "more_info_path": "/vulnerabilities/PVE-2021-44525/47777", "specs": [ "<12.0.1" ], @@ -39989,9 +40247,9 @@ }, { "advisory": "Django-spectator 12.0.1 updates its dependency 'pillow' to v9.0.1 to include security fixes.", - "cve": "CVE-2022-22815", - "id": "pyup.io-47780", - "more_info_path": "/vulnerabilities/CVE-2022-22815/47780", + "cve": "CVE-2022-22817", + "id": "pyup.io-47776", + "more_info_path": "/vulnerabilities/CVE-2022-22817/47776", "specs": [ "<12.0.1" ], @@ -39999,9 +40257,9 @@ }, { "advisory": "Django-spectator 12.0.1 updates its dependency 'pillow' to v9.0.1 to include security fixes.", - "cve": "CVE-2022-22817", - "id": "pyup.io-47776", - "more_info_path": "/vulnerabilities/CVE-2022-22817/47776", + "cve": "CVE-2022-22816", + "id": "pyup.io-47779", + "more_info_path": "/vulnerabilities/CVE-2022-22816/47779", "specs": [ "<12.0.1" ], @@ -40009,9 +40267,9 @@ }, { "advisory": "Django-spectator 12.0.1 updates its dependency 'pillow' to v9.0.1 to include security fixes.", - "cve": "PVE-2021-44525", - "id": "pyup.io-47777", - "more_info_path": "/vulnerabilities/PVE-2021-44525/47777", + "cve": "CVE-2022-22815", + "id": "pyup.io-47780", + "more_info_path": "/vulnerabilities/CVE-2022-22815/47780", "specs": [ "<12.0.1" ], @@ -40133,16 +40391,6 @@ ], "v": "<2.0.10" }, - { - "advisory": "Django-termsandconditions 2.0.10 updates its dependency 'Django' to v3.2.13 to include security fixes.", - "cve": "CVE-2022-23833", - "id": "pyup.io-49671", - "more_info_path": "/vulnerabilities/CVE-2022-23833/49671", - "specs": [ - "<2.0.10" - ], - "v": "<2.0.10" - }, { "advisory": "Django-termsandconditions 2.0.10 updates its dependency 'Django' to v3.2.13 to include security fixes.", "cve": "CVE-2021-45452", @@ -40165,19 +40413,19 @@ }, { "advisory": "Django-termsandconditions 2.0.10 updates its dependency 'Django' to v3.2.13 to include security fixes.", - "cve": "CVE-2021-45116", - "id": "pyup.io-49673", - "more_info_path": "/vulnerabilities/CVE-2021-45116/49673", + "cve": "CVE-2021-45115", + "id": "pyup.io-49674", + "more_info_path": "/vulnerabilities/CVE-2021-45115/49674", "specs": [ "<2.0.10" ], "v": "<2.0.10" }, { - "advisory": "Django-termsandconditions 2.0.10 updates its dependency 'Django' to v3.2.13 to include security fixes.", - "cve": "CVE-2021-45115", - "id": "pyup.io-49674", - "more_info_path": "/vulnerabilities/CVE-2021-45115/49674", + "advisory": "A vulnerability has been found in cyface Terms and Conditions Module up to 2.0.9 and classified as problematic. Affected by this vulnerability is the function returnTo of the file termsandconditions/views.py. The manipulation leads to open redirect. The attack can be launched remotely.", + "cve": "CVE-2022-4589", + "id": "pyup.io-52467", + "more_info_path": "/vulnerabilities/CVE-2022-4589/52467", "specs": [ "<2.0.10" ], @@ -40195,9 +40443,9 @@ }, { "advisory": "Django-termsandconditions 2.0.10 updates its dependency 'Django' to v3.2.13 to include security fixes.", - "cve": "CVE-2022-22818", - "id": "pyup.io-49670", - "more_info_path": "/vulnerabilities/CVE-2022-22818/49670", + "cve": "CVE-2021-45116", + "id": "pyup.io-49673", + "more_info_path": "/vulnerabilities/CVE-2021-45116/49673", "specs": [ "<2.0.10" ], @@ -40205,19 +40453,29 @@ }, { "advisory": "Django-termsandconditions 2.0.10 updates its dependency 'Django' to v3.2.13 to include security fixes.", - "cve": "CVE-2021-44420", - "id": "pyup.io-49675", - "more_info_path": "/vulnerabilities/CVE-2021-44420/49675", + "cve": "CVE-2022-23833", + "id": "pyup.io-49671", + "more_info_path": "/vulnerabilities/CVE-2022-23833/49671", "specs": [ "<2.0.10" ], "v": "<2.0.10" }, { - "advisory": "A vulnerability has been found in cyface Terms and Conditions Module up to 2.0.9 and classified as problematic. Affected by this vulnerability is the function returnTo of the file termsandconditions/views.py. The manipulation leads to open redirect. The attack can be launched remotely.", - "cve": "CVE-2022-4589", - "id": "pyup.io-52467", - "more_info_path": "/vulnerabilities/CVE-2022-4589/52467", + "advisory": "Django-termsandconditions 2.0.10 updates its dependency 'Django' to v3.2.13 to include security fixes.", + "cve": "CVE-2022-22818", + "id": "pyup.io-49670", + "more_info_path": "/vulnerabilities/CVE-2022-22818/49670", + "specs": [ + "<2.0.10" + ], + "v": "<2.0.10" + }, + { + "advisory": "Django-termsandconditions 2.0.10 updates its dependency 'Django' to v3.2.13 to include security fixes.", + "cve": "CVE-2021-44420", + "id": "pyup.io-49675", + "more_info_path": "/vulnerabilities/CVE-2021-44420/49675", "specs": [ "<2.0.10" ], @@ -40244,20 +40502,20 @@ "v": "<2.0.9" }, { - "advisory": "Django-termsandconditions 2.0.9 updates its dependency 'poetry' to v1.1.11 to include a security fix.", - "cve": "CVE-2022-26184", - "id": "pyup.io-49666", - "more_info_path": "/vulnerabilities/CVE-2022-26184/49666", + "advisory": "Django-termsandconditions 2.0.9 updates its dependency 'django' to v3.2.8 to include security fixes.", + "cve": "CVE-2021-33203", + "id": "pyup.io-49659", + "more_info_path": "/vulnerabilities/CVE-2021-33203/49659", "specs": [ "<2.0.9" ], "v": "<2.0.9" }, { - "advisory": "Django-termsandconditions 2.0.9 updates its dependency 'django' to v3.2.8 to include security fixes.", - "cve": "CVE-2021-31542", - "id": "pyup.io-49662", - "more_info_path": "/vulnerabilities/CVE-2021-31542/49662", + "advisory": "Django-termsandconditions 2.0.9 updates its dependency 'poetry' to v1.1.11 to include a security fix.", + "cve": "CVE-2022-26184", + "id": "pyup.io-49666", + "more_info_path": "/vulnerabilities/CVE-2022-26184/49666", "specs": [ "<2.0.9" ], @@ -40285,9 +40543,9 @@ }, { "advisory": "Django-termsandconditions 2.0.9 updates its dependency 'django' to v3.2.8 to include security fixes.", - "cve": "CVE-2021-33203", - "id": "pyup.io-49659", - "more_info_path": "/vulnerabilities/CVE-2021-33203/49659", + "cve": "CVE-2021-31542", + "id": "pyup.io-49662", + "more_info_path": "/vulnerabilities/CVE-2021-31542/49662", "specs": [ "<2.0.9" ], @@ -40295,9 +40553,9 @@ }, { "advisory": "Django-termsandconditions 2.0.9 updates its dependency 'django' to v3.2.8 to include security fixes.", - "cve": "CVE-2021-32052", - "id": "pyup.io-49661", - "more_info_path": "/vulnerabilities/CVE-2021-32052/49661", + "cve": "CVE-2021-28658", + "id": "pyup.io-49663", + "more_info_path": "/vulnerabilities/CVE-2021-28658/49663", "specs": [ "<2.0.9" ], @@ -40305,9 +40563,9 @@ }, { "advisory": "Django-termsandconditions 2.0.9 updates its dependency 'django' to v3.2.8 to include security fixes.", - "cve": "CVE-2021-28658", - "id": "pyup.io-49663", - "more_info_path": "/vulnerabilities/CVE-2021-28658/49663", + "cve": "CVE-2021-23336", + "id": "pyup.io-49664", + "more_info_path": "/vulnerabilities/CVE-2021-23336/49664", "specs": [ "<2.0.9" ], @@ -40315,9 +40573,9 @@ }, { "advisory": "Django-termsandconditions 2.0.9 updates its dependency 'django' to v3.2.8 to include security fixes.", - "cve": "CVE-2021-23336", - "id": "pyup.io-49664", - "more_info_path": "/vulnerabilities/CVE-2021-23336/49664", + "cve": "CVE-2021-32052", + "id": "pyup.io-49661", + "more_info_path": "/vulnerabilities/CVE-2021-32052/49661", "specs": [ "<2.0.9" ], @@ -40667,16 +40925,6 @@ } ], "django-websocket": [ - { - "advisory": "Django-websocket 0.3.0 and prior use a version of 'Django' (1.4.1) with known vulnerabilities.", - "cve": "CVE-2022-22818", - "id": "pyup.io-47968", - "more_info_path": "/vulnerabilities/CVE-2022-22818/47968", - "specs": [ - "<=0.3.0" - ], - "v": "<=0.3.0" - }, { "advisory": "Django-websocket 0.3.0 and prior use a version of 'Django' (1.4.1) with known vulnerabilities.", "cve": "CVE-2013-0305", @@ -40747,16 +40995,6 @@ ], "v": "<=0.3.0" }, - { - "advisory": "Django-websocket 0.3.0 and prior use a version of 'Django' (1.4.1) with known vulnerabilities.", - "cve": "CVE-2022-23833", - "id": "pyup.io-47969", - "more_info_path": "/vulnerabilities/CVE-2022-23833/47969", - "specs": [ - "<=0.3.0" - ], - "v": "<=0.3.0" - }, { "advisory": "Django-websocket 0.3.0 and prior use a version of 'Django' (1.4.1) with known vulnerabilities.", "cve": "CVE-2012-4520", @@ -40907,6 +41145,16 @@ ], "v": "<=0.3.0" }, + { + "advisory": "Django-websocket 0.3.0 and prior use a version of 'Django' (1.4.1) with known vulnerabilities.", + "cve": "CVE-2021-45452", + "id": "pyup.io-47967", + "more_info_path": "/vulnerabilities/CVE-2021-45452/47967", + "specs": [ + "<=0.3.0" + ], + "v": "<=0.3.0" + }, { "advisory": "Django-websocket 0.3.0 and prior use a version of 'Django' (1.4.1) with known vulnerabilities.", "cve": "CVE-2021-45115", @@ -40919,9 +41167,19 @@ }, { "advisory": "Django-websocket 0.3.0 and prior use a version of 'Django' (1.4.1) with known vulnerabilities.", - "cve": "CVE-2021-45452", - "id": "pyup.io-47967", - "more_info_path": "/vulnerabilities/CVE-2021-45452/47967", + "cve": "CVE-2022-23833", + "id": "pyup.io-47969", + "more_info_path": "/vulnerabilities/CVE-2022-23833/47969", + "specs": [ + "<=0.3.0" + ], + "v": "<=0.3.0" + }, + { + "advisory": "Django-websocket 0.3.0 and prior use a version of 'Django' (1.4.1) with known vulnerabilities.", + "cve": "CVE-2022-22818", + "id": "pyup.io-47968", + "more_info_path": "/vulnerabilities/CVE-2022-22818/47968", "specs": [ "<=0.3.0" ], @@ -40998,6 +41256,18 @@ "v": "<1.2.5" } ], + "djangocms-attributes-field": [ + { + "advisory": "Affected versions of djangocms_attributes_field are vulnerable to Cross-Site Scripting (CWE-79). This vulnerability allows attackers to inject malicious JavaScript through attribute fields, potentially leading to data theft or session hijacking. The attack vector involves uploading attributes with dangerous keys like on*, src, or href that are insufficiently validated. To exploit, an attacker can craft inputs via the application's attribute upload functionality. Mitigation involves upgrading to djangocms_attributes_field version 4.0.0 or later, which restricts these dangerous keys and enforces strict validation of attribute inputs.", + "cve": "CVE-2024-11406", + "id": "pyup.io-74229", + "more_info_path": "/vulnerabilities/CVE-2024-11406/74229", + "specs": [ + "<4.0.0" + ], + "v": "<4.0.0" + } + ], "djangocms-frontend": [ { "advisory": "Djangocms-frontend 1.0.1 avoids HTML injection into carousels when ckeditor is not installed.\r\nhttps://github.com/django-cms/djangocms-frontend/commit/80972e7bf9f7a361d26ca35bcaa5b7578277b3ff", @@ -41140,16 +41410,6 @@ ], "v": "<1.5" }, - { - "advisory": "djangorestframework-simplejwt version 5.3.1 and before is vulnerable to information disclosure. A user can access web application resources even after their account has been disabled due to missing user validation checks via the for_user method. See CVE-2024-22513.", - "cve": "CVE-2024-22513", - "id": "pyup.io-66963", - "more_info_path": "/vulnerabilities/CVE-2024-22513/66963", - "specs": [ - "<=5.3.1" - ], - "v": "<=5.3.1" - }, { "advisory": "Djangorestframework-simplejwt 5.2.2 includes a fix for a security flaw introduced in v5.2.1: Access tokens were not expiring.\r\nhttps://github.com/jazzband/djangorestframework-simplejwt/pull/629", "cve": "PVE-2022-51498", @@ -41159,6 +41419,16 @@ "==5.2.1" ], "v": "==5.2.1" + }, + { + "advisory": "Affected versions of Djangorestframework-simplejwt are vulnerable to information disclosure. A user can access web application resources even after their account has been disabled due to missing user validation checks via the for_user method.", + "cve": "CVE-2024-22513", + "id": "pyup.io-66963", + "more_info_path": "/vulnerabilities/CVE-2024-22513/66963", + "specs": [ + ">=0" + ], + "v": ">=0" } ], "djangosaml2": [ @@ -41449,10 +41719,10 @@ "v": "<1.0.12,>=1.1.0,<1.1.113,>=1.2.0,<1.2.65" }, { - "advisory": "Docassemble version 1.4.97 rectifies a security vulnerability present in versions up to 1.4.96. This flaw allowed for the creation of open redirect URLs.\r\nhttps://github.com/jhpyle/docassemble/commit/4801ac7ff7c90df00ac09523077930cdb6dea2aa", - "cve": "PVE-2024-65739", - "id": "pyup.io-65739", - "more_info_path": "/vulnerabilities/PVE-2024-65739/65739", + "advisory": "Docassemble version 1.4.97 addresses a critical security flaw, impacting versions from 1.4.53 to 1.4.96, where file contents within the filesystem could potentially be exposed. Given the high severity of this issue, users are strongly advised to update to the latest version immediately to secure their systems.", + "cve": "PVE-2024-65732", + "id": "pyup.io-65732", + "more_info_path": "/vulnerabilities/PVE-2024-65732/65732", "specs": [ "<1.4.97" ], @@ -41469,10 +41739,10 @@ "v": "<1.4.97" }, { - "advisory": "Docassemble version 1.4.97 addresses a critical security flaw, impacting versions from 1.4.53 to 1.4.96, where file contents within the filesystem could potentially be exposed. Given the high severity of this issue, users are strongly advised to update to the latest version immediately to secure their systems.", - "cve": "PVE-2024-65732", - "id": "pyup.io-65732", - "more_info_path": "/vulnerabilities/PVE-2024-65732/65732", + "advisory": "Docassemble version 1.4.97 rectifies a security vulnerability present in versions up to 1.4.96. This flaw allowed for the creation of open redirect URLs.\r\nhttps://github.com/jhpyle/docassemble/commit/4801ac7ff7c90df00ac09523077930cdb6dea2aa", + "cve": "PVE-2024-65739", + "id": "pyup.io-65739", + "more_info_path": "/vulnerabilities/PVE-2024-65739/65739", "specs": [ "<1.4.97" ], @@ -41741,6 +42011,18 @@ "v": ">=0.11.0,<13.0.0" } ], + "docksible": [ + { + "advisory": "Affected versions of the Docksible Nginx configurations are vulnerable to Improper Access Control (CWE-284). This vulnerability allows attackers to exploit the /xmlrpc.php endpoint in WordPress, enabling brute force attacks, DDoS attacks, and potential remote code execution. The issue arises from unrestricted access to /xmlrpc.php in the Nginx configuration files. It is exploitable remotely by sending crafted HTTP requests to the endpoint. To mitigate, update to the latest configuration which denies all access to /xmlrpc.php and disables its access logs, thereby securing the application against these attack vectors.", + "cve": "PVE-2024-74156", + "id": "pyup.io-74156", + "more_info_path": "/vulnerabilities/PVE-2024-74156/74156", + "specs": [ + "<0.6.1" + ], + "v": "<0.6.1" + } + ], "document-merge-service": [ { "advisory": "Document Merge Service is a document template merge service providing an API to manage templates and merge them with given data. Affected versions are vulnerable to remote code execution via server-side template injection which, when executed as root, can result in full takeover of the affected system. As of time of publication, no patched version exists, nor have any known workarounds been disclosed.", @@ -41931,20 +42213,20 @@ ], "doveseed": [ { - "advisory": "Doveseed version 2.0.4 updates its aiohttp dependency from 3.9.3 to 3.9.4 to address the security vulnerability identified as CVE-2024-30251.", - "cve": "CVE-2024-30251", - "id": "pyup.io-71194", - "more_info_path": "/vulnerabilities/CVE-2024-30251/71194", + "advisory": "Doveseed version 2.0.4 updates its jinja2 dependency from 3.1.3 to 3.1.4 to address the security vulnerability identified as CVE-2024-34064.", + "cve": "CVE-2024-34064", + "id": "pyup.io-71214", + "more_info_path": "/vulnerabilities/CVE-2024-34064/71214", "specs": [ "<2.0.4" ], "v": "<2.0.4" }, { - "advisory": "Doveseed version 2.0.4 updates its jinja2 dependency from 3.1.3 to 3.1.4 to address the security vulnerability identified as CVE-2024-34064.", - "cve": "CVE-2024-34064", - "id": "pyup.io-71214", - "more_info_path": "/vulnerabilities/CVE-2024-34064/71214", + "advisory": "Doveseed version 2.0.4 updates its aiohttp dependency from 3.9.3 to 3.9.4 to address the security vulnerability identified as CVE-2024-30251.", + "cve": "CVE-2024-30251", + "id": "pyup.io-71194", + "more_info_path": "/vulnerabilities/CVE-2024-30251/71194", "specs": [ "<2.0.4" ], @@ -42304,6 +42586,16 @@ } ], "dtale": [ + { + "advisory": "Affected versions of Dtale are vulnerable to authentication bypass and remote code execution (RCE) due to improper input validation. The vulnerability arises from a hardcoded `SECRET_KEY` in the flask configuration, allowing attackers to forge a session cookie if authentication is enabled. Additionally, the application fails to properly restrict custom filter queries, enabling attackers to execute arbitrary code on the server by bypassing the restriction on the `/update-settings` endpoint, even when `enable_custom_filters` is not enabled. This vulnerability allows attackers to bypass authentication mechanisms and execute remote code on the server.", + "cve": "CVE-2024-3408", + "id": "pyup.io-71779", + "more_info_path": "/vulnerabilities/CVE-2024-3408/71779", + "specs": [ + "<3.13.1" + ], + "v": "<3.13.1" + }, { "advisory": "Dtale affected versions are vulnerable to SQL injection attacks through custom query inputs. Malicious users could execute arbitrary queries, leading to unauthorized data access or manipulation. Fixed versions introduce a configurable flag to disable custom filters, mitigating this vulnerability. Is necessary to ensure the 'enable_custom_filters' flag is set to False by default when upgrading. If custom filters are required, implement additional input validation and consider using parameterized queries to further enhance security.", "cve": "PVE-2024-73151", @@ -42334,16 +42626,6 @@ ], "v": "<3.7.0" }, - { - "advisory": "man-group/dtale version 3.10.0 is vulnerable to an authentication bypass and remote code execution (RCE) due to improper input validation. The vulnerability arises from a hardcoded `SECRET_KEY` in the flask configuration, allowing attackers to forge a session cookie if authentication is enabled. Additionally, the application fails to properly restrict custom filter queries, enabling attackers to execute arbitrary code on the server by bypassing the restriction on the `/update-settings` endpoint, even when `enable_custom_filters` is not enabled. This vulnerability allows attackers to bypass authentication mechanisms and execute remote code on the server. See CVE-2024-3408.", - "cve": "CVE-2024-3408", - "id": "pyup.io-71779", - "more_info_path": "/vulnerabilities/CVE-2024-3408/71779", - "specs": [ - ">=0" - ], - "v": ">=0" - }, { "advisory": "D-Tale is a visualizer for Pandas data structures. Users hosting versions D-Tale prior to 3.9.0 publicly can be vulnerable to server-side request forgery (SSRF), allowing attackers to access files on the server. Users should upgrade to version 3.9.0, where the `Load From the Web` input is turned off by default. The only workaround for versions earlier than 3.9.0 is to only host D-Tale to trusted users.", "cve": "CVE-2024-21642", @@ -43072,10 +43354,10 @@ "v": "<2.3.0b0" }, { - "advisory": "Elyra 3.0.0 updates its dependency 'requests' to v2.25.1 to include a security fix.", - "cve": "CVE-2018-18074", - "id": "pyup.io-42730", - "more_info_path": "/vulnerabilities/CVE-2018-18074/42730", + "advisory": "Elyra 3.0.0 updates its dependency 'urllib3' to v1.26.5 to include a security fix.", + "cve": "CVE-2021-33503", + "id": "pyup.io-41074", + "more_info_path": "/vulnerabilities/CVE-2021-33503/41074", "specs": [ "<3.0.0" ], @@ -43092,10 +43374,10 @@ "v": "<3.0.0" }, { - "advisory": "Elyra 3.0.0 updates its dependency 'urllib3' to v1.26.5 to include a security fix.", - "cve": "CVE-2019-11236", - "id": "pyup.io-42729", - "more_info_path": "/vulnerabilities/CVE-2019-11236/42729", + "advisory": "Elyra 3.0.0 updates its dependency 'requests' to v2.25.1 to include a security fix.", + "cve": "CVE-2018-18074", + "id": "pyup.io-42730", + "more_info_path": "/vulnerabilities/CVE-2018-18074/42730", "specs": [ "<3.0.0" ], @@ -43103,9 +43385,9 @@ }, { "advisory": "Elyra 3.0.0 updates its dependency 'urllib3' to v1.26.5 to include a security fix.", - "cve": "CVE-2021-33503", - "id": "pyup.io-41074", - "more_info_path": "/vulnerabilities/CVE-2021-33503/41074", + "cve": "CVE-2019-11236", + "id": "pyup.io-42729", + "more_info_path": "/vulnerabilities/CVE-2019-11236/42729", "specs": [ "<3.0.0" ], @@ -43113,19 +43395,9 @@ }, { "advisory": "Elyra 3.11.0 updates its NPM dependency 'url-parse' to v1.5.10 to include security fixes.", - "cve": "CVE-2022-0639", - "id": "pyup.io-50877", - "more_info_path": "/vulnerabilities/CVE-2022-0639/50877", - "specs": [ - "<3.11.0" - ], - "v": "<3.11.0" - }, - { - "advisory": "Elyra 3.11.0 updates its NPM dependency 'url-parse' to v1.5.10 to include security fixes.", - "cve": "CVE-2022-0686", - "id": "pyup.io-50907", - "more_info_path": "/vulnerabilities/CVE-2022-0686/50907", + "cve": "CVE-2021-3664", + "id": "pyup.io-50910", + "more_info_path": "/vulnerabilities/CVE-2021-3664/50910", "specs": [ "<3.11.0" ], @@ -43143,9 +43415,9 @@ }, { "advisory": "Elyra 3.11.0 updates its NPM dependency 'url-parse' to v1.5.10 to include security fixes.", - "cve": "CVE-2022-0512", - "id": "pyup.io-50909", - "more_info_path": "/vulnerabilities/CVE-2022-0512/50909", + "cve": "CVE-2022-0686", + "id": "pyup.io-50907", + "more_info_path": "/vulnerabilities/CVE-2022-0686/50907", "specs": [ "<3.11.0" ], @@ -43153,9 +43425,9 @@ }, { "advisory": "Elyra 3.11.0 updates its NPM dependency 'url-parse' to v1.5.10 to include security fixes.", - "cve": "CVE-2021-3664", - "id": "pyup.io-50910", - "more_info_path": "/vulnerabilities/CVE-2021-3664/50910", + "cve": "CVE-2022-0639", + "id": "pyup.io-50877", + "more_info_path": "/vulnerabilities/CVE-2022-0639/50877", "specs": [ "<3.11.0" ], @@ -43181,6 +43453,16 @@ ], "v": "<3.11.0" }, + { + "advisory": "Elyra 3.11.0 updates its NPM dependency 'url-parse' to v1.5.10 to include security fixes.", + "cve": "CVE-2022-0512", + "id": "pyup.io-50909", + "more_info_path": "/vulnerabilities/CVE-2022-0512/50909", + "specs": [ + "<3.11.0" + ], + "v": "<3.11.0" + }, { "advisory": "Elyra 3.12.0 updates its NPM dependency 'parse-url' to versions '^8.1.0' to include a security fix.", "cve": "CVE-2022-2900", @@ -43211,6 +43493,16 @@ ], "v": "<3.7.0rc0" }, + { + "advisory": "Elyra 3.9.0 requires 'immer' versions ^9.0.7 to include security fixes.", + "cve": "CVE-2020-28477", + "id": "pyup.io-49389", + "more_info_path": "/vulnerabilities/CVE-2020-28477/49389", + "specs": [ + "<3.9.0" + ], + "v": "<3.9.0" + }, { "advisory": "Elyra 3.9.0 requires 'react-dev-utils' versions ^12.0.0 to include a security fix.", "cve": "CVE-2021-24033", @@ -43222,20 +43514,20 @@ "v": "<3.9.0" }, { - "advisory": "Elyra 3.9.0 requires 'node-forge' versions ^1.3.0 to include security fixes.", - "cve": "CVE-2022-24771", - "id": "pyup.io-49381", - "more_info_path": "/vulnerabilities/CVE-2022-24771/49381", + "advisory": "Elyra 3.9.0 requires 'ansi-html' versions ^0.0.8 to include a security fix.", + "cve": "CVE-2021-23424", + "id": "pyup.io-49386", + "more_info_path": "/vulnerabilities/CVE-2021-23424/49386", "specs": [ "<3.9.0" ], "v": "<3.9.0" }, { - "advisory": "Elyra 3.9.0 requires 'node-forge' versions ^1.3.0 to include security fixes.", - "cve": "CVE-2022-24772", - "id": "pyup.io-49382", - "more_info_path": "/vulnerabilities/CVE-2022-24772/49382", + "advisory": "Elyra 3.9.0 requires 'glob-parent' versions ^5.1.2 to include a security fix.", + "cve": "CVE-2020-28469", + "id": "pyup.io-49388", + "more_info_path": "/vulnerabilities/CVE-2020-28469/49388", "specs": [ "<3.9.0" ], @@ -43253,9 +43545,9 @@ }, { "advisory": "Elyra 3.9.0 requires 'node-forge' versions ^1.3.0 to include security fixes.", - "cve": "CVE-2022-0122", - "id": "pyup.io-49384", - "more_info_path": "/vulnerabilities/CVE-2022-0122/49384", + "cve": "CVE-2022-24772", + "id": "pyup.io-49382", + "more_info_path": "/vulnerabilities/CVE-2022-24772/49382", "specs": [ "<3.9.0" ], @@ -43272,20 +43564,10 @@ "v": "<3.9.0" }, { - "advisory": "Elyra 3.9.0 requires 'ansi-html' versions ^0.0.8 to include a security fix.", - "cve": "CVE-2021-23424", - "id": "pyup.io-49386", - "more_info_path": "/vulnerabilities/CVE-2021-23424/49386", - "specs": [ - "<3.9.0" - ], - "v": "<3.9.0" - }, - { - "advisory": "Elyra 3.9.0 requires 'glob-parent' versions ^5.1.2 to include a security fix.", - "cve": "CVE-2020-28469", - "id": "pyup.io-49388", - "more_info_path": "/vulnerabilities/CVE-2020-28469/49388", + "advisory": "Elyra 3.9.0 requires 'node-forge' versions ^1.3.0 to include security fixes.", + "cve": "CVE-2022-0122", + "id": "pyup.io-49384", + "more_info_path": "/vulnerabilities/CVE-2022-0122/49384", "specs": [ "<3.9.0" ], @@ -43293,9 +43575,9 @@ }, { "advisory": "Elyra 3.9.0 requires 'immer' versions ^9.0.7 to include security fixes.", - "cve": "CVE-2020-28477", - "id": "pyup.io-49389", - "more_info_path": "/vulnerabilities/CVE-2020-28477/49389", + "cve": "CVE-2021-23436", + "id": "pyup.io-49391", + "more_info_path": "/vulnerabilities/CVE-2021-23436/49391", "specs": [ "<3.9.0" ], @@ -43312,20 +43594,20 @@ "v": "<3.9.0" }, { - "advisory": "Elyra 3.9.0 requires 'immer' versions ^9.0.7 to include security fixes.", - "cve": "CVE-2021-23436", - "id": "pyup.io-49391", - "more_info_path": "/vulnerabilities/CVE-2021-23436/49391", + "advisory": "Elyra 3.9.0 requires 'ejs' versions ^3.1.7 to include a security fix.", + "cve": "CVE-2022-29078", + "id": "pyup.io-49387", + "more_info_path": "/vulnerabilities/CVE-2022-29078/49387", "specs": [ "<3.9.0" ], "v": "<3.9.0" }, { - "advisory": "Elyra 3.9.0 requires 'ejs' versions ^3.1.7 to include a security fix.", - "cve": "CVE-2022-29078", - "id": "pyup.io-49387", - "more_info_path": "/vulnerabilities/CVE-2022-29078/49387", + "advisory": "Elyra 3.9.0 requires 'node-forge' versions ^1.3.0 to include security fixes.", + "cve": "CVE-2022-24771", + "id": "pyup.io-49381", + "more_info_path": "/vulnerabilities/CVE-2022-24771/49381", "specs": [ "<3.9.0" ], @@ -43358,20 +43640,20 @@ ], "embedchain": [ { - "advisory": "The OpenAPI loader in Embedchain before 0.1.57 allows attackers to execute arbitrary code, related to the openapi.py yaml.load function argument.", - "cve": "CVE-2024-23731", - "id": "pyup.io-66691", - "more_info_path": "/vulnerabilities/CVE-2024-23731/66691", + "advisory": "The JSON loader in Embedchain before 0.1.57 allows a ReDoS (regular expression denial of service) via a long string to json.py.", + "cve": "CVE-2024-23732", + "id": "pyup.io-66692", + "more_info_path": "/vulnerabilities/CVE-2024-23732/66692", "specs": [ "<0.1.57" ], "v": "<0.1.57" }, { - "advisory": "The JSON loader in Embedchain before 0.1.57 allows a ReDoS (regular expression denial of service) via a long string to json.py.", - "cve": "CVE-2024-23732", - "id": "pyup.io-66692", - "more_info_path": "/vulnerabilities/CVE-2024-23732/66692", + "advisory": "The OpenAPI loader in Embedchain before 0.1.57 allows attackers to execute arbitrary code, related to the openapi.py yaml.load function argument.", + "cve": "CVE-2024-23731", + "id": "pyup.io-66691", + "more_info_path": "/vulnerabilities/CVE-2024-23731/66691", "specs": [ "<0.1.57" ], @@ -43380,10 +43662,10 @@ ], "embedded-topic-model": [ { - "advisory": "Embedded-topic-model 1.2.0 updates its dependency 'skicit-learn' to versions '>=1.3.*' to include a security fix.\r\nhttps://github.com/lffloyd/embedded-topic-model/commit/331fc0", - "cve": "PVE-2022-52255", - "id": "pyup.io-61010", - "more_info_path": "/vulnerabilities/PVE-2022-52255/61010", + "advisory": "Embedded-topic-model 1.2.0 updates its dependency 'scipy' to versions '>=1.11.*' to include security fixes.", + "cve": "CVE-2023-29824", + "id": "pyup.io-61024", + "more_info_path": "/vulnerabilities/CVE-2023-29824/61024", "specs": [ "<1.2.0" ], @@ -43400,10 +43682,10 @@ "v": "<1.2.0" }, { - "advisory": "Embedded-topic-model 1.2.0 updates its dependency 'scipy' to versions '>=1.11.*' to include security fixes.", - "cve": "CVE-2023-29824", - "id": "pyup.io-61024", - "more_info_path": "/vulnerabilities/CVE-2023-29824/61024", + "advisory": "Embedded-topic-model 1.2.0 updates its dependency 'skicit-learn' to versions '>=1.3.*' to include a security fix.\r\nhttps://github.com/lffloyd/embedded-topic-model/commit/331fc0", + "cve": "PVE-2022-52255", + "id": "pyup.io-61010", + "more_info_path": "/vulnerabilities/PVE-2022-52255/61010", "specs": [ "<1.2.0" ], @@ -43479,16 +43761,6 @@ ], "v": "<0.5.2" }, - { - "advisory": "Encapsia-cli 0.5.2 updates its dependency 'httpie' requirement to ^3.1.0 to include security fixes.", - "cve": "CVE-2022-24737", - "id": "pyup.io-52522", - "more_info_path": "/vulnerabilities/CVE-2022-24737/52522", - "specs": [ - "<0.5.2" - ], - "v": "<0.5.2" - }, { "advisory": "Encapsia-cli 0.5.2 updates its dependency 'certifi' to v2022.12.7 to include a security fix.", "cve": "CVE-2022-23491", @@ -43508,6 +43780,16 @@ "<0.5.2" ], "v": "<0.5.2" + }, + { + "advisory": "Encapsia-cli 0.5.2 updates its dependency 'httpie' requirement to ^3.1.0 to include security fixes.", + "cve": "CVE-2022-24737", + "id": "pyup.io-52522", + "more_info_path": "/vulnerabilities/CVE-2022-24737/52522", + "specs": [ + "<0.5.2" + ], + "v": "<0.5.2" } ], "encord-active": [ @@ -43928,20 +44210,20 @@ "v": "<2.22.1" }, { - "advisory": "Ethyca's Fides 2.22.1 patches a high-severity SSRF vulnerability (CVE-2023-46124) affecting versions before 2.22.1. This vulnerability allowed attackers with certain API access to make internal system requests that could lead to data breaches. The update is crucial for users with CONNECTOR_TEMPLATE_REGISTER scope and should be applied immediately to secure systems.\r\nhttps://github.com/ethyca/fides/security/advisories/GHSA-jq3w-9mgf-43m4", - "cve": "CVE-2023-46124", - "id": "pyup.io-63347", - "more_info_path": "/vulnerabilities/CVE-2023-46124/63347", + "advisory": "Ethyca-fides 2.22.1 addresses the moderate severity vulnerability CVE-2023-46126. This issue affected versions of Fides before 2.22.1, where the privacy policy URL field in consent and privacy notices was not properly validated. Malicious users with contributor access or higher could exploit this to execute arbitrary JavaScript on integrated websites. The patch now properly sanitizes input, preventing such attacks. https://github.com/ethyca/fides/security/advisories/GHSA-fgjj-5jmr-gh83", + "cve": "CVE-2023-46126", + "id": "pyup.io-63526", + "more_info_path": "/vulnerabilities/CVE-2023-46126/63526", "specs": [ "<2.22.1" ], "v": "<2.22.1" }, { - "advisory": "Ethyca-fides 2.22.1 addresses the moderate severity vulnerability CVE-2023-46126. This issue affected versions of Fides before 2.22.1, where the privacy policy URL field in consent and privacy notices was not properly validated. Malicious users with contributor access or higher could exploit this to execute arbitrary JavaScript on integrated websites. The patch now properly sanitizes input, preventing such attacks. https://github.com/ethyca/fides/security/advisories/GHSA-fgjj-5jmr-gh83", - "cve": "CVE-2023-46126", - "id": "pyup.io-63526", - "more_info_path": "/vulnerabilities/CVE-2023-46126/63526", + "advisory": "Ethyca's Fides 2.22.1 patches a high-severity SSRF vulnerability (CVE-2023-46124) affecting versions before 2.22.1. This vulnerability allowed attackers with certain API access to make internal system requests that could lead to data breaches. The update is crucial for users with CONNECTOR_TEMPLATE_REGISTER scope and should be applied immediately to secure systems.\r\nhttps://github.com/ethyca/fides/security/advisories/GHSA-jq3w-9mgf-43m4", + "cve": "CVE-2023-46124", + "id": "pyup.io-63347", + "more_info_path": "/vulnerabilities/CVE-2023-46124/63347", "specs": [ "<2.22.1" ], @@ -44326,10 +44608,10 @@ "v": "<0.8" }, { - "advisory": "Evennia 0.8 updates its dependency 'Twisted' minimum requirement to v18.0.0 to include a security fix.\r\nhttps://github.com/evennia/evennia/commit/6b7766d2956ae7be19f2cf7be0d43056c0accbb0", - "cve": "CVE-2016-1000111", - "id": "pyup.io-51937", - "more_info_path": "/vulnerabilities/CVE-2016-1000111/51937", + "advisory": "Evennia 0.8 updates its dependency 'pillow' to v5.2.0 to include security fixes.\r\nhttps://github.com/evennia/evennia/commit/6b7766d2956ae7be19f2cf7be0d43056c0accbb0", + "cve": "CVE-2016-4009", + "id": "pyup.io-52036", + "more_info_path": "/vulnerabilities/CVE-2016-4009/52036", "specs": [ "<0.8" ], @@ -44346,10 +44628,10 @@ "v": "<0.8" }, { - "advisory": "Evennia 0.8 updates its dependency 'pillow' to v5.2.0 to include security fixes.\r\nhttps://github.com/evennia/evennia/commit/6b7766d2956ae7be19f2cf7be0d43056c0accbb0", - "cve": "CVE-2016-4009", - "id": "pyup.io-52036", - "more_info_path": "/vulnerabilities/CVE-2016-4009/52036", + "advisory": "Evennia 0.8 updates its dependency 'Twisted' minimum requirement to v18.0.0 to include a security fix.\r\nhttps://github.com/evennia/evennia/commit/6b7766d2956ae7be19f2cf7be0d43056c0accbb0", + "cve": "CVE-2016-1000111", + "id": "pyup.io-51937", + "more_info_path": "/vulnerabilities/CVE-2016-1000111/51937", "specs": [ "<0.8" ], @@ -44357,9 +44639,9 @@ }, { "advisory": "Evennia 0.9.5 updates its dependency 'twisted' minimum requirement to \">=20.3.0\" to include security fixes.", - "cve": "CVE-2019-12855", - "id": "pyup.io-52046", - "more_info_path": "/vulnerabilities/CVE-2019-12855/52046", + "cve": "CVE-2020-10108", + "id": "pyup.io-51936", + "more_info_path": "/vulnerabilities/CVE-2020-10108/51936", "specs": [ "<0.9.5" ], @@ -44377,9 +44659,9 @@ }, { "advisory": "Evennia 0.9.5 updates its dependency 'twisted' minimum requirement to \">=20.3.0\" to include security fixes.", - "cve": "CVE-2020-10108", - "id": "pyup.io-51936", - "more_info_path": "/vulnerabilities/CVE-2020-10108/51936", + "cve": "CVE-2019-12855", + "id": "pyup.io-52046", + "more_info_path": "/vulnerabilities/CVE-2019-12855/52046", "specs": [ "<0.9.5" ], @@ -44548,20 +44830,20 @@ ], "exasol-bucketfs": [ { - "advisory": "Exasol-bucketfs 0.12.0 addresses CVE-2024-21503, a vulnerability in the black package that is included as a transitive dependency via exasol-toolbox.", - "cve": "CVE-2024-21503", - "id": "pyup.io-72123", - "more_info_path": "/vulnerabilities/CVE-2024-21503/72123", + "advisory": "Exasol-bucketfs 0.12.0 addresses CVE-2024-35195, a vulnerability in the requests package in versions below 2.32.0.", + "cve": "CVE-2024-35195", + "id": "pyup.io-72131", + "more_info_path": "/vulnerabilities/CVE-2024-35195/72131", "specs": [ "<0.12.0" ], "v": "<0.12.0" }, { - "advisory": "Exasol-bucketfs 0.12.0 addresses CVE-2024-35195, a vulnerability in the requests package in versions below 2.32.0.", - "cve": "CVE-2024-35195", - "id": "pyup.io-72131", - "more_info_path": "/vulnerabilities/CVE-2024-35195/72131", + "advisory": "Exasol-bucketfs 0.12.0 addresses CVE-2024-21503, a vulnerability in the black package that is included as a transitive dependency via exasol-toolbox.", + "cve": "CVE-2024-21503", + "id": "pyup.io-72123", + "more_info_path": "/vulnerabilities/CVE-2024-21503/72123", "specs": [ "<0.12.0" ], @@ -44602,20 +44884,20 @@ ], "exasol-python-test-framework": [ { - "advisory": "Exasol-python-test-framework updates its certifi dependency to address a security vulnerability identified as CVE-2022-23491.", - "cve": "CVE-2022-23491", - "id": "pyup.io-72063", - "more_info_path": "/vulnerabilities/CVE-2022-23491/72063", + "advisory": "Exasol-python-test-framework updates its GitPython dependency to address a security vulnerability identified as CVE-2022-24439.", + "cve": "CVE-2022-24439", + "id": "pyup.io-72071", + "more_info_path": "/vulnerabilities/CVE-2022-24439/72071", "specs": [ "<0.5.0" ], "v": "<0.5.0" }, { - "advisory": "Exasol-python-test-framework updates its GitPython dependency to address a security vulnerability identified as CVE-2022-24439.", - "cve": "CVE-2022-24439", - "id": "pyup.io-72071", - "more_info_path": "/vulnerabilities/CVE-2022-24439/72071", + "advisory": "Exasol-python-test-framework updates its certifi dependency to address a security vulnerability identified as CVE-2022-23491.", + "cve": "CVE-2022-23491", + "id": "pyup.io-72063", + "more_info_path": "/vulnerabilities/CVE-2022-23491/72063", "specs": [ "<0.5.0" ], @@ -44718,20 +45000,20 @@ "v": "<0.3a2" }, { - "advisory": "Exgrex-py 0.3a2 updates its dependency 'cryptography' to v3.3.2 to include a security fix.", - "cve": "CVE-2020-36242", - "id": "pyup.io-42750", - "more_info_path": "/vulnerabilities/CVE-2020-36242/42750", + "advisory": "Exgrex-py 0.3a2 updates its dependency 'pygments' to v2.7.4 to include security fixes.", + "cve": "CVE-2021-27291", + "id": "pyup.io-40142", + "more_info_path": "/vulnerabilities/CVE-2021-27291/40142", "specs": [ "<0.3a2" ], "v": "<0.3a2" }, { - "advisory": "Exgrex-py 0.3a2 updates its dependency 'pygments' to v2.7.4 to include security fixes.", - "cve": "CVE-2021-27291", - "id": "pyup.io-40142", - "more_info_path": "/vulnerabilities/CVE-2021-27291/40142", + "advisory": "Exgrex-py 0.3a2 updates its dependency 'cryptography' to v3.3.2 to include a security fix.", + "cve": "CVE-2020-36242", + "id": "pyup.io-42750", + "more_info_path": "/vulnerabilities/CVE-2020-36242/42750", "specs": [ "<0.3a2" ], @@ -44750,10 +45032,10 @@ ], "exgrex-pytest": [ { - "advisory": "Exgrex-pytest 0.1a2 updates underlying dependencies for security reasons: bleach -> 3.3.0.", - "cve": "CVE-2021-23980", - "id": "pyup.io-43013", - "more_info_path": "/vulnerabilities/CVE-2021-23980/43013", + "advisory": "Exgrex-pytest 0.1a2 updates its dependency 'urllib3' to v1.26.4 to include a security fix.", + "cve": "CVE-2021-28363", + "id": "pyup.io-40148", + "more_info_path": "/vulnerabilities/CVE-2021-28363/40148", "specs": [ "<0.1a2" ], @@ -44770,10 +45052,10 @@ "v": "<0.1a2" }, { - "advisory": "Exgrex-pytest 0.1a2 updates its dependency 'urllib3' to v1.26.4 to include a security fix.", - "cve": "CVE-2021-28363", - "id": "pyup.io-40148", - "more_info_path": "/vulnerabilities/CVE-2021-28363/40148", + "advisory": "Exgrex-pytest 0.1a2 updates underlying dependencies for security reasons: bleach -> 3.3.0.", + "cve": "CVE-2021-23980", + "id": "pyup.io-43013", + "more_info_path": "/vulnerabilities/CVE-2021-23980/43013", "specs": [ "<0.1a2" ], @@ -45078,6 +45360,19 @@ "v": ">=0.9.0,<1.13.2" } ], + "fabrice": [ + { + "advisory": "Fabrice is a typo squatting of the popular fabric SSH automation library.", + "cve": "PVE-2024-74102", + "id": "pyup.io-74102", + "more_info_path": "/vulnerabilities/PVE-2024-74102/74102", + "specs": [ + ">=0", + "<=0" + ], + "v": ">=0,<=0" + } + ], "facebook-online-friend-tracker": [ { "advisory": "Facebook-online-friend-tracker 2.0.0 makes users enter their credentials securely instead of entering them in raw text.\r\nhttps://github.com/bhamodi/facebook-online-friend-tracker/commit/979f27f8b78aa44f242ff099cbfad71bb938e4d7", @@ -45137,9 +45432,9 @@ }, { "advisory": "Fafi 0.2.1 updates its dependency 'pillow' to version '8.1.1' to include a security fix.\r\nhttps://github.com/svandragt/fafi/pull/42", - "cve": "CVE-2021-25291", - "id": "pyup.io-59156", - "more_info_path": "/vulnerabilities/CVE-2021-25291/59156", + "cve": "CVE-2021-25290", + "id": "pyup.io-59157", + "more_info_path": "/vulnerabilities/CVE-2021-25290/59157", "specs": [ "<0.2.1" ], @@ -45147,9 +45442,9 @@ }, { "advisory": "Fafi 0.2.1 updates its dependency 'pillow' to version '8.1.1' to include a security fix.\r\nhttps://github.com/svandragt/fafi/pull/42", - "cve": "CVE-2021-25921", - "id": "pyup.io-59153", - "more_info_path": "/vulnerabilities/CVE-2021-25921/59153", + "cve": "CVE-2021-25289", + "id": "pyup.io-59158", + "more_info_path": "/vulnerabilities/CVE-2021-25289/59158", "specs": [ "<0.2.1" ], @@ -45157,9 +45452,9 @@ }, { "advisory": "Fafi 0.2.1 updates its dependency 'pillow' to version '8.1.1' to include a security fix.\r\nhttps://github.com/svandragt/fafi/pull/42", - "cve": "CVE-2021-25922", - "id": "pyup.io-59099", - "more_info_path": "/vulnerabilities/CVE-2021-25922/59099", + "cve": "CVE-2021-25921", + "id": "pyup.io-59153", + "more_info_path": "/vulnerabilities/CVE-2021-25921/59153", "specs": [ "<0.2.1" ], @@ -45167,19 +45462,19 @@ }, { "advisory": "Fafi 0.2.1 updates its dependency 'pillow' to version '8.1.1' to include a security fix.\r\nhttps://github.com/svandragt/fafi/pull/42", - "cve": "CVE-2021-25290", - "id": "pyup.io-59157", - "more_info_path": "/vulnerabilities/CVE-2021-25290/59157", + "cve": "CVE-2021-25922", + "id": "pyup.io-59099", + "more_info_path": "/vulnerabilities/CVE-2021-25922/59099", "specs": [ "<0.2.1" ], "v": "<0.2.1" }, { - "advisory": "Fafi 0.2.1 updates its dependency 'pillow' to version '8.1.1' to include a security fix.\r\nhttps://github.com/svandragt/fafi/pull/42", - "cve": "CVE-2021-25293", - "id": "pyup.io-59154", - "more_info_path": "/vulnerabilities/CVE-2021-25293/59154", + "advisory": "Fafi 0.2.1 updates its dependency 'lxml' to version '4.6.3' to include a security fix.\r\nhttps://github.com/svandragt/fafi/pull/36", + "cve": "CVE-2021-28957", + "id": "pyup.io-59098", + "more_info_path": "/vulnerabilities/CVE-2021-28957/59098", "specs": [ "<0.2.1" ], @@ -45187,29 +45482,29 @@ }, { "advisory": "Fafi 0.2.1 updates its dependency 'pillow' to version '8.1.1' to include a security fix.\r\nhttps://github.com/svandragt/fafi/pull/42", - "cve": "CVE-2021-25289", - "id": "pyup.io-59158", - "more_info_path": "/vulnerabilities/CVE-2021-25289/59158", + "cve": "CVE-2021-25291", + "id": "pyup.io-59156", + "more_info_path": "/vulnerabilities/CVE-2021-25291/59156", "specs": [ "<0.2.1" ], "v": "<0.2.1" }, { - "advisory": "Fafi 0.2.1 updates its dependency 'lxml' to version '4.6.3' to include a security fix.\r\nhttps://github.com/svandragt/fafi/pull/36", - "cve": "CVE-2021-28957", - "id": "pyup.io-59098", - "more_info_path": "/vulnerabilities/CVE-2021-28957/59098", + "advisory": "Fafi 0.2.1 updates its dependency 'urllib3' to version '1.26.4' to include a security fix.\r\nhttps://github.com/svandragt/fafi/pull/39/files", + "cve": "CVE-2021-28363", + "id": "pyup.io-59097", + "more_info_path": "/vulnerabilities/CVE-2021-28363/59097", "specs": [ "<0.2.1" ], "v": "<0.2.1" }, { - "advisory": "Fafi 0.2.1 updates its dependency 'urllib3' to version '1.26.4' to include a security fix.\r\nhttps://github.com/svandragt/fafi/pull/39/files", - "cve": "CVE-2021-28363", - "id": "pyup.io-59097", - "more_info_path": "/vulnerabilities/CVE-2021-28363/59097", + "advisory": "Fafi 0.2.1 updates its dependency 'pillow' to version '8.1.1' to include a security fix.\r\nhttps://github.com/svandragt/fafi/pull/42", + "cve": "CVE-2021-25293", + "id": "pyup.io-59154", + "more_info_path": "/vulnerabilities/CVE-2021-25293/59154", "specs": [ "<0.2.1" ], @@ -45276,20 +45571,20 @@ "v": "<4.3.1" }, { - "advisory": "Falocalrepo 4.3.4 updates its dependency 'faapi' to v3.7.4 to include a security fix.", + "advisory": "Falocalrepo 4.3.4 updates its dependency 'falocalrepo-server' to v3.2.7 to include a security fix.", "cve": "CVE-2022-2309", - "id": "pyup.io-50141", - "more_info_path": "/vulnerabilities/CVE-2022-2309/50141", + "id": "pyup.io-50174", + "more_info_path": "/vulnerabilities/CVE-2022-2309/50174", "specs": [ "<4.3.4" ], "v": "<4.3.4" }, { - "advisory": "Falocalrepo 4.3.4 updates its dependency 'falocalrepo-server' to v3.2.7 to include a security fix.", + "advisory": "Falocalrepo 4.3.4 updates its dependency 'faapi' to v3.7.4 to include a security fix.", "cve": "CVE-2022-2309", - "id": "pyup.io-50174", - "more_info_path": "/vulnerabilities/CVE-2022-2309/50174", + "id": "pyup.io-50141", + "more_info_path": "/vulnerabilities/CVE-2022-2309/50141", "specs": [ "<4.3.4" ], @@ -45347,16 +45642,6 @@ ], "v": "<3.2.7" }, - { - "advisory": "Falocalrepo-server 3.3.4 updates its dependency 'pillow' to v10.0.1 to include a security fix.", - "cve": "CVE-2023-4863", - "id": "pyup.io-61801", - "more_info_path": "/vulnerabilities/CVE-2023-4863/61801", - "specs": [ - "<3.3.4" - ], - "v": "<3.3.4" - }, { "advisory": "Falocalrepo-server 3.3.4 updates its dependency 'fastapi' to v0.103.2 to include a security fix.", "cve": "CVE-2023-30798", @@ -45376,6 +45661,16 @@ "<3.3.4" ], "v": "<3.3.4" + }, + { + "advisory": "Falocalrepo-server 3.3.4 updates its dependency 'pillow' to v10.0.1 to include a security fix.", + "cve": "CVE-2023-4863", + "id": "pyup.io-61801", + "more_info_path": "/vulnerabilities/CVE-2023-4863/61801", + "specs": [ + "<3.3.4" + ], + "v": "<3.3.4" } ], "faq": [ @@ -45560,7 +45855,7 @@ "v": "<0.95.2" }, { - "advisory": "FastAPI is a web framework for building APIs with Python 3.8+ based on standard Python type hints. When using form data, `python-multipart` uses a Regular Expression to parse the HTTP `Content-Type` header, including options. An attacker could send a custom-made `Content-Type` option that is very difficult for the RegEx to process, consuming CPU resources and stalling indefinitely (minutes or more) while holding the main event loop. This means that the process can't handle any more requests. It's a ReDoS(Regular expression Denial of Service), it only applies to those reading form data, using `python-multipart`. This vulnerability has been patched in version 0.109.1. See CVE-2024-24762.", + "advisory": "Fastapi 0.109.1 updates its minimum version of 'python-multipart' to >=0.0.7 to include a security fix.", "cve": "CVE-2024-24762", "id": "pyup.io-65293", "more_info_path": "/vulnerabilities/CVE-2024-24762/65293", @@ -45637,6 +45932,16 @@ ], "v": "<1.6.0" }, + { + "advisory": "Fastapi-login 1.6.1 updates its dependency 'fastapi' to v0.65.2 to include a security fix.", + "cve": "CVE-2021-32677", + "id": "pyup.io-45767", + "more_info_path": "/vulnerabilities/CVE-2021-32677/45767", + "specs": [ + "<1.6.1" + ], + "v": "<1.6.1" + }, { "advisory": "Fastapi-login 1.6.1 updates its dependency 'urllib3' to v1.26.5 to include a security fix.", "cve": "CVE-2021-33503", @@ -45657,16 +45962,6 @@ ], "v": "<1.6.1" }, - { - "advisory": "Fastapi-login 1.6.1 updates its dependency 'fastapi' to v0.65.2 to include a security fix.", - "cve": "CVE-2021-32677", - "id": "pyup.io-45767", - "more_info_path": "/vulnerabilities/CVE-2021-32677/45767", - "specs": [ - "<1.6.1" - ], - "v": "<1.6.1" - }, { "advisory": "Fastapi-login 1.8.0 updates its dependency 'mkdocs' to v1.2.3 to include a security fix.", "cve": "CVE-2021-40978", @@ -45699,9 +45994,9 @@ }, { "advisory": "Fastapi-login 1.9.0 updates its dependency 'cryptography' to v39.0.2 to include security fixes. Note that this is now an optional dependency.", - "cve": "CVE-2023-0215", - "id": "pyup.io-53894", - "more_info_path": "/vulnerabilities/CVE-2023-0215/53894", + "cve": "CVE-2023-0217", + "id": "pyup.io-53893", + "more_info_path": "/vulnerabilities/CVE-2023-0217/53893", "specs": [ "<1.9.0" ], @@ -45709,9 +46004,9 @@ }, { "advisory": "Fastapi-login 1.9.0 updates its dependency 'cryptography' to v39.0.2 to include security fixes. Note that this is now an optional dependency.", - "cve": "CVE-2023-0401", - "id": "pyup.io-53886", - "more_info_path": "/vulnerabilities/CVE-2023-0401/53886", + "cve": "CVE-2022-4304", + "id": "pyup.io-53895", + "more_info_path": "/vulnerabilities/CVE-2022-4304/53895", "specs": [ "<1.9.0" ], @@ -45719,9 +46014,9 @@ }, { "advisory": "Fastapi-login 1.9.0 updates its dependency 'cryptography' to v39.0.2 to include security fixes. Note that this is now an optional dependency.", - "cve": "CVE-2023-23931", - "id": "pyup.io-53896", - "more_info_path": "/vulnerabilities/CVE-2023-23931/53896", + "cve": "CVE-2023-0215", + "id": "pyup.io-53894", + "more_info_path": "/vulnerabilities/CVE-2023-0215/53894", "specs": [ "<1.9.0" ], @@ -45729,9 +46024,9 @@ }, { "advisory": "Fastapi-login 1.9.0 updates its dependency 'cryptography' to v39.0.2 to include security fixes. Note that this is now an optional dependency.", - "cve": "CVE-2023-0217", - "id": "pyup.io-53893", - "more_info_path": "/vulnerabilities/CVE-2023-0217/53893", + "cve": "CVE-2023-0401", + "id": "pyup.io-53886", + "more_info_path": "/vulnerabilities/CVE-2023-0401/53886", "specs": [ "<1.9.0" ], @@ -45739,9 +46034,9 @@ }, { "advisory": "Fastapi-login 1.9.0 updates its dependency 'cryptography' to v39.0.2 to include security fixes. Note that this is now an optional dependency.", - "cve": "CVE-2022-4304", - "id": "pyup.io-53895", - "more_info_path": "/vulnerabilities/CVE-2022-4304/53895", + "cve": "CVE-2023-23931", + "id": "pyup.io-53896", + "more_info_path": "/vulnerabilities/CVE-2023-23931/53896", "specs": [ "<1.9.0" ], @@ -45832,40 +46127,40 @@ "v": "<2.0.0" }, { - "advisory": "Fastapi-opa updates its dependency 'cryptography' to version 42.0.8 to include a security fix for CVE-2024-4603.", - "cve": "CVE-2024-4603", - "id": "pyup.io-72179", - "more_info_path": "/vulnerabilities/CVE-2024-4603/72179", + "advisory": "FastAPI OPA includes a security issue where HTTP `OPTIONS` requests are unconditionally allowed by `OpaMiddleware`, even when they lack authentication. These requests bypass policy evaluation and are forwarded directly to the application. This behavior can allow an unauthenticated attacker to determine the existence of entities within the application based on different responses to HTTP `OPTIONS` requests. For instance, responses might indicate whether an entity is writable at a system level. At present, there are no identified workarounds for this vulnerability.", + "cve": "CVE-2024-40627", + "id": "pyup.io-72251", + "more_info_path": "/vulnerabilities/CVE-2024-40627/72251", "specs": [ "<2.0.1" ], "v": "<2.0.1" }, { - "advisory": "Fastapi-opa has updated `idna` to versions 3.6 and 3.7 due to the CVE-2024-3651.", - "cve": "CVE-2024-3651", - "id": "pyup.io-72180", - "more_info_path": "/vulnerabilities/CVE-2024-3651/72180", + "advisory": "Fastapi-opa has updated `certifi` to versions 2024.2.2 and 2024.7.4 to address CVE-2024-39689.", + "cve": "CVE-2024-39689", + "id": "pyup.io-72173", + "more_info_path": "/vulnerabilities/CVE-2024-39689/72173", "specs": [ "<2.0.1" ], "v": "<2.0.1" }, { - "advisory": "Fastapi-opa has updated `certifi` to versions 2024.2.2 and 2024.7.4 to address CVE-2024-39689.", - "cve": "CVE-2024-39689", - "id": "pyup.io-72173", - "more_info_path": "/vulnerabilities/CVE-2024-39689/72173", + "advisory": "Fastapi-opa has updated `idna` to versions 3.6 and 3.7 due to the CVE-2024-3651.", + "cve": "CVE-2024-3651", + "id": "pyup.io-72180", + "more_info_path": "/vulnerabilities/CVE-2024-3651/72180", "specs": [ "<2.0.1" ], "v": "<2.0.1" }, { - "advisory": "FastAPI OPA includes a security issue where HTTP `OPTIONS` requests are unconditionally allowed by `OpaMiddleware`, even when they lack authentication. These requests bypass policy evaluation and are forwarded directly to the application. This behavior can allow an unauthenticated attacker to determine the existence of entities within the application based on different responses to HTTP `OPTIONS` requests. For instance, responses might indicate whether an entity is writable at a system level. At present, there are no identified workarounds for this vulnerability.", - "cve": "CVE-2024-40627", - "id": "pyup.io-72251", - "more_info_path": "/vulnerabilities/CVE-2024-40627/72251", + "advisory": "Fastapi-opa updates its dependency 'cryptography' to version 42.0.8 to include a security fix for CVE-2024-4603.", + "cve": "CVE-2024-4603", + "id": "pyup.io-72179", + "more_info_path": "/vulnerabilities/CVE-2024-4603/72179", "specs": [ "<2.0.1" ], @@ -45918,6 +46213,18 @@ "v": "<3.2.0" } ], + "fastapi-sso": [ + { + "advisory": "Affected versions of fastapi-sso are vulnerable to Race Condition [CWE-362]. When multiple concurrent login requests are processed simultaneously, the state shared between requests could allow one user to unintentionally obtain another user's access token and assume their identity. The vulnerability exists in the SSO login flow, where the provider instance state is not properly isolated between concurrent requests. This could be exploited in high concurrency scenarios by timing login requests precisely. The fix introduces async locking to ensure requests are processed sequentially and providers must be used within an async context manager.", + "cve": "PVE-2024-74027", + "id": "pyup.io-74027", + "more_info_path": "/vulnerabilities/PVE-2024-74027/74027", + "specs": [ + "<0.16.0" + ], + "v": "<0.16.0" + } + ], "fastapi-toolkit": [ { "advisory": "Fastapi-toolkit 0.0.27 has a backdoor that adds a FastAPI route allowing a remote attacker to execute arbitrary python code and SQL queries in the context of the web application.\r\nhttps://securitylabs.datadoghq.com/articles/malicious-pypi-package-fastapi-toolkit", @@ -46106,6 +46413,19 @@ "v": "<0.6.1" } ], + "fc-clip": [ + { + "advisory": "The OpenSSF Package Analysis project has identified the PyPI package 'fc-clip' version 99.6 as malicious because it communicates with a domain associated with malicious activity, indicating potential security risks.", + "cve": "PVE-2024-74231", + "id": "pyup.io-74231", + "more_info_path": "/vulnerabilities/PVE-2024-74231/74231", + "specs": [ + ">=0", + "<=0" + ], + "v": ">=0,<=0" + } + ], "feast": [ { "advisory": "Feast 0.1.2 (Python SDK) includes Feast Core and UI, which update dependencies to include security fixes.\r\nhttps://github.com/feast-dev/feast/commit/93e08927baf58e068efba186d91e8b1951ce88b0", @@ -46128,17 +46448,20 @@ "v": ">0" } ], - "featurebyte": [ + "feature-preserve-portrait-editing": [ { - "advisory": "Featurebyte 0.3.0 updates its dependency 'starlette' to v0.27.0 to include a security fix.", - "cve": "PVE-2023-58713", - "id": "pyup.io-58915", - "more_info_path": "/vulnerabilities/PVE-2023-58713/58915", + "advisory": "The OpenSSF Package Analysis project has identified the PyPI package 'feature-preserve-portrait-editing' version 99.6 as malicious because it communicates with a domain associated with malicious activity, indicating potential security risks.", + "cve": "PVE-2024-74247", + "id": "pyup.io-74247", + "more_info_path": "/vulnerabilities/PVE-2024-74247/74247", "specs": [ - "<0.3.0" + ">=0", + "<=0" ], - "v": "<0.3.0" - }, + "v": ">=0,<=0" + } + ], + "featurebyte": [ { "advisory": "Featurebyte 0.3.0 updates its dependency 'pymdown-extensions' to include a security fix.", "cve": "CVE-2023-32309", @@ -46150,14 +46473,14 @@ "v": "<0.3.0" }, { - "advisory": "Featurebyte version 1.0.3 updates its 'black' dependency from '^23.3.0' to '^24.3.0' to address the security vulnerability identified in CVE-2024-21503.", - "cve": "CVE-2024-21503", - "id": "pyup.io-71107", - "more_info_path": "/vulnerabilities/CVE-2024-21503/71107", + "advisory": "Featurebyte 0.3.0 updates its dependency 'starlette' to v0.27.0 to include a security fix.", + "cve": "PVE-2023-58713", + "id": "pyup.io-58915", + "more_info_path": "/vulnerabilities/PVE-2023-58713/58915", "specs": [ - "<1.0.3" + "<0.3.0" ], - "v": "<1.0.3" + "v": "<0.3.0" }, { "advisory": "Featurebyte version 1.0.3 updates its `cryptography` dependency from `^41.0.3` to `^42.0.4` to address the security vulnerability identified as CVE-2024-26130. This update ensures that users are protected from issues present in the older version of the `cryptography` library.", @@ -46179,6 +46502,16 @@ ], "v": "<1.0.3" }, + { + "advisory": "Featurebyte version 1.0.3 updates its 'black' dependency from '^23.3.0' to '^24.3.0' to address the security vulnerability identified in CVE-2024-21503.", + "cve": "CVE-2024-21503", + "id": "pyup.io-71107", + "more_info_path": "/vulnerabilities/CVE-2024-21503/71107", + "specs": [ + "<1.0.3" + ], + "v": "<1.0.3" + }, { "advisory": "Featurebyte has updated aiohttp to version 3.10.2 or higher to address CVE-2024-27306 and other potential security vulnerabilities.", "cve": "CVE-2024-27306", @@ -46419,9 +46752,9 @@ }, { "advisory": "Fhir-pyrate 0.2.0b1 updates its dependency \"numpy\" to v1.23.1 to include security fixes.", - "cve": "CVE-2021-41495", - "id": "pyup.io-50793", - "more_info_path": "/vulnerabilities/CVE-2021-41495/50793", + "cve": "CVE-2021-41496", + "id": "pyup.io-50794", + "more_info_path": "/vulnerabilities/CVE-2021-41496/50794", "specs": [ "<0.2.0b1" ], @@ -46429,9 +46762,9 @@ }, { "advisory": "Fhir-pyrate 0.2.0b1 updates its dependency \"numpy\" to v1.23.1 to include security fixes.", - "cve": "CVE-2021-41496", - "id": "pyup.io-50794", - "more_info_path": "/vulnerabilities/CVE-2021-41496/50794", + "cve": "CVE-2021-41495", + "id": "pyup.io-50793", + "more_info_path": "/vulnerabilities/CVE-2021-41495/50793", "specs": [ "<0.2.0b1" ], @@ -46485,19 +46818,9 @@ "fiduswriter": [ { "advisory": "Fiduswriter 3.9.24 updates its dependency 'pillow' to v8.2.0 to include security fixes.", - "cve": "CVE-2021-25288", - "id": "pyup.io-43508", - "more_info_path": "/vulnerabilities/CVE-2021-25288/43508", - "specs": [ - "<3.9.24" - ], - "v": "<3.9.24" - }, - { - "advisory": "Fiduswriter 3.9.24 updates its dependency 'Django' to v3.1.12 to include security fixes.", - "cve": "CVE-2021-31542", - "id": "pyup.io-43512", - "more_info_path": "/vulnerabilities/CVE-2021-31542/43512", + "cve": "CVE-2021-28677", + "id": "pyup.io-43510", + "more_info_path": "/vulnerabilities/CVE-2021-28677/43510", "specs": [ "<3.9.24" ], @@ -46523,11 +46846,21 @@ ], "v": "<3.9.24" }, + { + "advisory": "Fiduswriter 3.9.24 updates its dependency 'Django' to v3.1.12 to include security fixes.", + "cve": "CVE-2021-31542", + "id": "pyup.io-43512", + "more_info_path": "/vulnerabilities/CVE-2021-31542/43512", + "specs": [ + "<3.9.24" + ], + "v": "<3.9.24" + }, { "advisory": "Fiduswriter 3.9.24 updates its dependency 'pillow' to v8.2.0 to include security fixes.", - "cve": "CVE-2021-28677", - "id": "pyup.io-43510", - "more_info_path": "/vulnerabilities/CVE-2021-28677/43510", + "cve": "CVE-2021-25288", + "id": "pyup.io-43508", + "more_info_path": "/vulnerabilities/CVE-2021-25288/43508", "specs": [ "<3.9.24" ], @@ -46565,9 +46898,9 @@ }, { "advisory": "Fiduswriter 3.9.24 updates its dependency 'pillow' to v8.2.0 to include security fixes.", - "cve": "CVE-2021-28678", - "id": "pyup.io-43511", - "more_info_path": "/vulnerabilities/CVE-2021-28678/43511", + "cve": "CVE-2021-27922", + "id": "pyup.io-43506", + "more_info_path": "/vulnerabilities/CVE-2021-27922/43506", "specs": [ "<3.9.24" ], @@ -46575,9 +46908,9 @@ }, { "advisory": "Fiduswriter 3.9.24 updates its dependency 'pillow' to v8.2.0 to include security fixes.", - "cve": "CVE-2021-27922", - "id": "pyup.io-43506", - "more_info_path": "/vulnerabilities/CVE-2021-27922/43506", + "cve": "CVE-2021-28678", + "id": "pyup.io-43511", + "more_info_path": "/vulnerabilities/CVE-2021-28678/43511", "specs": [ "<3.9.24" ], @@ -46850,10 +47183,10 @@ ], "flafl": [ { - "advisory": "Flafl 0.0.5 updates its dependency 'jinja2' to v2.11.3 to include a security fix.", - "cve": "CVE-2020-28493", - "id": "pyup.io-58681", - "more_info_path": "/vulnerabilities/CVE-2020-28493/58681", + "advisory": "Flafl 0.0.5 updates its dependency 'werkzeug' to v2.2.3 to include security fixes.", + "cve": "CVE-2023-25577", + "id": "pyup.io-58685", + "more_info_path": "/vulnerabilities/CVE-2023-25577/58685", "specs": [ "<0.0.5" ], @@ -46889,16 +47222,6 @@ ], "v": "<0.0.5" }, - { - "advisory": "Flafl 0.0.5 updates its dependency 'werkzeug' to v2.2.3 to include security fixes.", - "cve": "CVE-2023-25577", - "id": "pyup.io-58685", - "more_info_path": "/vulnerabilities/CVE-2023-25577/58685", - "specs": [ - "<0.0.5" - ], - "v": "<0.0.5" - }, { "advisory": "Flafl 0.0.5 updates its dependency 'werkzeug' to v2.2.3 to include security fixes.", "cve": "CVE-2019-14322", @@ -46918,6 +47241,16 @@ "<0.0.5" ], "v": "<0.0.5" + }, + { + "advisory": "Flafl 0.0.5 updates its dependency 'jinja2' to v2.11.3 to include a security fix.", + "cve": "CVE-2020-28493", + "id": "pyup.io-58681", + "more_info_path": "/vulnerabilities/CVE-2020-28493/58681", + "specs": [ + "<0.0.5" + ], + "v": "<0.0.5" } ], "flag-leak-r": [ @@ -47372,20 +47705,20 @@ ], "flask-flatpages": [ { - "advisory": "Flask-flatpages 0.7.1 updates its dependency 'jinja2' to v2.10.1 to include a security fix.", - "cve": "CVE-2019-10906", - "id": "pyup.io-37077", - "more_info_path": "/vulnerabilities/CVE-2019-10906/37077", + "advisory": "Flask-flatpages 0.7.1 updates its dependency 'urllib3' to v1.24.2 to include a security fix.", + "cve": "CVE-2019-11324", + "id": "pyup.io-44978", + "more_info_path": "/vulnerabilities/CVE-2019-11324/44978", "specs": [ "<0.7.1" ], "v": "<0.7.1" }, { - "advisory": "Flask-flatpages 0.7.1 updates its dependency 'urllib3' to v1.24.2 to include a security fix.", - "cve": "CVE-2019-11324", - "id": "pyup.io-44978", - "more_info_path": "/vulnerabilities/CVE-2019-11324/44978", + "advisory": "Flask-flatpages 0.7.1 updates its dependency 'jinja2' to v2.10.1 to include a security fix.", + "cve": "CVE-2019-10906", + "id": "pyup.io-37077", + "more_info_path": "/vulnerabilities/CVE-2019-10906/37077", "specs": [ "<0.7.1" ], @@ -47884,20 +48217,20 @@ ], "flaskcode": [ { - "advisory": "Flaskcode is affected by a path traversal vulnerability. An unauthenticated directory traversal, exploitable with a POST request to a /update-resource-data/ URI (from views.py), allows attackers to write to arbitrary files.", - "cve": "CVE-2023-52289", - "id": "pyup.io-64224", - "more_info_path": "/vulnerabilities/CVE-2023-52289/64224", + "advisory": "Flaskcode is affected by a path traversal vulnerability. An unauthenticated directory traversal, exploitable with a GET request to a /resource-data/.txt URI (from views.py), allows attackers to read arbitrary files.", + "cve": "CVE-2023-52288", + "id": "pyup.io-64223", + "more_info_path": "/vulnerabilities/CVE-2023-52288/64223", "specs": [ "<=0.0.8" ], "v": "<=0.0.8" }, { - "advisory": "Flaskcode is affected by a path traversal vulnerability. An unauthenticated directory traversal, exploitable with a GET request to a /resource-data/.txt URI (from views.py), allows attackers to read arbitrary files.", - "cve": "CVE-2023-52288", - "id": "pyup.io-64223", - "more_info_path": "/vulnerabilities/CVE-2023-52288/64223", + "advisory": "Flaskcode is affected by a path traversal vulnerability. An unauthenticated directory traversal, exploitable with a POST request to a /update-resource-data/ URI (from views.py), allows attackers to write to arbitrary files.", + "cve": "CVE-2023-52289", + "id": "pyup.io-64224", + "more_info_path": "/vulnerabilities/CVE-2023-52289/64224", "specs": [ "<=0.0.8" ], @@ -47983,6 +48316,16 @@ ], "v": "<1.0.0a1" }, + { + "advisory": "Flowchem 1.0.0a1 updates its dependency 'protobuf' to v3.19.5 to include a security fix.", + "cve": "CVE-2022-1941", + "id": "pyup.io-52050", + "more_info_path": "/vulnerabilities/CVE-2022-1941/52050", + "specs": [ + "<1.0.0a1" + ], + "v": "<1.0.0a1" + }, { "advisory": "Flowchem 1.0.0a1 updates its dependency 'jupyter-core' to v4.11.2 to include a security fix.", "cve": "CVE-2022-39286", @@ -48002,16 +48345,6 @@ "<1.0.0a1" ], "v": "<1.0.0a1" - }, - { - "advisory": "Flowchem 1.0.0a1 updates its dependency 'protobuf' to v3.19.5 to include a security fix.", - "cve": "CVE-2022-1941", - "id": "pyup.io-52050", - "more_info_path": "/vulnerabilities/CVE-2022-1941/52050", - "specs": [ - "<1.0.0a1" - ], - "v": "<1.0.0a1" } ], "flower": [ @@ -48088,50 +48421,50 @@ "v": "<1.1.0" }, { - "advisory": "Flytekit 1.2.0 updates its dependency 'notebook' to v6.4.12 to include a security fix.", - "cve": "CVE-2022-29238", - "id": "pyup.io-51330", - "more_info_path": "/vulnerabilities/CVE-2022-29238/51330", + "advisory": "Flytekit 1.2.0 updates its dependency 'oauthlib' to v3.2.1 to include a security fix.", + "cve": "CVE-2022-36087", + "id": "pyup.io-51333", + "more_info_path": "/vulnerabilities/CVE-2022-36087/51333", "specs": [ "<1.2.0" ], "v": "<1.2.0" }, { - "advisory": "Flytekit 1.2.0 updates its dependency 'cookiecutter' to v2.1.1 to include a security fix.", - "cve": "CVE-2022-24065", - "id": "pyup.io-51331", - "more_info_path": "/vulnerabilities/CVE-2022-24065/51331", + "advisory": "Flytekit 1.2.0 updates its dependency 'pyspark' to v3.3.0 to include a security fix.", + "cve": "CVE-2022-33891", + "id": "pyup.io-51332", + "more_info_path": "/vulnerabilities/CVE-2022-33891/51332", "specs": [ "<1.2.0" ], "v": "<1.2.0" }, { - "advisory": "Flytekit 1.2.0 updates its dependency 'lxml' to v4.9.1 to include a security fix.", - "cve": "CVE-2022-2309", - "id": "pyup.io-51327", - "more_info_path": "/vulnerabilities/CVE-2022-2309/51327", + "advisory": "Flytekit 1.2.0 updates its dependency 'notebook' to v6.4.12 to include a security fix.", + "cve": "CVE-2022-29238", + "id": "pyup.io-51330", + "more_info_path": "/vulnerabilities/CVE-2022-29238/51330", "specs": [ "<1.2.0" ], "v": "<1.2.0" }, { - "advisory": "Flytekit 1.2.0 updates its dependency 'oauthlib' to v3.2.1 to include a security fix.", - "cve": "CVE-2022-36087", - "id": "pyup.io-51333", - "more_info_path": "/vulnerabilities/CVE-2022-36087/51333", + "advisory": "Flytekit 1.2.0 updates its dependency 'cookiecutter' to v2.1.1 to include a security fix.", + "cve": "CVE-2022-24065", + "id": "pyup.io-51331", + "more_info_path": "/vulnerabilities/CVE-2022-24065/51331", "specs": [ "<1.2.0" ], "v": "<1.2.0" }, { - "advisory": "Flytekit 1.2.0 updates its dependency 'pyspark' to v3.3.0 to include a security fix.", - "cve": "CVE-2022-33891", - "id": "pyup.io-51332", - "more_info_path": "/vulnerabilities/CVE-2022-33891/51332", + "advisory": "Flytekit 1.2.0 updates its dependency 'lxml' to v4.9.1 to include a security fix.", + "cve": "CVE-2022-2309", + "id": "pyup.io-51327", + "more_info_path": "/vulnerabilities/CVE-2022-2309/51327", "specs": [ "<1.2.0" ], @@ -48410,40 +48743,40 @@ ], "fractal-server": [ { - "advisory": "Fractal-server 1.3.0a3 updates its dependency 'pymdown-extensions' to version '10.0.1' to include a security fix.\r\nhttps://github.com/fractal-analytics-platform/fractal-server/pull/723", - "cve": "CVE-2023-32309", - "id": "pyup.io-58995", - "more_info_path": "/vulnerabilities/CVE-2023-32309/58995", + "advisory": "Fractal-server 1.3.0a3 updates its dependency 'requests' to version '2.31.0' to include a security fix.\r\nhttps://github.com/fractal-analytics-platform/fractal-server/pull/723", + "cve": "CVE-2023-32681", + "id": "pyup.io-59000", + "more_info_path": "/vulnerabilities/CVE-2023-32681/59000", "specs": [ "<1.3.0a3" ], "v": "<1.3.0a3" }, { - "advisory": "Fractal-server 1.3.0a3 updates its dependency 'starlette' to version '0.27.0' to include a security fix.\r\nhttps://github.com/fractal-analytics-platform/fractal-server/pull/723", - "cve": "CVE-2023-29159", - "id": "pyup.io-59001", - "more_info_path": "/vulnerabilities/CVE-2023-29159/59001", + "advisory": "Fractal-server 1.3.0a3 updates its dependency 'pymdown-extensions' to version '10.0.1' to include a security fix.\r\nhttps://github.com/fractal-analytics-platform/fractal-server/pull/723", + "cve": "CVE-2023-32309", + "id": "pyup.io-58995", + "more_info_path": "/vulnerabilities/CVE-2023-32309/58995", "specs": [ "<1.3.0a3" ], "v": "<1.3.0a3" }, { - "advisory": "Fractal-server 1.3.0a3 updates its dependency 'requests' to version '2.31.0' to include a security fix.\r\nhttps://github.com/fractal-analytics-platform/fractal-server/pull/723", - "cve": "CVE-2023-32681", - "id": "pyup.io-59000", - "more_info_path": "/vulnerabilities/CVE-2023-32681/59000", + "advisory": "Fractal-server 1.3.0a3 updates its dependency 'cryptography' to version '41.0.1' to include a security fix.\r\nhttps://github.com/fractal-analytics-platform/fractal-server/pull/739/commits/ec5bbd57acabf5a1fc357cfb96c21e059c619475", + "cve": "CVE-2023-2650", + "id": "pyup.io-59002", + "more_info_path": "/vulnerabilities/CVE-2023-2650/59002", "specs": [ "<1.3.0a3" ], "v": "<1.3.0a3" }, { - "advisory": "Fractal-server 1.3.0a3 updates its dependency 'cryptography' to version '41.0.1' to include a security fix.\r\nhttps://github.com/fractal-analytics-platform/fractal-server/pull/739/commits/ec5bbd57acabf5a1fc357cfb96c21e059c619475", - "cve": "CVE-2023-2650", - "id": "pyup.io-59002", - "more_info_path": "/vulnerabilities/CVE-2023-2650/59002", + "advisory": "Fractal-server 1.3.0a3 updates its dependency 'starlette' to version '0.27.0' to include a security fix.\r\nhttps://github.com/fractal-analytics-platform/fractal-server/pull/723", + "cve": "CVE-2023-29159", + "id": "pyup.io-59001", + "more_info_path": "/vulnerabilities/CVE-2023-29159/59001", "specs": [ "<1.3.0a3" ], @@ -48839,10 +49172,10 @@ "v": ">=0,<=1.9.8" }, { - "advisory": "An issue in the ?filename= argument of the route /DataPackageTable in FreeTAKServer-UI v1.9.8 allows attackers to place arbitrary files anywhere on the system.", - "cve": "CVE-2022-25511", - "id": "pyup.io-54400", - "more_info_path": "/vulnerabilities/CVE-2022-25511/54400", + "advisory": "FreeTAKServer-UI v1.9.8 was discovered to contain a stored cross-site scripting (XSS) vulnerability via the Callsign parameter.", + "cve": "CVE-2022-25507", + "id": "pyup.io-54398", + "more_info_path": "/vulnerabilities/CVE-2022-25507/54398", "specs": [ ">=0,<=1.9.8" ], @@ -48859,10 +49192,10 @@ "v": ">=0,<=1.9.8" }, { - "advisory": "FreeTAKServer-UI v1.9.8 was discovered to contain a stored cross-site scripting (XSS) vulnerability via the Callsign parameter.", - "cve": "CVE-2022-25507", - "id": "pyup.io-54398", - "more_info_path": "/vulnerabilities/CVE-2022-25507/54398", + "advisory": "An issue in the ?filename= argument of the route /DataPackageTable in FreeTAKServer-UI v1.9.8 allows attackers to place arbitrary files anywhere on the system.", + "cve": "CVE-2022-25511", + "id": "pyup.io-54400", + "more_info_path": "/vulnerabilities/CVE-2022-25511/54400", "specs": [ ">=0,<=1.9.8" ], @@ -49077,20 +49410,20 @@ ], "fundaml": [ { - "advisory": "Fundaml 0.1.32 updates its dependency 'ipython' to version '8.10.0' to include a fix for a Remote Code Execution vulnerability.\r\nhttps://github.com/tzoght/fundaml/commit/02e60c4d8474aa673f02a65556fef2382fe4cf16", - "cve": "CVE-2023-24816", - "id": "pyup.io-59401", - "more_info_path": "/vulnerabilities/CVE-2023-24816/59401", + "advisory": "Fundaml 0.1.32 updates its dependency 'tornado' to version '6.3.2' to include a fix for an Open Redirect vulnerability.\r\nhttps://github.com/tzoght/fundaml/commit/b892b169d7156c2470d266e874e877ba41e40d5e", + "cve": "CVE-2023-28370", + "id": "pyup.io-59402", + "more_info_path": "/vulnerabilities/CVE-2023-28370/59402", "specs": [ "<0.1.32" ], "v": "<0.1.32" }, { - "advisory": "Fundaml 0.1.32 updates its dependency 'tornado' to version '6.3.2' to include a fix for an Open Redirect vulnerability.\r\nhttps://github.com/tzoght/fundaml/commit/b892b169d7156c2470d266e874e877ba41e40d5e", - "cve": "CVE-2023-28370", - "id": "pyup.io-59402", - "more_info_path": "/vulnerabilities/CVE-2023-28370/59402", + "advisory": "Fundaml 0.1.32 updates its dependency 'ipython' to version '8.10.0' to include a fix for a Remote Code Execution vulnerability.\r\nhttps://github.com/tzoght/fundaml/commit/02e60c4d8474aa673f02a65556fef2382fe4cf16", + "cve": "CVE-2023-24816", + "id": "pyup.io-59401", + "more_info_path": "/vulnerabilities/CVE-2023-24816/59401", "specs": [ "<0.1.32" ], @@ -49468,6 +49801,16 @@ ], "v": "<3.8.0" }, + { + "advisory": "Affected versions of GDAL's GMLAS driver are vulnerable to XML Entity Expansion attacks (Billion Laughs attack). This vulnerability can lead to a Denial of Service (DoS) by causing excessive resource consumption when parsing specially crafted XML files with recursive entity definitions. The attack vector involves feeding malicious XML content to the GMLAS driver, exploiting the unlimited entity expansion during parsing. The vulnerability exists in the GMLASReader class's XML parsing functions that lack restrictions on entity expansion. An attacker can exploit this by providing a crafted XML input to any application using the vulnerable GMLAS driver, potentially rendering the application unresponsive. The issue is mitigated by introducing a limit on entity expansions and aborting parsing when the limit is exceeded.", + "cve": "PVE-2024-74054", + "id": "pyup.io-74054", + "more_info_path": "/vulnerabilities/PVE-2024-74054/74054", + "specs": [ + "<3.9.3" + ], + "v": "<3.9.3" + }, { "advisory": "GDAL before is vulnerable to a Denial of Service (DoS) attack via the OverviewScan function. DoS attacks aim to make systems inaccessible to their legitimate users by overwhelming them with a flood of requests, causing downtime. This vulnerability in GDAL can be exploited to crash or severely impair the service without compromising security integrity.\r\nhttps://github.com/OSGeo/gdal/pull/2461", "cve": "PVE-2024-99784", @@ -49654,20 +49997,20 @@ ], "geonode": [ { - "advisory": "Geonode 2.10 updates 'twisted' to v19.2.1 to include security fixes.", - "cve": "PVE-2021-37040", - "id": "pyup.io-42971", - "more_info_path": "/vulnerabilities/PVE-2021-37040/42971", + "advisory": "Geonode 2.10 updates 'urllib3' to v1.24.2 to include security fixes.", + "cve": "CVE-2019-11324", + "id": "pyup.io-42968", + "more_info_path": "/vulnerabilities/CVE-2019-11324/42968", "specs": [ "<2.10" ], "v": "<2.10" }, { - "advisory": "Geonode 2.10 updates 'urllib3' to v1.24.2 to include security fixes.", - "cve": "CVE-2019-11324", - "id": "pyup.io-42968", - "more_info_path": "/vulnerabilities/CVE-2019-11324/42968", + "advisory": "Geonode 2.10 updates 'twisted' to v19.2.1 to include security fixes.", + "cve": "PVE-2021-37040", + "id": "pyup.io-42971", + "more_info_path": "/vulnerabilities/PVE-2021-37040/42971", "specs": [ "<2.10" ], @@ -49684,20 +50027,20 @@ "v": "<2.10" }, { - "advisory": "Geonode 2.10 updates 'twisted' to v19.2.1 to include security fixes.", - "cve": "CVE-2019-12387", - "id": "pyup.io-42970", - "more_info_path": "/vulnerabilities/CVE-2019-12387/42970", + "advisory": "Geonode 2.10 updates 'urllib3' to v1.24.2 to include security fixes.", + "cve": "CVE-2018-20060", + "id": "pyup.io-42969", + "more_info_path": "/vulnerabilities/CVE-2018-20060/42969", "specs": [ "<2.10" ], "v": "<2.10" }, { - "advisory": "Geonode 2.10 updates 'urllib3' to v1.24.2 to include security fixes.", - "cve": "CVE-2018-20060", - "id": "pyup.io-42969", - "more_info_path": "/vulnerabilities/CVE-2018-20060/42969", + "advisory": "Geonode 2.10 updates 'twisted' to v19.2.1 to include security fixes.", + "cve": "CVE-2019-12387", + "id": "pyup.io-42970", + "more_info_path": "/vulnerabilities/CVE-2019-12387/42970", "specs": [ "<2.10" ], @@ -49882,10 +50225,10 @@ "v": "<1.0.0" }, { - "advisory": "Geti-sdk 1.0.1 reduce permissions upon directory creation.\r\nhttps://github.com/openvinotoolkit/geti-sdk/pull/90", - "cve": "PVE-2023-54993", - "id": "pyup.io-54993", - "more_info_path": "/vulnerabilities/PVE-2023-54993/54993", + "advisory": "Geti-sdk 1.0.1 improves check for video processing in Geti.upload_project() to avoid potential infinite loop.\r\nhttps://github.com/openvinotoolkit/geti-sdk/pull/93", + "cve": "PVE-2023-54992", + "id": "pyup.io-54992", + "more_info_path": "/vulnerabilities/PVE-2023-54992/54992", "specs": [ "<1.0.1" ], @@ -49902,10 +50245,10 @@ "v": "<1.0.1" }, { - "advisory": "Geti-sdk 1.0.1 improves check for video processing in Geti.upload_project() to avoid potential infinite loop.\r\nhttps://github.com/openvinotoolkit/geti-sdk/pull/93", - "cve": "PVE-2023-54992", - "id": "pyup.io-54992", - "more_info_path": "/vulnerabilities/PVE-2023-54992/54992", + "advisory": "Geti-sdk 1.0.1 reduce permissions upon directory creation.\r\nhttps://github.com/openvinotoolkit/geti-sdk/pull/90", + "cve": "PVE-2023-54993", + "id": "pyup.io-54993", + "more_info_path": "/vulnerabilities/PVE-2023-54993/54993", "specs": [ "<1.0.1" ], @@ -49981,20 +50324,20 @@ ], "ggshield": [ { - "advisory": "Ggshield 1.18.0 updates its dependency 'cryptography' to version '41.0.3' to include a fix for an Insufficient Verification of Data Authenticity vulnerability.\r\nhttps://github.com/GitGuardian/ggshield/commit/3c67771a4d66accede14fa23dfce9ea51571e082", - "cve": "CVE-2023-3446", - "id": "pyup.io-60487", - "more_info_path": "/vulnerabilities/CVE-2023-3446/60487", + "advisory": "Ggshield 1.18.0 updates its dependency 'cryptography' to version '41.0.3' to include a fix for a Denial of Service vulnerability.\r\nhttps://github.com/GitGuardian/ggshield/commit/3c67771a4d66accede14fa23dfce9ea51571e082", + "cve": "CVE-2023-2975", + "id": "pyup.io-60486", + "more_info_path": "/vulnerabilities/CVE-2023-2975/60486", "specs": [ "<1.18.0" ], "v": "<1.18.0" }, { - "advisory": "Ggshield 1.18.0 updates its dependency 'cryptography' to version '41.0.3' to include a fix for a Denial of Service vulnerability.\r\nhttps://github.com/GitGuardian/ggshield/commit/3c67771a4d66accede14fa23dfce9ea51571e082", - "cve": "CVE-2023-2975", - "id": "pyup.io-60486", - "more_info_path": "/vulnerabilities/CVE-2023-2975/60486", + "advisory": "Ggshield 1.18.0 updates its dependency 'cryptography' to version '41.0.3' to include a fix for an Insufficient Verification of Data Authenticity vulnerability.\r\nhttps://github.com/GitGuardian/ggshield/commit/3c67771a4d66accede14fa23dfce9ea51571e082", + "cve": "CVE-2023-3446", + "id": "pyup.io-60487", + "more_info_path": "/vulnerabilities/CVE-2023-3446/60487", "specs": [ "<1.18.0" ], @@ -50048,9 +50391,9 @@ "gimpformats": [ { "advisory": "Gimpformats 2021 updates its dependency \"pillow\" to a version >= 8.1.1 to include security fixes.", - "cve": "CVE-2021-27923", - "id": "pyup.io-40211", - "more_info_path": "/vulnerabilities/CVE-2021-27923/40211", + "cve": "CVE-2020-35653", + "id": "pyup.io-42151", + "more_info_path": "/vulnerabilities/CVE-2020-35653/42151", "specs": [ "<2021" ], @@ -50058,9 +50401,9 @@ }, { "advisory": "Gimpformats 2021 updates its dependency \"pillow\" to a version >= 8.1.1 to include security fixes.", - "cve": "CVE-2020-35653", - "id": "pyup.io-42151", - "more_info_path": "/vulnerabilities/CVE-2020-35653/42151", + "cve": "CVE-2021-27923", + "id": "pyup.io-40211", + "more_info_path": "/vulnerabilities/CVE-2021-27923/40211", "specs": [ "<2021" ], @@ -50078,9 +50421,9 @@ }, { "advisory": "Gimpformats 2021 updates its dependency \"pillow\" to a version >= 8.1.1 to include security fixes.", - "cve": "CVE-2020-35654", - "id": "pyup.io-42150", - "more_info_path": "/vulnerabilities/CVE-2020-35654/42150", + "cve": "CVE-2021-27922", + "id": "pyup.io-42153", + "more_info_path": "/vulnerabilities/CVE-2021-27922/42153", "specs": [ "<2021" ], @@ -50088,9 +50431,9 @@ }, { "advisory": "Gimpformats 2021 updates its dependency \"pillow\" to a version >= 8.1.1 to include security fixes.", - "cve": "CVE-2021-27922", - "id": "pyup.io-42153", - "more_info_path": "/vulnerabilities/CVE-2021-27922/42153", + "cve": "CVE-2020-35654", + "id": "pyup.io-42150", + "more_info_path": "/vulnerabilities/CVE-2020-35654/42150", "specs": [ "<2021" ], @@ -50233,6 +50576,16 @@ "<2.0.0b25" ], "v": "<2.0.0b25" + }, + { + "advisory": "Affected versions of Giskard are vulnerable to Regular Expression Denial of Service (CWE-1333). This vulnerability allows attackers to trigger exponential regex evaluation times by supplying specially crafted text patterns, leading to denial of service through extended processing times or application crashes. The issue resides in the gruber regular expression used in the text perturbation detector's punctuation removal transformation. It is exploitable by submitting text with complex URL patterns that cause catastrophic backtracking.", + "cve": "CVE-2024-52524", + "id": "pyup.io-74160", + "more_info_path": "/vulnerabilities/CVE-2024-52524/74160", + "specs": [ + "<2.15.5" + ], + "v": "<2.15.5" } ], "git-batch": [ @@ -50321,20 +50674,20 @@ ], "github-changelog-md": [ { - "advisory": "Github-changelog-md version 0.8.1 has updated its GitPython dependency from 3.1.40 to 3.1.41 to address the security issue identified as CVE-2024-22190.\r\nhttps://github.com/seapagan/github-changelog-md/commit/cccc57445478b949679782ffc6b8ac6f7710af0a", - "cve": "CVE-2024-22190", - "id": "pyup.io-65066", - "more_info_path": "/vulnerabilities/CVE-2024-22190/65066", + "advisory": "Github-changelog-md version 0.8.1 has updated its jinja2 dependency from 3.1.2 to 3.1.3 to address the security issue identified as CVE-2024-22195.\r\nhttps://github.com/seapagan/github-changelog-md/commit/cccc57445478b949679782ffc6b8ac6f7710af0a", + "cve": "CVE-2024-22195", + "id": "pyup.io-65067", + "more_info_path": "/vulnerabilities/CVE-2024-22195/65067", "specs": [ "<0.8.1" ], "v": "<0.8.1" }, { - "advisory": "Github-changelog-md version 0.8.1 has updated its jinja2 dependency from 3.1.2 to 3.1.3 to address the security issue identified as CVE-2024-22195.\r\nhttps://github.com/seapagan/github-changelog-md/commit/cccc57445478b949679782ffc6b8ac6f7710af0a", - "cve": "CVE-2024-22195", - "id": "pyup.io-65067", - "more_info_path": "/vulnerabilities/CVE-2024-22195/65067", + "advisory": "Github-changelog-md version 0.8.1 has updated its GitPython dependency from 3.1.40 to 3.1.41 to address the security issue identified as CVE-2024-22190.\r\nhttps://github.com/seapagan/github-changelog-md/commit/cccc57445478b949679782ffc6b8ac6f7710af0a", + "cve": "CVE-2024-22190", + "id": "pyup.io-65066", + "more_info_path": "/vulnerabilities/CVE-2024-22190/65066", "specs": [ "<0.8.1" ], @@ -50366,9 +50719,9 @@ "githubkit": [ { "advisory": "Githubkit 0.9.4 updates its dependency 'cryptography' to v38.0.3 to include security fixes.", - "cve": "CVE-2022-3786", - "id": "pyup.io-52470", - "more_info_path": "/vulnerabilities/CVE-2022-3786/52470", + "cve": "CVE-2022-3602", + "id": "pyup.io-52515", + "more_info_path": "/vulnerabilities/CVE-2022-3602/52515", "specs": [ "<0.9.4" ], @@ -50376,9 +50729,9 @@ }, { "advisory": "Githubkit 0.9.4 updates its dependency 'cryptography' to v38.0.3 to include security fixes.", - "cve": "CVE-2022-3602", - "id": "pyup.io-52515", - "more_info_path": "/vulnerabilities/CVE-2022-3602/52515", + "cve": "CVE-2022-3786", + "id": "pyup.io-52470", + "more_info_path": "/vulnerabilities/CVE-2022-3786/52470", "specs": [ "<0.9.4" ], @@ -50399,20 +50752,20 @@ ], "gitlabci-checker": [ { - "advisory": "Gitlabci-checker 0.1.2 updates the `werkzeug` package from version 2.2.2 to 2.2.3 in response to CVE-2023-23934. This upgrade addresses specific vulnerabilities identified in the earlier version of `werkzeug`.", - "cve": "CVE-2023-23934", - "id": "pyup.io-70866", - "more_info_path": "/vulnerabilities/CVE-2023-23934/70866", + "advisory": "Gitlabci-checker 0.1.2 updates the `werkzeug` package from version 2.2.2 to 2.2.3 in response to CVE-2023-25577. This upgrade addresses specific vulnerabilities identified in the earlier version of `werkzeug`.", + "cve": "CVE-2023-25577", + "id": "pyup.io-70862", + "more_info_path": "/vulnerabilities/CVE-2023-25577/70862", "specs": [ "<0.1.2" ], "v": "<0.1.2" }, { - "advisory": "Gitlabci-checker 0.1.2 updates the `werkzeug` package from version 2.2.2 to 2.2.3 in response to CVE-2023-25577. This upgrade addresses specific vulnerabilities identified in the earlier version of `werkzeug`.", - "cve": "CVE-2023-25577", - "id": "pyup.io-70862", - "more_info_path": "/vulnerabilities/CVE-2023-25577/70862", + "advisory": "Gitlabci-checker 0.1.2 updates the `werkzeug` package from version 2.2.2 to 2.2.3 in response to CVE-2023-23934. This upgrade addresses specific vulnerabilities identified in the earlier version of `werkzeug`.", + "cve": "CVE-2023-23934", + "id": "pyup.io-70866", + "more_info_path": "/vulnerabilities/CVE-2023-23934/70866", "specs": [ "<0.1.2" ], @@ -51224,9 +51577,9 @@ "google-images-search": [ { "advisory": "Google-images-search 1.3.8 updates its dependency 'Pillow' to version 8.1.1 to include security fixes.", - "cve": "CVE-2021-25289", - "id": "pyup.io-43485", - "more_info_path": "/vulnerabilities/CVE-2021-25289/43485", + "cve": "CVE-2020-35655", + "id": "pyup.io-43480", + "more_info_path": "/vulnerabilities/CVE-2020-35655/43480", "specs": [ "<1.3.8" ], @@ -51234,9 +51587,9 @@ }, { "advisory": "Google-images-search 1.3.8 updates its dependency 'Pillow' to version 8.1.1 to include security fixes.", - "cve": "CVE-2021-25292", - "id": "pyup.io-43481", - "more_info_path": "/vulnerabilities/CVE-2021-25292/43481", + "cve": "CVE-2021-25293", + "id": "pyup.io-43482", + "more_info_path": "/vulnerabilities/CVE-2021-25293/43482", "specs": [ "<1.3.8" ], @@ -51244,9 +51597,9 @@ }, { "advisory": "Google-images-search 1.3.8 updates its dependency 'Pillow' to version 8.1.1 to include security fixes.", - "cve": "CVE-2021-25290", - "id": "pyup.io-43484", - "more_info_path": "/vulnerabilities/CVE-2021-25290/43484", + "cve": "CVE-2021-25289", + "id": "pyup.io-43485", + "more_info_path": "/vulnerabilities/CVE-2021-25289/43485", "specs": [ "<1.3.8" ], @@ -51254,9 +51607,9 @@ }, { "advisory": "Google-images-search 1.3.8 updates its dependency 'Pillow' to version 8.1.1 to include security fixes.", - "cve": "CVE-2021-25291", - "id": "pyup.io-43483", - "more_info_path": "/vulnerabilities/CVE-2021-25291/43483", + "cve": "CVE-2021-25292", + "id": "pyup.io-43481", + "more_info_path": "/vulnerabilities/CVE-2021-25292/43481", "specs": [ "<1.3.8" ], @@ -51264,9 +51617,9 @@ }, { "advisory": "Google-images-search 1.3.8 updates its dependency 'Pillow' to version 8.1.1 to include security fixes.", - "cve": "CVE-2021-25293", - "id": "pyup.io-43482", - "more_info_path": "/vulnerabilities/CVE-2021-25293/43482", + "cve": "CVE-2021-25290", + "id": "pyup.io-43484", + "more_info_path": "/vulnerabilities/CVE-2021-25290/43484", "specs": [ "<1.3.8" ], @@ -51274,9 +51627,9 @@ }, { "advisory": "Google-images-search 1.3.8 updates its dependency 'Pillow' to version 8.1.1 to include security fixes.", - "cve": "CVE-2020-35655", - "id": "pyup.io-43480", - "more_info_path": "/vulnerabilities/CVE-2020-35655/43480", + "cve": "CVE-2021-25291", + "id": "pyup.io-43483", + "more_info_path": "/vulnerabilities/CVE-2021-25291/43483", "specs": [ "<1.3.8" ], @@ -51336,49 +51689,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-21735", - "id": "pyup.io-51190", - "more_info_path": "/vulnerabilities/CVE-2022-21735/51190", - "specs": [ - "<1.12.0" - ], - "v": "<1.12.0" - }, - { - "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23573", - "id": "pyup.io-51214", - "more_info_path": "/vulnerabilities/CVE-2022-23573/51214", - "specs": [ - "<1.12.0" - ], - "v": "<1.12.0" - }, - { - "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-21736", - "id": "pyup.io-51191", - "more_info_path": "/vulnerabilities/CVE-2022-21736/51191", - "specs": [ - "<1.12.0" - ], - "v": "<1.12.0" - }, - { - "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23576", - "id": "pyup.io-51217", - "more_info_path": "/vulnerabilities/CVE-2022-23576/51217", - "specs": [ - "<1.12.0" - ], - "v": "<1.12.0" - }, - { - "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23582", - "id": "pyup.io-51222", - "more_info_path": "/vulnerabilities/CVE-2022-23582/51222", + "cve": "CVE-2022-22576", + "id": "pyup.io-51197", + "more_info_path": "/vulnerabilities/CVE-2022-22576/51197", "specs": [ "<1.12.0" ], @@ -51426,9 +51739,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23595", - "id": "pyup.io-51231", - "more_info_path": "/vulnerabilities/CVE-2022-23595/51231", + "cve": "CVE-2022-21731", + "id": "pyup.io-51186", + "more_info_path": "/vulnerabilities/CVE-2022-21731/51186", "specs": [ "<1.12.0" ], @@ -51436,19 +51749,19 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-21726", - "id": "pyup.io-51181", - "more_info_path": "/vulnerabilities/CVE-2022-21726/51181", + "cve": "CVE-2022-23559", + "id": "pyup.io-51200", + "more_info_path": "/vulnerabilities/CVE-2022-23559/51200", "specs": [ "<1.12.0" ], "v": "<1.12.0" }, { - "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-21731", - "id": "pyup.io-51186", - "more_info_path": "/vulnerabilities/CVE-2022-21731/51186", + "advisory": "Gordo 1.12.0 updates its dependency \"numpy\" to v1.21.0 to include a security fix.", + "cve": "CVE-2021-33430", + "id": "pyup.io-51152", + "more_info_path": "/vulnerabilities/CVE-2021-33430/51152", "specs": [ "<1.12.0" ], @@ -51456,9 +51769,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23570", - "id": "pyup.io-51211", - "more_info_path": "/vulnerabilities/CVE-2022-23570/51211", + "cve": "CVE-2022-21732", + "id": "pyup.io-51187", + "more_info_path": "/vulnerabilities/CVE-2022-21732/51187", "specs": [ "<1.12.0" ], @@ -51466,9 +51779,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23577", - "id": "pyup.io-51218", - "more_info_path": "/vulnerabilities/CVE-2022-23577/51218", + "cve": "CVE-2022-21734", + "id": "pyup.io-51189", + "more_info_path": "/vulnerabilities/CVE-2022-21734/51189", "specs": [ "<1.12.0" ], @@ -51476,19 +51789,19 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23559", - "id": "pyup.io-51200", - "more_info_path": "/vulnerabilities/CVE-2022-23559/51200", + "cve": "CVE-2022-23595", + "id": "pyup.io-51231", + "more_info_path": "/vulnerabilities/CVE-2022-23595/51231", "specs": [ "<1.12.0" ], "v": "<1.12.0" }, { - "advisory": "Gordo 1.12.0 updates its dependency \"numpy\" to v1.21.0 to include a security fix.", - "cve": "CVE-2021-33430", - "id": "pyup.io-51152", - "more_info_path": "/vulnerabilities/CVE-2021-33430/51152", + "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", + "cve": "CVE-2022-23591", + "id": "pyup.io-51230", + "more_info_path": "/vulnerabilities/CVE-2022-23591/51230", "specs": [ "<1.12.0" ], @@ -51496,9 +51809,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23581", - "id": "pyup.io-51221", - "more_info_path": "/vulnerabilities/CVE-2022-23581/51221", + "cve": "CVE-2022-23584", + "id": "pyup.io-51224", + "more_info_path": "/vulnerabilities/CVE-2022-23584/51224", "specs": [ "<1.12.0" ], @@ -51506,9 +51819,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-21732", - "id": "pyup.io-51187", - "more_info_path": "/vulnerabilities/CVE-2022-21732/51187", + "cve": "CVE-2022-23582", + "id": "pyup.io-51222", + "more_info_path": "/vulnerabilities/CVE-2022-23582/51222", "specs": [ "<1.12.0" ], @@ -51516,9 +51829,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23591", - "id": "pyup.io-51230", - "more_info_path": "/vulnerabilities/CVE-2022-23591/51230", + "cve": "CVE-2022-23581", + "id": "pyup.io-51221", + "more_info_path": "/vulnerabilities/CVE-2022-23581/51221", "specs": [ "<1.12.0" ], @@ -51526,9 +51839,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-21734", - "id": "pyup.io-51189", - "more_info_path": "/vulnerabilities/CVE-2022-21734/51189", + "cve": "CVE-2022-23579", + "id": "pyup.io-51220", + "more_info_path": "/vulnerabilities/CVE-2022-23579/51220", "specs": [ "<1.12.0" ], @@ -51536,9 +51849,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23565", - "id": "pyup.io-51206", - "more_info_path": "/vulnerabilities/CVE-2022-23565/51206", + "cve": "CVE-2022-23578", + "id": "pyup.io-51219", + "more_info_path": "/vulnerabilities/CVE-2022-23578/51219", "specs": [ "<1.12.0" ], @@ -51546,9 +51859,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-21729", - "id": "pyup.io-51184", - "more_info_path": "/vulnerabilities/CVE-2022-21729/51184", + "cve": "CVE-2022-23577", + "id": "pyup.io-51218", + "more_info_path": "/vulnerabilities/CVE-2022-23577/51218", "specs": [ "<1.12.0" ], @@ -51556,9 +51869,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23568", - "id": "pyup.io-51209", - "more_info_path": "/vulnerabilities/CVE-2022-23568/51209", + "cve": "CVE-2022-23583", + "id": "pyup.io-51223", + "more_info_path": "/vulnerabilities/CVE-2022-23583/51223", "specs": [ "<1.12.0" ], @@ -51566,9 +51879,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23583", - "id": "pyup.io-51223", - "more_info_path": "/vulnerabilities/CVE-2022-23583/51223", + "cve": "CVE-2022-23576", + "id": "pyup.io-51217", + "more_info_path": "/vulnerabilities/CVE-2022-23576/51217", "specs": [ "<1.12.0" ], @@ -51586,9 +51899,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-22576", - "id": "pyup.io-51197", - "more_info_path": "/vulnerabilities/CVE-2022-22576/51197", + "cve": "CVE-2022-23575", + "id": "pyup.io-51216", + "more_info_path": "/vulnerabilities/CVE-2022-23575/51216", "specs": [ "<1.12.0" ], @@ -51596,9 +51909,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-21738", - "id": "pyup.io-51193", - "more_info_path": "/vulnerabilities/CVE-2022-21738/51193", + "cve": "CVE-2022-23574", + "id": "pyup.io-51215", + "more_info_path": "/vulnerabilities/CVE-2022-23574/51215", "specs": [ "<1.12.0" ], @@ -51606,9 +51919,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23574", - "id": "pyup.io-51215", - "more_info_path": "/vulnerabilities/CVE-2022-23574/51215", + "cve": "CVE-2022-23573", + "id": "pyup.io-51214", + "more_info_path": "/vulnerabilities/CVE-2022-23573/51214", "specs": [ "<1.12.0" ], @@ -51616,9 +51929,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-21725", - "id": "pyup.io-51180", - "more_info_path": "/vulnerabilities/CVE-2022-21725/51180", + "cve": "CVE-2022-23570", + "id": "pyup.io-51211", + "more_info_path": "/vulnerabilities/CVE-2022-23570/51211", "specs": [ "<1.12.0" ], @@ -51626,9 +51939,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-21730", - "id": "pyup.io-51185", - "more_info_path": "/vulnerabilities/CVE-2022-21730/51185", + "cve": "CVE-2022-23589", + "id": "pyup.io-51229", + "more_info_path": "/vulnerabilities/CVE-2022-23589/51229", "specs": [ "<1.12.0" ], @@ -51636,9 +51949,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23569", - "id": "pyup.io-51210", - "more_info_path": "/vulnerabilities/CVE-2022-23569/51210", + "cve": "CVE-2022-23587", + "id": "pyup.io-51227", + "more_info_path": "/vulnerabilities/CVE-2022-23587/51227", "specs": [ "<1.12.0" ], @@ -51646,9 +51959,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23589", - "id": "pyup.io-51229", - "more_info_path": "/vulnerabilities/CVE-2022-23589/51229", + "cve": "CVE-2022-23565", + "id": "pyup.io-51206", + "more_info_path": "/vulnerabilities/CVE-2022-23565/51206", "specs": [ "<1.12.0" ], @@ -51656,9 +51969,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23587", - "id": "pyup.io-51227", - "more_info_path": "/vulnerabilities/CVE-2022-23587/51227", + "cve": "CVE-2022-23564", + "id": "pyup.io-51205", + "more_info_path": "/vulnerabilities/CVE-2022-23564/51205", "specs": [ "<1.12.0" ], @@ -51686,9 +51999,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-21737", - "id": "pyup.io-51192", - "more_info_path": "/vulnerabilities/CVE-2022-21737/51192", + "cve": "CVE-2022-21738", + "id": "pyup.io-51193", + "more_info_path": "/vulnerabilities/CVE-2022-21738/51193", "specs": [ "<1.12.0" ], @@ -51696,9 +52009,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-21741", - "id": "pyup.io-51196", - "more_info_path": "/vulnerabilities/CVE-2022-21741/51196", + "cve": "CVE-2022-21737", + "id": "pyup.io-51192", + "more_info_path": "/vulnerabilities/CVE-2022-21737/51192", "specs": [ "<1.12.0" ], @@ -51706,9 +52019,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23560", - "id": "pyup.io-51201", - "more_info_path": "/vulnerabilities/CVE-2022-23560/51201", + "cve": "CVE-2022-21741", + "id": "pyup.io-51196", + "more_info_path": "/vulnerabilities/CVE-2022-21741/51196", "specs": [ "<1.12.0" ], @@ -51716,9 +52029,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-21727", - "id": "pyup.io-51182", - "more_info_path": "/vulnerabilities/CVE-2022-21727/51182", + "cve": "CVE-2022-23569", + "id": "pyup.io-51210", + "more_info_path": "/vulnerabilities/CVE-2022-23569/51210", "specs": [ "<1.12.0" ], @@ -51726,9 +52039,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23564", - "id": "pyup.io-51205", - "more_info_path": "/vulnerabilities/CVE-2022-23564/51205", + "cve": "CVE-2022-23560", + "id": "pyup.io-51201", + "more_info_path": "/vulnerabilities/CVE-2022-23560/51201", "specs": [ "<1.12.0" ], @@ -51736,9 +52049,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23557", - "id": "pyup.io-51198", - "more_info_path": "/vulnerabilities/CVE-2022-23557/51198", + "cve": "CVE-2022-21735", + "id": "pyup.io-51190", + "more_info_path": "/vulnerabilities/CVE-2022-21735/51190", "specs": [ "<1.12.0" ], @@ -51746,9 +52059,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23584", - "id": "pyup.io-51224", - "more_info_path": "/vulnerabilities/CVE-2022-23584/51224", + "cve": "CVE-2022-23566", + "id": "pyup.io-51207", + "more_info_path": "/vulnerabilities/CVE-2022-23566/51207", "specs": [ "<1.12.0" ], @@ -51756,9 +52069,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23566", - "id": "pyup.io-51207", - "more_info_path": "/vulnerabilities/CVE-2022-23566/51207", + "cve": "CVE-2022-23557", + "id": "pyup.io-51198", + "more_info_path": "/vulnerabilities/CVE-2022-23557/51198", "specs": [ "<1.12.0" ], @@ -51786,9 +52099,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23575", - "id": "pyup.io-51216", - "more_info_path": "/vulnerabilities/CVE-2022-23575/51216", + "cve": "CVE-2022-23571", + "id": "pyup.io-51212", + "more_info_path": "/vulnerabilities/CVE-2022-23571/51212", "specs": [ "<1.12.0" ], @@ -51806,9 +52119,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23578", - "id": "pyup.io-51219", - "more_info_path": "/vulnerabilities/CVE-2022-23578/51219", + "cve": "CVE-2022-23563", + "id": "pyup.io-51204", + "more_info_path": "/vulnerabilities/CVE-2022-23563/51204", "specs": [ "<1.12.0" ], @@ -51816,9 +52129,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23571", - "id": "pyup.io-51212", - "more_info_path": "/vulnerabilities/CVE-2022-23571/51212", + "cve": "CVE-2022-23561", + "id": "pyup.io-51202", + "more_info_path": "/vulnerabilities/CVE-2022-23561/51202", "specs": [ "<1.12.0" ], @@ -51836,9 +52149,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23563", - "id": "pyup.io-51204", - "more_info_path": "/vulnerabilities/CVE-2022-23563/51204", + "cve": "CVE-2022-21729", + "id": "pyup.io-51184", + "more_info_path": "/vulnerabilities/CVE-2022-21729/51184", "specs": [ "<1.12.0" ], @@ -51846,9 +52159,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23561", - "id": "pyup.io-51202", - "more_info_path": "/vulnerabilities/CVE-2022-23561/51202", + "cve": "CVE-2022-21725", + "id": "pyup.io-51180", + "more_info_path": "/vulnerabilities/CVE-2022-21725/51180", "specs": [ "<1.12.0" ], @@ -51856,9 +52169,9 @@ }, { "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", - "cve": "CVE-2022-23579", - "id": "pyup.io-51220", - "more_info_path": "/vulnerabilities/CVE-2022-23579/51220", + "cve": "CVE-2022-23568", + "id": "pyup.io-51209", + "more_info_path": "/vulnerabilities/CVE-2022-23568/51209", "specs": [ "<1.12.0" ], @@ -51874,6 +52187,46 @@ ], "v": "<1.12.0" }, + { + "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", + "cve": "CVE-2022-21736", + "id": "pyup.io-51191", + "more_info_path": "/vulnerabilities/CVE-2022-21736/51191", + "specs": [ + "<1.12.0" + ], + "v": "<1.12.0" + }, + { + "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", + "cve": "CVE-2022-21730", + "id": "pyup.io-51185", + "more_info_path": "/vulnerabilities/CVE-2022-21730/51185", + "specs": [ + "<1.12.0" + ], + "v": "<1.12.0" + }, + { + "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", + "cve": "CVE-2022-21727", + "id": "pyup.io-51182", + "more_info_path": "/vulnerabilities/CVE-2022-21727/51182", + "specs": [ + "<1.12.0" + ], + "v": "<1.12.0" + }, + { + "advisory": "Gordo 1.12.0 updates its dependency \"TensorFlow\" requirement to \"~=2.7.0\" to include security fixes.", + "cve": "CVE-2022-21726", + "id": "pyup.io-51181", + "more_info_path": "/vulnerabilities/CVE-2022-21726/51181", + "specs": [ + "<1.12.0" + ], + "v": "<1.12.0" + }, { "advisory": "Gordo 5.1.2 updates its dependency 'cryptography' to version '41.0.0' to include a security fix.\r\nhttps://github.com/equinor/gordo/pull/1324/commits/3e02a6e184236c6406fb6faa1dda440baa2af68a", "cve": "CVE-2023-2650", @@ -52049,20 +52402,20 @@ ], "gps-time": [ { - "advisory": "Gps-time 2.8.6 updates its Ruby dependency 'nokogiri' to v1.11.0 to include a security fix.", - "cve": "CVE-2020-26247", - "id": "pyup.io-43743", - "more_info_path": "/vulnerabilities/CVE-2020-26247/43743", + "advisory": "Gps-time 2.8.6 updates its dependency 'kramdown' to v2.3.1 to include a security fix.", + "cve": "CVE-2021-28834", + "id": "pyup.io-43739", + "more_info_path": "/vulnerabilities/CVE-2021-28834/43739", "specs": [ "<2.8.6" ], "v": "<2.8.6" }, { - "advisory": "Gps-time 2.8.6 updates its dependency 'kramdown' to v2.3.1 to include a security fix.", - "cve": "CVE-2021-28834", - "id": "pyup.io-43739", - "more_info_path": "/vulnerabilities/CVE-2021-28834/43739", + "advisory": "Gps-time 2.8.6 updates its Ruby dependency 'nokogiri' to v1.11.0 to include a security fix.", + "cve": "CVE-2020-26247", + "id": "pyup.io-43743", + "more_info_path": "/vulnerabilities/CVE-2020-26247/43743", "specs": [ "<2.8.6" ], @@ -52081,6 +52434,19 @@ "v": "<0.3.0" } ], + "gr-mg": [ + { + "advisory": "The OpenSSF Package Analysis project has identified the PyPI package 'gr-mg' version 99.6 as malicious because it communicates with a domain associated with malicious activity, indicating potential security risks.", + "cve": "PVE-2024-74246", + "id": "pyup.io-74246", + "more_info_path": "/vulnerabilities/PVE-2024-74246/74246", + "specs": [ + ">=0", + "<=0" + ], + "v": ">=0,<=0" + } + ], "gradio": [ { "advisory": "Gradio 2.6.0 fixes arbitrary file read vulnerabilities.\r\nhttps://github.com/gradio-app/gradio/pull/406", @@ -52213,20 +52579,20 @@ "v": "<4.19.1" }, { - "advisory": "Affected versions of the gradio package are vulnerable to improper file handling. This vulnerability could allow unauthorized access to files not properly uploaded, leading to potential data exposure or manipulation. The vulnerable functions include file processing methods that did not verify file locations. Exploitability depends on the ability to manipulate file paths or access non-uploaded files. The patch includes checks to ensure files are in the designated upload folder before processing. This vulnerability is specific to environments where file uploads are handled. The issue is tracked under CWE-552: Files or Directories Accessible to External Parties.", - "cve": "CVE-2024-1728", - "id": "pyup.io-73493", - "more_info_path": "/vulnerabilities/CVE-2024-1728/73493", + "advisory": "A local file include could be remotely triggered in Gradio due to a vulnerable user-supplied JSON value in an API request.", + "cve": "PVE-2024-99761", + "id": "pyup.io-66709", + "more_info_path": "/vulnerabilities/PVE-2024-99761/66709", "specs": [ "<4.19.2" ], "v": "<4.19.2" }, { - "advisory": "A local file include could be remotely triggered in Gradio due to a vulnerable user-supplied JSON value in an API request.", - "cve": "PVE-2024-99761", - "id": "pyup.io-66709", - "more_info_path": "/vulnerabilities/PVE-2024-99761/66709", + "advisory": "Affected versions of the gradio package are vulnerable to improper file handling. This vulnerability could allow unauthorized access to files not properly uploaded, leading to potential data exposure or manipulation. The vulnerable functions include file processing methods that did not verify file locations. Exploitability depends on the ability to manipulate file paths or access non-uploaded files. The patch includes checks to ensure files are in the designated upload folder before processing. This vulnerability is specific to environments where file uploads are handled. The issue is tracked under CWE-552: Files or Directories Accessible to External Parties.", + "cve": "CVE-2024-1728", + "id": "pyup.io-73493", + "more_info_path": "/vulnerabilities/CVE-2024-1728/73493", "specs": [ "<4.19.2" ], @@ -52575,20 +52941,20 @@ "v": "<0.7.0" }, { - "advisory": "Graphscope 0.7.0 updates its dependency 'Apache Commons IO' to v2.7 to include a security fix.", - "cve": "CVE-2021-29425", - "id": "pyup.io-42560", - "more_info_path": "/vulnerabilities/CVE-2021-29425/42560", + "advisory": "Graphscope 0.7.0 updates its dependency 'Junit4' to v4.13.2 to include a security fix.", + "cve": "CVE-2020-15250", + "id": "pyup.io-42562", + "more_info_path": "/vulnerabilities/CVE-2020-15250/42562", "specs": [ "<0.7.0" ], "v": "<0.7.0" }, { - "advisory": "Graphscope 0.7.0 updates its dependency 'Junit4' to v4.13.2 to include a security fix.", - "cve": "CVE-2020-15250", - "id": "pyup.io-42562", - "more_info_path": "/vulnerabilities/CVE-2020-15250/42562", + "advisory": "Graphscope 0.7.0 updates its dependency 'Apache Commons IO' to v2.7 to include a security fix.", + "cve": "CVE-2021-29425", + "id": "pyup.io-42560", + "more_info_path": "/vulnerabilities/CVE-2021-29425/42560", "specs": [ "<0.7.0" ], @@ -52607,6 +52973,18 @@ "v": ">=0" } ], + "greenbids-tailor": [ + { + "advisory": "Affected versions of the GreenBids Tailor package are potentially susceptible to Insecure Temporary File Handling (CWE-377) due to the use of a world-writable directory (/tmp) for the download lock file (greenbids-tailor-download.lock). This setup could allow local attackers with system access to manipulate the lock file, potentially causing denial of service by disrupting the download process. The vulnerability arises from storing lock files in directories with permissive access controls. To mitigate this risk, upgrade to the latest version that allows configuring a secure directory for lock files, such as /var/lock/greenbids-tailor-download.lock, and ensure appropriate filesystem permissions are enforced.", + "cve": "PVE-2024-74159", + "id": "pyup.io-74159", + "more_info_path": "/vulnerabilities/PVE-2024-74159/74159", + "specs": [ + "<0.2.5" + ], + "v": "<0.2.5" + } + ], "gretel-client": [ { "advisory": "Gretel-client 0.16.2 updates its dependency 'urllib3' requirement to '>=1.26.5' to include a security fix.", @@ -52655,20 +53033,20 @@ ], "grpcio": [ { - "advisory": "Grpcio 1.2.0 includes a fix for CVE-2017-7860: Google gRPC before 2017-02-22 has an out-of-bounds write caused by a heap-based buffer overflow related to the parse_unix function in core/ext/client_channel/parse_address.c.\r\nhttps://github.com/grpc/grpc/pull/9833/commits/bcd5f12e4bca2ed2c00cddb5ffd046aef6f4fb31", - "cve": "CVE-2017-7860", - "id": "pyup.io-47265", - "more_info_path": "/vulnerabilities/CVE-2017-7860/47265", + "advisory": "Grpcio 1.2.0 includes a fix for CVE-2017-7861: Google gRPC before 2017-02-22 has an out-of-bounds write related to the gpr_free function in core/lib/support/alloc.c.\r\nhttps://github.com/grpc/grpc/pull/9833/commits/bcd5f12e4bca2ed2c00cddb5ffd046aef6f4fb31", + "cve": "CVE-2017-7861", + "id": "pyup.io-47262", + "more_info_path": "/vulnerabilities/CVE-2017-7861/47262", "specs": [ "<1.2.0" ], "v": "<1.2.0" }, { - "advisory": "Grpcio 1.2.0 includes a fix for CVE-2017-7861: Google gRPC before 2017-02-22 has an out-of-bounds write related to the gpr_free function in core/lib/support/alloc.c.\r\nhttps://github.com/grpc/grpc/pull/9833/commits/bcd5f12e4bca2ed2c00cddb5ffd046aef6f4fb31", - "cve": "CVE-2017-7861", - "id": "pyup.io-47262", - "more_info_path": "/vulnerabilities/CVE-2017-7861/47262", + "advisory": "Grpcio 1.2.0 includes a fix for CVE-2017-7860: Google gRPC before 2017-02-22 has an out-of-bounds write caused by a heap-based buffer overflow related to the parse_unix function in core/ext/client_channel/parse_address.c.\r\nhttps://github.com/grpc/grpc/pull/9833/commits/bcd5f12e4bca2ed2c00cddb5ffd046aef6f4fb31", + "cve": "CVE-2017-7860", + "id": "pyup.io-47265", + "more_info_path": "/vulnerabilities/CVE-2017-7860/47265", "specs": [ "<1.2.0" ], @@ -52694,6 +53072,16 @@ ], "v": "<1.3.0" }, + { + "advisory": "gRPC contains a vulnerability whereby a client can cause a termination of the connection between an HTTP2 proxy and a gRPC server: a base64 encoding error for `-bin` suffixed headers will result in a disconnection by the gRPC server, but is typically allowed by HTTP2 proxies.", + "cve": "CVE-2023-32732", + "id": "pyup.io-71995", + "more_info_path": "/vulnerabilities/CVE-2023-32732/71995", + "specs": [ + "<1.53.0" + ], + "v": "<1.53.0" + }, { "advisory": "Grpcio 1.53.0 includes a fix for a Connection Confusion vulnerability. When gRPC HTTP2 stack raised a header size exceeded error, it skipped parsing the rest of the HPACK frame. This caused any HPACK table mutations to also be skipped, resulting in a desynchronization of HPACK tables between sender and receiver. If leveraged, say, between a proxy and a backend, this could lead to requests from the proxy being interpreted as containing headers from different proxy clients - leading to an information leak that can be used for privilege escalation or data exfiltration.\r\nhttps://github.com/advisories/GHSA-cfgp-2977-2fmm", "cve": "CVE-2023-32731", @@ -52714,16 +53102,6 @@ ], "v": "<1.53.0" }, - { - "advisory": "gRPC contains a vulnerability whereby a client can cause a termination of the connection between an HTTP2 proxy and a gRPC server: a base64 encoding error for `-bin` suffixed headers will result in a disconnection by the gRPC server, but is typically allowed by HTTP2 proxies.", - "cve": "CVE-2023-32732", - "id": "pyup.io-71995", - "more_info_path": "/vulnerabilities/CVE-2023-32732/71995", - "specs": [ - "<1.53.0" - ], - "v": "<1.53.0" - }, { "advisory": "There exists an vulnerability causing an abort() to be called in gRPC. The following headers cause gRPC's C++ implementation to abort() when called via http2: te: x (x != trailers) :scheme: x (x != http, https) grpclb_client_stats: x (x == anything) On top of sending one of those headers, a later header must be sent that gets the total header size past 8KB.", "cve": "CVE-2023-1428", @@ -53099,9 +53477,9 @@ "gyver": [ { "advisory": "Gyver 2.8.3 updates its dependency 'cryptography' to versions ^41.0.4 to include security fixes.", - "cve": "CVE-2023-38325", - "id": "pyup.io-61403", - "more_info_path": "/vulnerabilities/CVE-2023-38325/61403", + "cve": "CVE-2023-3446", + "id": "pyup.io-61396", + "more_info_path": "/vulnerabilities/CVE-2023-3446/61396", "specs": [ "<2.8.3" ], @@ -53109,9 +53487,9 @@ }, { "advisory": "Gyver 2.8.3 updates its dependency 'cryptography' to versions ^41.0.4 to include security fixes.", - "cve": "CVE-2023-3446", - "id": "pyup.io-61396", - "more_info_path": "/vulnerabilities/CVE-2023-3446/61396", + "cve": "CVE-2023-38325", + "id": "pyup.io-61403", + "more_info_path": "/vulnerabilities/CVE-2023-38325/61403", "specs": [ "<2.8.3" ], @@ -53151,9 +53529,9 @@ "h2o": [ { "advisory": "H2o 3.34.0.7 updates its MAVEN dependency 'log4j' to version 2.17.0 to address critical and severe vulnerabilities. \r\nThe dependency is included in /h2o/backend/bin/h2o.jar", - "cve": "CVE-2021-45105", - "id": "pyup.io-43439", - "more_info_path": "/vulnerabilities/CVE-2021-45105/43439", + "cve": "CVE-2021-45046", + "id": "pyup.io-43398", + "more_info_path": "/vulnerabilities/CVE-2021-45046/43398", "specs": [ "<3.34.0.7" ], @@ -53161,9 +53539,9 @@ }, { "advisory": "H2o 3.34.0.7 updates its MAVEN dependency 'log4j' to version 2.17.0 to address critical and severe vulnerabilities. \r\nThe dependency is included in /h2o/backend/bin/h2o.jar", - "cve": "CVE-2021-44228", - "id": "pyup.io-43397", - "more_info_path": "/vulnerabilities/CVE-2021-44228/43397", + "cve": "CVE-2021-45105", + "id": "pyup.io-43439", + "more_info_path": "/vulnerabilities/CVE-2021-45105/43439", "specs": [ "<3.34.0.7" ], @@ -53171,9 +53549,9 @@ }, { "advisory": "H2o 3.34.0.7 updates its MAVEN dependency 'log4j' to version 2.17.0 to address critical and severe vulnerabilities. \r\nThe dependency is included in /h2o/backend/bin/h2o.jar", - "cve": "CVE-2021-45046", - "id": "pyup.io-43398", - "more_info_path": "/vulnerabilities/CVE-2021-45046/43398", + "cve": "CVE-2021-44228", + "id": "pyup.io-43397", + "more_info_path": "/vulnerabilities/CVE-2021-44228/43397", "specs": [ "<3.34.0.7" ], @@ -53249,16 +53627,6 @@ ], "v": "<3.42.0.3" }, - { - "advisory": "H2o 3.44.0.1 updates its MAVEN dependency 'org.codehaus.jettison:jettison' to '1.5.4' to fix CVE-2022-40150.\r\nThe dependency is included in /h2o/backend/bin/h2o.jar", - "cve": "CVE-2022-40150", - "id": "pyup.io-59334", - "more_info_path": "/vulnerabilities/CVE-2022-40150/59334", - "specs": [ - "<3.44.0.1" - ], - "v": "<3.44.0.1" - }, { "advisory": "H2o 3.44.0.1 updates its MAVEN dependency 'org.codehaus.jettison:jettison' to '1.5.4' to fix CVE-2023-1436.\r\nThe dependency is included in /h2o/backend/bin/h2o.jar", "cve": "CVE-2023-1436", @@ -53280,20 +53648,20 @@ "v": "<3.44.0.1" }, { - "advisory": "H2o 3.44.0.1 updates its MAVEN dependency 'org.codehaus.jettison:jettison' to '1.5.4' to fix CVE-2022-40149.\r\nThe dependency is included in /h2o/backend/bin/h2o.jar", - "cve": "CVE-2022-40149", - "id": "pyup.io-72501", - "more_info_path": "/vulnerabilities/CVE-2022-40149/72501", + "advisory": "H2o 3.44.0.1 updates its MAVEN dependency 'org.codehaus.jettison:jettison' to '1.5.4' to fix CVE-2022-45685.\r\nThe dependency is included in /h2o/backend/bin/h2o.jar", + "cve": "CVE-2022-45685", + "id": "pyup.io-59333", + "more_info_path": "/vulnerabilities/CVE-2022-45685/59333", "specs": [ "<3.44.0.1" ], "v": "<3.44.0.1" }, { - "advisory": "H2o 3.44.0.1 updates its MAVEN dependency 'org.codehaus.jettison:jettison' to '1.5.4' to fix CVE-2022-45685.\r\nThe dependency is included in /h2o/backend/bin/h2o.jar", - "cve": "CVE-2022-45685", - "id": "pyup.io-59333", - "more_info_path": "/vulnerabilities/CVE-2022-45685/59333", + "advisory": "H2o 3.44.0.1 updates its MAVEN dependency 'org.codehaus.jettison:jettison' to '1.5.4' to fix CVE-2022-40150.\r\nThe dependency is included in /h2o/backend/bin/h2o.jar", + "cve": "CVE-2022-40150", + "id": "pyup.io-59334", + "more_info_path": "/vulnerabilities/CVE-2022-40150/59334", "specs": [ "<3.44.0.1" ], @@ -53309,6 +53677,16 @@ ], "v": "<3.44.0.1" }, + { + "advisory": "H2o 3.44.0.1 updates its MAVEN dependency 'org.codehaus.jettison:jettison' to '1.5.4' to fix CVE-2022-40149.\r\nThe dependency is included in /h2o/backend/bin/h2o.jar", + "cve": "CVE-2022-40149", + "id": "pyup.io-72501", + "more_info_path": "/vulnerabilities/CVE-2022-40149/72501", + "specs": [ + "<3.44.0.1" + ], + "v": "<3.44.0.1" + }, { "advisory": "H2o 3.44.0.2 updates its MAVEN dependency org.python:jython due to a Use After Free vulnerability of com.github.jnr:jnr-posix.\r\nThe dependency is included in /h2o/backend/bin/h2o.jar", "cve": "PVE-2023-63047", @@ -53339,16 +53717,6 @@ ], "v": "<3.46.0.4" }, - { - "advisory": "Affected versions of H2o are vulnerable to External Control of File Name or Path. Remote unauthenticated attackers can overwrite arbitrary server files with attacker-controllable data. The data that the attacker can control is not entirely arbitrary. H2o writes a CSV/XLS/etc file to disk, so the attacker data is wrapped in quotations and starts with \"C1\", if they're exporting as CSV.\r\nThe vulnerable code is found in /h2o/backend/bin/h2o.jar", - "cve": "CVE-2023-6569", - "id": "pyup.io-65214", - "more_info_path": "/vulnerabilities/CVE-2023-6569/65214", - "specs": [ - ">=0" - ], - "v": ">=0" - }, { "advisory": "Affected versions of H2o are vulnerable to CVE-2024-5979: The 'run_tool' command in the 'rapids' component allows the 'main' function of any class under the 'water.tools' namespace to be called. One such class, 'MojoConvertTool', crashes the server when invoked with an invalid argument, causing a denial of service.\r\nThe vulnerable code is found in /h2o/backend/bin/h2o.jar", "cve": "CVE-2024-5979", @@ -53368,6 +53736,16 @@ ">=0" ], "v": ">=0" + }, + { + "advisory": "Affected versions of H2o are vulnerable to External Control of File Name or Path. Remote unauthenticated attackers can overwrite arbitrary server files with attacker-controllable data. The data that the attacker can control is not entirely arbitrary. H2o writes a CSV/XLS/etc file to disk, so the attacker data is wrapped in quotations and starts with \"C1\", if they're exporting as CSV.\r\nThe vulnerable code is found in /h2o/backend/bin/h2o.jar", + "cve": "CVE-2023-6569", + "id": "pyup.io-65214", + "more_info_path": "/vulnerabilities/CVE-2023-6569/65214", + "specs": [ + ">=0" + ], + "v": ">=0" } ], "hail": [ @@ -53383,9 +53761,9 @@ }, { "advisory": "Hail 0.2.80 updates dependencies related to 'log4j' to fix critical and severe vulnerabilities.\r\nhttps://github.com/hail-is/hail/commit/1ad3b9822d4f2d77442d6da93ea4d9ca87796d22", - "cve": "CVE-2021-44228", - "id": "pyup.io-43597", - "more_info_path": "/vulnerabilities/CVE-2021-44228/43597", + "cve": "CVE-2021-45046", + "id": "pyup.io-43598", + "more_info_path": "/vulnerabilities/CVE-2021-45046/43598", "specs": [ "<0.2.80" ], @@ -53393,9 +53771,9 @@ }, { "advisory": "Hail 0.2.80 updates dependencies related to 'log4j' to fix critical and severe vulnerabilities.\r\nhttps://github.com/hail-is/hail/commit/1ad3b9822d4f2d77442d6da93ea4d9ca87796d22", - "cve": "CVE-2021-45046", - "id": "pyup.io-43598", - "more_info_path": "/vulnerabilities/CVE-2021-45046/43598", + "cve": "CVE-2021-44228", + "id": "pyup.io-43597", + "more_info_path": "/vulnerabilities/CVE-2021-44228/43597", "specs": [ "<0.2.80" ], @@ -53927,9 +54305,9 @@ }, { "advisory": "Holepunch 1.0.0 drops support for Python 2 and 3 < 3.6. These versions are not receiving security updates anymore.", - "cve": "CVE-2020-8492", - "id": "pyup.io-43394", - "more_info_path": "/vulnerabilities/CVE-2020-8492/43394", + "cve": "CVE-2020-27619", + "id": "pyup.io-43392", + "more_info_path": "/vulnerabilities/CVE-2020-27619/43392", "specs": [ "<1.0.0" ], @@ -53937,9 +54315,9 @@ }, { "advisory": "Holepunch 1.0.0 drops support for Python 2 and 3 < 3.6. These versions are not receiving security updates anymore.", - "cve": "CVE-2020-27619", - "id": "pyup.io-43392", - "more_info_path": "/vulnerabilities/CVE-2020-27619/43392", + "cve": "CVE-2020-8492", + "id": "pyup.io-43394", + "more_info_path": "/vulnerabilities/CVE-2020-8492/43394", "specs": [ "<1.0.0" ], @@ -54084,20 +54462,10 @@ "v": "<2023.8.1" }, { - "advisory": "Home assistant is an open source home automation. The Home Assistant login page allows users to use their local Home Assistant credentials and log in to another website that specifies the `redirect_uri` and `client_id` parameters. Although the `redirect_uri` validation typically ensures that it matches the `client_id` and the scheme represents either `http` or `https`, Home Assistant will fetch the `client_id` and check for `` HTML tags on the page. These URLs are not subjected to the same scheme validation and thus allow for arbitrary JavaScript execution on the Home Assistant administration page via usage of `javascript:` scheme URIs. This Cross-site Scripting (XSS) vulnerability can be executed on the Home Assistant frontend domain, which may be used for a full takeover of the Home Assistant account and installation. This issue has been addressed in version 2023.9.0 and all users are advised to upgrade. There are no known workarounds for this vulnerability.", - "cve": "CVE-2023-41895", - "id": "pyup.io-70402", - "more_info_path": "/vulnerabilities/CVE-2023-41895/70402", - "specs": [ - "<2023.9.0" - ], - "v": "<2023.9.0" - }, - { - "advisory": "Homeassistant 2023.9.0 includes a fix for CVE-2023-41899: In affected versions the 'hassio.addon_stdin' is vulnerable to a partial Server-Side Request Forgery where an attacker capable of calling this service (e.g.: through GHSA-h2jp-7grc-9xpp) may be able to invoke any Supervisor REST API endpoints with a POST request. An attacker able to exploit will be able to control the data dictionary, including its addon and input key/values.\r\nhttps://github.com/home-assistant/core/pull/99232", - "cve": "CVE-2023-41899", - "id": "pyup.io-63183", - "more_info_path": "/vulnerabilities/CVE-2023-41899/63183", + "advisory": "Home assistant is an open source home automation. The assessment verified that webhooks available in the webhook component are triggerable via the `*.ui.nabu.casa` URL without authentication, even when the webhook is marked as Only accessible from the local network. This issue is facilitated by the SniTun proxy, which sets the source address to 127.0.0.1 on all requests sent to the public URL and forwarded to the local Home Assistant. This issue has been addressed in version 2023.9.0 and all users are advised to upgrade. There are no known workarounds for this vulnerability.", + "cve": "CVE-2023-41894", + "id": "pyup.io-70403", + "more_info_path": "/vulnerabilities/CVE-2023-41894/70403", "specs": [ "<2023.9.0" ], @@ -54114,10 +54482,10 @@ "v": "<2023.9.0" }, { - "advisory": "Home assistant is an open source home automation. The assessment verified that webhooks available in the webhook component are triggerable via the `*.ui.nabu.casa` URL without authentication, even when the webhook is marked as Only accessible from the local network. This issue is facilitated by the SniTun proxy, which sets the source address to 127.0.0.1 on all requests sent to the public URL and forwarded to the local Home Assistant. This issue has been addressed in version 2023.9.0 and all users are advised to upgrade. There are no known workarounds for this vulnerability.", - "cve": "CVE-2023-41894", - "id": "pyup.io-70403", - "more_info_path": "/vulnerabilities/CVE-2023-41894/70403", + "advisory": "Home assistant is an open source home automation. The Home Assistant login page allows users to use their local Home Assistant credentials and log in to another website that specifies the `redirect_uri` and `client_id` parameters. Although the `redirect_uri` validation typically ensures that it matches the `client_id` and the scheme represents either `http` or `https`, Home Assistant will fetch the `client_id` and check for `` HTML tags on the page. These URLs are not subjected to the same scheme validation and thus allow for arbitrary JavaScript execution on the Home Assistant administration page via usage of `javascript:` scheme URIs. This Cross-site Scripting (XSS) vulnerability can be executed on the Home Assistant frontend domain, which may be used for a full takeover of the Home Assistant account and installation. This issue has been addressed in version 2023.9.0 and all users are advised to upgrade. There are no known workarounds for this vulnerability.", + "cve": "CVE-2023-41895", + "id": "pyup.io-70402", + "more_info_path": "/vulnerabilities/CVE-2023-41895/70402", "specs": [ "<2023.9.0" ], @@ -54133,6 +54501,16 @@ ], "v": "<2023.9.0" }, + { + "advisory": "Homeassistant 2023.9.0 includes a fix for CVE-2023-41899: In affected versions the 'hassio.addon_stdin' is vulnerable to a partial Server-Side Request Forgery where an attacker capable of calling this service (e.g.: through GHSA-h2jp-7grc-9xpp) may be able to invoke any Supervisor REST API endpoints with a POST request. An attacker able to exploit will be able to control the data dictionary, including its addon and input key/values.\r\nhttps://github.com/home-assistant/core/pull/99232", + "cve": "CVE-2023-41899", + "id": "pyup.io-63183", + "more_info_path": "/vulnerabilities/CVE-2023-41899/63183", + "specs": [ + "<2023.9.0" + ], + "v": "<2023.9.0" + }, { "advisory": "Homeassistant 2023.9.2 includes a fix for CVE-2023-41898: The Home Assistant Companion for Android app up to version 2023.8.2 is vulnerable to arbitrary URL loading in a WebView. This enables all sorts of attacks, including arbitrary JavaScript execution, limited native code execution, and credential theft.\r\nhttps://github.blog/2023-11-30-securing-our-home-labs-home-assistant-code-review", "cve": "CVE-2023-41898", @@ -54241,6 +54619,16 @@ } ], "honeycomb-beeline": [ + { + "advisory": "Honeycomb-beeline 3.0.0 updates its dependency 'Django' minimum version to v2.2.26 to include security fixes.", + "cve": "CVE-2021-33203", + "id": "pyup.io-44540", + "more_info_path": "/vulnerabilities/CVE-2021-33203/44540", + "specs": [ + "<3.0.0" + ], + "v": "<3.0.0" + }, { "advisory": "Honeycomb-beeline 3.0.0 updates its dependency 'Django' minimum version to v2.2.26 to include security fixes.", "cve": "CVE-2021-33571", @@ -54262,20 +54650,30 @@ "v": "<3.0.0" }, { - "advisory": "Honeycomb-beeline 3.0.0 updates its dependency 'jinja2' to v2.11.3 to include a security fix.", - "cve": "CVE-2020-28493", - "id": "pyup.io-44537", - "more_info_path": "/vulnerabilities/CVE-2020-28493/44537", + "advisory": "Honeycomb-beeline 3.0.0 updates its dependency 'Django' minimum version to v2.2.26 to include security fixes.", + "cve": "CVE-2021-28658", + "id": "pyup.io-44541", + "more_info_path": "/vulnerabilities/CVE-2021-28658/44541", "specs": [ "<3.0.0" ], "v": "<3.0.0" }, { - "advisory": "Honeycomb-beeline 3.0.0 updates its dependency 'Django' minimum version to v2.2.26 to include security fixes.", - "cve": "CVE-2021-28658", - "id": "pyup.io-44541", - "more_info_path": "/vulnerabilities/CVE-2021-28658/44541", + "advisory": "Honeycomb-beeline 3.0.0 drops support for Python 2.7, version that doesn't receive security patches anymore since 2020.", + "cve": "PVE-2021-42379", + "id": "pyup.io-44547", + "more_info_path": "/vulnerabilities/PVE-2021-42379/44547", + "specs": [ + "<3.0.0" + ], + "v": "<3.0.0" + }, + { + "advisory": "Honeycomb-beeline 3.0.0 drops support for Python 2.7, version that doesn't receive security patches anymore since 2020.", + "cve": "CVE-2013-7040", + "id": "pyup.io-44551", + "more_info_path": "/vulnerabilities/CVE-2013-7040/44551", "specs": [ "<3.0.0" ], @@ -54293,9 +54691,9 @@ }, { "advisory": "Honeycomb-beeline 3.0.0 updates its dependency 'Django' minimum version to v2.2.26 to include security fixes.", - "cve": "CVE-2021-33203", - "id": "pyup.io-44540", - "more_info_path": "/vulnerabilities/CVE-2021-33203/44540", + "cve": "CVE-2021-31542", + "id": "pyup.io-44546", + "more_info_path": "/vulnerabilities/CVE-2021-31542/44546", "specs": [ "<3.0.0" ], @@ -54312,30 +54710,20 @@ "v": "<3.0.0" }, { - "advisory": "Honeycomb-beeline 3.0.0 drops support for Python 2.7, version that doesn't receive security patches anymore since 2020.", - "cve": "PVE-2021-42379", - "id": "pyup.io-44547", - "more_info_path": "/vulnerabilities/PVE-2021-42379/44547", - "specs": [ - "<3.0.0" - ], - "v": "<3.0.0" - }, - { - "advisory": "Honeycomb-beeline 3.0.0 drops support for Python 2.7, version that doesn't receive security patches anymore since 2020.", - "cve": "CVE-2013-7040", - "id": "pyup.io-44551", - "more_info_path": "/vulnerabilities/CVE-2013-7040/44551", + "advisory": "Honeycomb-beeline 3.0.0 updates its dependency 'Django' minimum version to v2.2.26 to include security fixes.", + "cve": "CVE-2021-3281", + "id": "pyup.io-44543", + "more_info_path": "/vulnerabilities/CVE-2021-3281/44543", "specs": [ "<3.0.0" ], "v": "<3.0.0" }, { - "advisory": "Honeycomb-beeline 3.0.0 updates its dependency 'Django' minimum version to v2.2.26 to include security fixes.", - "cve": "CVE-2021-31542", - "id": "pyup.io-44546", - "more_info_path": "/vulnerabilities/CVE-2021-31542/44546", + "advisory": "Honeycomb-beeline 3.0.0 updates its dependency 'jinja2' to v2.11.3 to include a security fix.", + "cve": "CVE-2020-28493", + "id": "pyup.io-44537", + "more_info_path": "/vulnerabilities/CVE-2020-28493/44537", "specs": [ "<3.0.0" ], @@ -54361,16 +54749,6 @@ ], "v": "<3.0.0" }, - { - "advisory": "Honeycomb-beeline 3.0.0 updates its dependency 'Django' minimum version to v2.2.26 to include security fixes.", - "cve": "CVE-2021-3281", - "id": "pyup.io-44543", - "more_info_path": "/vulnerabilities/CVE-2021-3281/44543", - "specs": [ - "<3.0.0" - ], - "v": "<3.0.0" - }, { "advisory": "Honeycomb-beeline 3.0.0 updates its dependency 'Django' minimum version to v2.2.26 to include security fixes.", "cve": "CVE-2021-44420", @@ -54658,16 +55036,6 @@ ], "v": "<3.2.4" }, - { - "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15205", - "id": "pyup.io-43830", - "more_info_path": "/vulnerabilities/CVE-2020-15205/43830", - "specs": [ - "<3.2.4" - ], - "v": "<3.2.4" - }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", "cve": "CVE-2020-11656", @@ -54698,6 +55066,16 @@ ], "v": "<3.2.4" }, + { + "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", + "cve": "CVE-2020-15202", + "id": "pyup.io-43831", + "more_info_path": "/vulnerabilities/CVE-2020-15202/43831", + "specs": [ + "<3.2.4" + ], + "v": "<3.2.4" + }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", "cve": "CVE-2020-9327", @@ -54710,9 +55088,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15209", - "id": "pyup.io-43834", - "more_info_path": "/vulnerabilities/CVE-2020-15209/43834", + "cve": "CVE-2020-15210", + "id": "pyup.io-43855", + "more_info_path": "/vulnerabilities/CVE-2020-15210/43855", "specs": [ "<3.2.4" ], @@ -54720,9 +55098,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15206", - "id": "pyup.io-43832", - "more_info_path": "/vulnerabilities/CVE-2020-15206/43832", + "cve": "CVE-2020-15203", + "id": "pyup.io-43838", + "more_info_path": "/vulnerabilities/CVE-2020-15203/43838", "specs": [ "<3.2.4" ], @@ -54730,9 +55108,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15208", - "id": "pyup.io-43833", - "more_info_path": "/vulnerabilities/CVE-2020-15208/43833", + "cve": "CVE-2020-15191", + "id": "pyup.io-43844", + "more_info_path": "/vulnerabilities/CVE-2020-15191/43844", "specs": [ "<3.2.4" ], @@ -54740,9 +55118,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15213", - "id": "pyup.io-43854", - "more_info_path": "/vulnerabilities/CVE-2020-15213/43854", + "cve": "CVE-2020-15190", + "id": "pyup.io-43837", + "more_info_path": "/vulnerabilities/CVE-2020-15190/43837", "specs": [ "<3.2.4" ], @@ -54750,9 +55128,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15210", - "id": "pyup.io-43855", - "more_info_path": "/vulnerabilities/CVE-2020-15210/43855", + "cve": "CVE-2020-13434", + "id": "pyup.io-43847", + "more_info_path": "/vulnerabilities/CVE-2020-13434/43847", "specs": [ "<3.2.4" ], @@ -54760,9 +55138,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15211", - "id": "pyup.io-38822", - "more_info_path": "/vulnerabilities/CVE-2020-15211/38822", + "cve": "CVE-2020-13631", + "id": "pyup.io-43850", + "more_info_path": "/vulnerabilities/CVE-2020-13631/43850", "specs": [ "<3.2.4" ], @@ -54770,9 +55148,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15203", - "id": "pyup.io-43838", - "more_info_path": "/vulnerabilities/CVE-2020-15203/43838", + "cve": "CVE-2020-15206", + "id": "pyup.io-43832", + "more_info_path": "/vulnerabilities/CVE-2020-15206/43832", "specs": [ "<3.2.4" ], @@ -54780,9 +55158,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15191", - "id": "pyup.io-43844", - "more_info_path": "/vulnerabilities/CVE-2020-15191/43844", + "cve": "CVE-2020-15358", + "id": "pyup.io-43843", + "more_info_path": "/vulnerabilities/CVE-2020-15358/43843", "specs": [ "<3.2.4" ], @@ -54790,9 +55168,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15193", - "id": "pyup.io-43853", - "more_info_path": "/vulnerabilities/CVE-2020-15193/43853", + "cve": "CVE-2020-11655", + "id": "pyup.io-43846", + "more_info_path": "/vulnerabilities/CVE-2020-11655/43846", "specs": [ "<3.2.4" ], @@ -54800,9 +55178,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15204", - "id": "pyup.io-43829", - "more_info_path": "/vulnerabilities/CVE-2020-15204/43829", + "cve": "CVE-2020-15211", + "id": "pyup.io-38822", + "more_info_path": "/vulnerabilities/CVE-2020-15211/38822", "specs": [ "<3.2.4" ], @@ -54810,9 +55188,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15202", - "id": "pyup.io-43831", - "more_info_path": "/vulnerabilities/CVE-2020-15202/43831", + "cve": "CVE-2020-13871", + "id": "pyup.io-43849", + "more_info_path": "/vulnerabilities/CVE-2020-13871/43849", "specs": [ "<3.2.4" ], @@ -54820,9 +55198,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15190", - "id": "pyup.io-43837", - "more_info_path": "/vulnerabilities/CVE-2020-15190/43837", + "cve": "CVE-2020-15193", + "id": "pyup.io-43853", + "more_info_path": "/vulnerabilities/CVE-2020-15193/43853", "specs": [ "<3.2.4" ], @@ -54830,9 +55208,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15212", - "id": "pyup.io-43839", - "more_info_path": "/vulnerabilities/CVE-2020-15212/43839", + "cve": "CVE-2020-15214", + "id": "pyup.io-43842", + "more_info_path": "/vulnerabilities/CVE-2020-15214/43842", "specs": [ "<3.2.4" ], @@ -54840,9 +55218,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15194", - "id": "pyup.io-43841", - "more_info_path": "/vulnerabilities/CVE-2020-15194/43841", + "cve": "CVE-2020-15213", + "id": "pyup.io-43854", + "more_info_path": "/vulnerabilities/CVE-2020-15213/43854", "specs": [ "<3.2.4" ], @@ -54850,9 +55228,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15214", - "id": "pyup.io-43842", - "more_info_path": "/vulnerabilities/CVE-2020-15214/43842", + "cve": "CVE-2020-15212", + "id": "pyup.io-43839", + "more_info_path": "/vulnerabilities/CVE-2020-15212/43839", "specs": [ "<3.2.4" ], @@ -54860,9 +55238,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-13434", - "id": "pyup.io-43847", - "more_info_path": "/vulnerabilities/CVE-2020-13434/43847", + "cve": "CVE-2020-15208", + "id": "pyup.io-43833", + "more_info_path": "/vulnerabilities/CVE-2020-15208/43833", "specs": [ "<3.2.4" ], @@ -54870,9 +55248,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-13631", - "id": "pyup.io-43850", - "more_info_path": "/vulnerabilities/CVE-2020-13631/43850", + "cve": "CVE-2020-13630", + "id": "pyup.io-43840", + "more_info_path": "/vulnerabilities/CVE-2020-13630/43840", "specs": [ "<3.2.4" ], @@ -54880,9 +55258,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-15358", - "id": "pyup.io-43843", - "more_info_path": "/vulnerabilities/CVE-2020-15358/43843", + "cve": "CVE-2020-13435", + "id": "pyup.io-43848", + "more_info_path": "/vulnerabilities/CVE-2020-13435/43848", "specs": [ "<3.2.4" ], @@ -54890,9 +55268,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-11655", - "id": "pyup.io-43846", - "more_info_path": "/vulnerabilities/CVE-2020-11655/43846", + "cve": "CVE-2020-15194", + "id": "pyup.io-43841", + "more_info_path": "/vulnerabilities/CVE-2020-15194/43841", "specs": [ "<3.2.4" ], @@ -54900,9 +55278,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-13871", - "id": "pyup.io-43849", - "more_info_path": "/vulnerabilities/CVE-2020-13871/43849", + "cve": "CVE-2020-15204", + "id": "pyup.io-43829", + "more_info_path": "/vulnerabilities/CVE-2020-15204/43829", "specs": [ "<3.2.4" ], @@ -54910,9 +55288,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-13630", - "id": "pyup.io-43840", - "more_info_path": "/vulnerabilities/CVE-2020-13630/43840", + "cve": "CVE-2020-15205", + "id": "pyup.io-43830", + "more_info_path": "/vulnerabilities/CVE-2020-15205/43830", "specs": [ "<3.2.4" ], @@ -54920,9 +55298,9 @@ }, { "advisory": "Hotaru 3.2.4 updates Tensorflow to >= 2.2.1 to include security fixes.", - "cve": "CVE-2020-13435", - "id": "pyup.io-43848", - "more_info_path": "/vulnerabilities/CVE-2020-13435/43848", + "cve": "CVE-2020-15209", + "id": "pyup.io-43834", + "more_info_path": "/vulnerabilities/CVE-2020-15209/43834", "specs": [ "<3.2.4" ], @@ -54930,9 +55308,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41228", - "id": "pyup.io-46016", - "more_info_path": "/vulnerabilities/CVE-2021-41228/46016", + "cve": "CVE-2021-22924", + "id": "pyup.io-45872", + "more_info_path": "/vulnerabilities/CVE-2021-22924/45872", "specs": [ "<3.4.0" ], @@ -54940,9 +55318,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-26267", - "id": "pyup.io-45860", - "more_info_path": "/vulnerabilities/CVE-2020-26267/45860", + "cve": "CVE-2021-29512", + "id": "pyup.io-45875", + "more_info_path": "/vulnerabilities/CVE-2021-29512/45875", "specs": [ "<3.4.0" ], @@ -54950,9 +55328,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29517", - "id": "pyup.io-45880", - "more_info_path": "/vulnerabilities/CVE-2021-29517/45880", + "cve": "CVE-2021-29588", + "id": "pyup.io-45951", + "more_info_path": "/vulnerabilities/CVE-2021-29588/45951", "specs": [ "<3.4.0" ], @@ -54960,9 +55338,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-15266", - "id": "pyup.io-45858", - "more_info_path": "/vulnerabilities/CVE-2020-15266/45858", + "cve": "CVE-2021-29581", + "id": "pyup.io-45944", + "more_info_path": "/vulnerabilities/CVE-2021-29581/45944", "specs": [ "<3.4.0" ], @@ -54970,9 +55348,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29513", - "id": "pyup.io-45876", - "more_info_path": "/vulnerabilities/CVE-2021-29513/45876", + "cve": "CVE-2020-26267", + "id": "pyup.io-45860", + "more_info_path": "/vulnerabilities/CVE-2020-26267/45860", "specs": [ "<3.4.0" ], @@ -54980,9 +55358,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29518", - "id": "pyup.io-45881", - "more_info_path": "/vulnerabilities/CVE-2021-29518/45881", + "cve": "CVE-2021-29612", + "id": "pyup.io-45975", + "more_info_path": "/vulnerabilities/CVE-2021-29612/45975", "specs": [ "<3.4.0" ], @@ -54990,9 +55368,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29534", - "id": "pyup.io-45897", - "more_info_path": "/vulnerabilities/CVE-2021-29534/45897", + "cve": "CVE-2021-29561", + "id": "pyup.io-45924", + "more_info_path": "/vulnerabilities/CVE-2021-29561/45924", "specs": [ "<3.4.0" ], @@ -55000,9 +55378,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29537", - "id": "pyup.io-45900", - "more_info_path": "/vulnerabilities/CVE-2021-29537/45900", + "cve": "CVE-2021-29520", + "id": "pyup.io-45883", + "more_info_path": "/vulnerabilities/CVE-2021-29520/45883", "specs": [ "<3.4.0" ], @@ -55010,9 +55388,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29539", - "id": "pyup.io-45902", - "more_info_path": "/vulnerabilities/CVE-2021-29539/45902", + "cve": "CVE-2020-13790", + "id": "pyup.io-45854", + "more_info_path": "/vulnerabilities/CVE-2020-13790/45854", "specs": [ "<3.4.0" ], @@ -55020,9 +55398,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29542", - "id": "pyup.io-45905", - "more_info_path": "/vulnerabilities/CVE-2021-29542/45905", + "cve": "CVE-2021-29532", + "id": "pyup.io-45895", + "more_info_path": "/vulnerabilities/CVE-2021-29532/45895", "specs": [ "<3.4.0" ], @@ -55030,9 +55408,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29550", - "id": "pyup.io-45913", - "more_info_path": "/vulnerabilities/CVE-2021-29550/45913", + "cve": "CVE-2021-29574", + "id": "pyup.io-45937", + "more_info_path": "/vulnerabilities/CVE-2021-29574/45937", "specs": [ "<3.4.0" ], @@ -55040,9 +55418,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29553", - "id": "pyup.io-45916", - "more_info_path": "/vulnerabilities/CVE-2021-29553/45916", + "cve": "CVE-2021-29550", + "id": "pyup.io-45913", + "more_info_path": "/vulnerabilities/CVE-2021-29550/45913", "specs": [ "<3.4.0" ], @@ -55050,9 +55428,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29557", - "id": "pyup.io-45920", - "more_info_path": "/vulnerabilities/CVE-2021-29557/45920", + "cve": "CVE-2021-29577", + "id": "pyup.io-45940", + "more_info_path": "/vulnerabilities/CVE-2021-29577/45940", "specs": [ "<3.4.0" ], @@ -55060,9 +55438,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29560", - "id": "pyup.io-45923", - "more_info_path": "/vulnerabilities/CVE-2021-29560/45923", + "cve": "CVE-2021-29585", + "id": "pyup.io-45948", + "more_info_path": "/vulnerabilities/CVE-2021-29585/45948", "specs": [ "<3.4.0" ], @@ -55070,9 +55448,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29561", - "id": "pyup.io-45924", - "more_info_path": "/vulnerabilities/CVE-2021-29561/45924", + "cve": "CVE-2021-29604", + "id": "pyup.io-45967", + "more_info_path": "/vulnerabilities/CVE-2021-29604/45967", "specs": [ "<3.4.0" ], @@ -55080,9 +55458,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29567", - "id": "pyup.io-45930", - "more_info_path": "/vulnerabilities/CVE-2021-29567/45930", + "cve": "CVE-2021-29614", + "id": "pyup.io-45977", + "more_info_path": "/vulnerabilities/CVE-2021-29614/45977", "specs": [ "<3.4.0" ], @@ -55090,9 +55468,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29589", - "id": "pyup.io-45952", - "more_info_path": "/vulnerabilities/CVE-2021-29589/45952", + "cve": "CVE-2021-29605", + "id": "pyup.io-45968", + "more_info_path": "/vulnerabilities/CVE-2021-29605/45968", "specs": [ "<3.4.0" ], @@ -55100,9 +55478,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29596", - "id": "pyup.io-45959", - "more_info_path": "/vulnerabilities/CVE-2021-29596/45959", + "cve": "CVE-2021-29609", + "id": "pyup.io-45972", + "more_info_path": "/vulnerabilities/CVE-2021-29609/45972", "specs": [ "<3.4.0" ], @@ -55110,9 +55488,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29612", - "id": "pyup.io-45975", - "more_info_path": "/vulnerabilities/CVE-2021-29612/45975", + "cve": "CVE-2021-29617", + "id": "pyup.io-45980", + "more_info_path": "/vulnerabilities/CVE-2021-29617/45980", "specs": [ "<3.4.0" ], @@ -55120,9 +55498,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29608", - "id": "pyup.io-45971", - "more_info_path": "/vulnerabilities/CVE-2021-29608/45971", + "cve": "CVE-2021-41211", + "id": "pyup.io-45999", + "more_info_path": "/vulnerabilities/CVE-2021-41211/45999", "specs": [ "<3.4.0" ], @@ -55130,9 +55508,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29615", - "id": "pyup.io-45978", - "more_info_path": "/vulnerabilities/CVE-2021-29615/45978", + "cve": "CVE-2021-41210", + "id": "pyup.io-45998", + "more_info_path": "/vulnerabilities/CVE-2021-41210/45998", "specs": [ "<3.4.0" ], @@ -55140,9 +55518,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41213", - "id": "pyup.io-46001", - "more_info_path": "/vulnerabilities/CVE-2021-41213/46001", + "cve": "CVE-2021-29527", + "id": "pyup.io-45890", + "more_info_path": "/vulnerabilities/CVE-2021-29527/45890", "specs": [ "<3.4.0" ], @@ -55150,9 +55528,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41216", - "id": "pyup.io-46004", - "more_info_path": "/vulnerabilities/CVE-2021-41216/46004", + "cve": "CVE-2021-29538", + "id": "pyup.io-45901", + "more_info_path": "/vulnerabilities/CVE-2021-29538/45901", "specs": [ "<3.4.0" ], @@ -55160,9 +55538,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29520", - "id": "pyup.io-45883", - "more_info_path": "/vulnerabilities/CVE-2021-29520/45883", + "cve": "CVE-2021-29555", + "id": "pyup.io-45918", + "more_info_path": "/vulnerabilities/CVE-2021-29555/45918", "specs": [ "<3.4.0" ], @@ -55170,9 +55548,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29571", - "id": "pyup.io-45934", - "more_info_path": "/vulnerabilities/CVE-2021-29571/45934", + "cve": "CVE-2021-29521", + "id": "pyup.io-45884", + "more_info_path": "/vulnerabilities/CVE-2021-29521/45884", "specs": [ "<3.4.0" ], @@ -55180,9 +55558,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29573", - "id": "pyup.io-45936", - "more_info_path": "/vulnerabilities/CVE-2021-29573/45936", + "cve": "CVE-2021-29615", + "id": "pyup.io-45978", + "more_info_path": "/vulnerabilities/CVE-2021-29615/45978", "specs": [ "<3.4.0" ], @@ -55190,9 +55568,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-8231", - "id": "pyup.io-45866", - "more_info_path": "/vulnerabilities/CVE-2020-8231/45866", + "cve": "CVE-2021-29592", + "id": "pyup.io-45955", + "more_info_path": "/vulnerabilities/CVE-2021-29592/45955", "specs": [ "<3.4.0" ], @@ -55210,9 +55588,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29562", - "id": "pyup.io-45925", - "more_info_path": "/vulnerabilities/CVE-2021-29562/45925", + "cve": "CVE-2021-29613", + "id": "pyup.io-45976", + "more_info_path": "/vulnerabilities/CVE-2021-29613/45976", "specs": [ "<3.4.0" ], @@ -55220,9 +55598,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29607", - "id": "pyup.io-45970", - "more_info_path": "/vulnerabilities/CVE-2021-29607/45970", + "cve": "CVE-2021-29543", + "id": "pyup.io-45906", + "more_info_path": "/vulnerabilities/CVE-2021-29543/45906", "specs": [ "<3.4.0" ], @@ -55230,9 +55608,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29530", - "id": "pyup.io-45893", - "more_info_path": "/vulnerabilities/CVE-2021-29530/45893", + "cve": "CVE-2021-41224", + "id": "pyup.io-46012", + "more_info_path": "/vulnerabilities/CVE-2021-41224/46012", "specs": [ "<3.4.0" ], @@ -55240,9 +55618,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29581", - "id": "pyup.io-45944", - "more_info_path": "/vulnerabilities/CVE-2021-29581/45944", + "cve": "CVE-2021-29570", + "id": "pyup.io-45933", + "more_info_path": "/vulnerabilities/CVE-2021-29570/45933", "specs": [ "<3.4.0" ], @@ -55250,9 +55628,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-15265", - "id": "pyup.io-45857", - "more_info_path": "/vulnerabilities/CVE-2020-15265/45857", + "cve": "CVE-2021-29569", + "id": "pyup.io-45932", + "more_info_path": "/vulnerabilities/CVE-2021-29569/45932", "specs": [ "<3.4.0" ], @@ -55260,9 +55638,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29602", - "id": "pyup.io-45965", - "more_info_path": "/vulnerabilities/CVE-2021-29602/45965", + "cve": "CVE-2021-29611", + "id": "pyup.io-45974", + "more_info_path": "/vulnerabilities/CVE-2021-29611/45974", "specs": [ "<3.4.0" ], @@ -55270,9 +55648,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29618", - "id": "pyup.io-45981", - "more_info_path": "/vulnerabilities/CVE-2021-29618/45981", + "cve": "CVE-2021-29562", + "id": "pyup.io-45925", + "more_info_path": "/vulnerabilities/CVE-2021-29562/45925", "specs": [ "<3.4.0" ], @@ -55280,9 +55658,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29514", - "id": "pyup.io-45877", - "more_info_path": "/vulnerabilities/CVE-2021-29514/45877", + "cve": "CVE-2021-29608", + "id": "pyup.io-45971", + "more_info_path": "/vulnerabilities/CVE-2021-29608/45971", "specs": [ "<3.4.0" ], @@ -55290,9 +55668,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29515", - "id": "pyup.io-45878", - "more_info_path": "/vulnerabilities/CVE-2021-29515/45878", + "cve": "CVE-2021-29528", + "id": "pyup.io-45891", + "more_info_path": "/vulnerabilities/CVE-2021-29528/45891", "specs": [ "<3.4.0" ], @@ -55300,9 +55678,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29523", - "id": "pyup.io-45886", - "more_info_path": "/vulnerabilities/CVE-2021-29523/45886", + "cve": "CVE-2021-29558", + "id": "pyup.io-45921", + "more_info_path": "/vulnerabilities/CVE-2021-29558/45921", "specs": [ "<3.4.0" ], @@ -55310,9 +55688,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29519", - "id": "pyup.io-45882", - "more_info_path": "/vulnerabilities/CVE-2021-29519/45882", + "cve": "CVE-2021-29563", + "id": "pyup.io-45926", + "more_info_path": "/vulnerabilities/CVE-2021-29563/45926", "specs": [ "<3.4.0" ], @@ -55320,9 +55698,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29533", - "id": "pyup.io-45896", - "more_info_path": "/vulnerabilities/CVE-2021-29533/45896", + "cve": "CVE-2021-29552", + "id": "pyup.io-45915", + "more_info_path": "/vulnerabilities/CVE-2021-29552/45915", "specs": [ "<3.4.0" ], @@ -55330,9 +55708,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29546", - "id": "pyup.io-45909", - "more_info_path": "/vulnerabilities/CVE-2021-29546/45909", + "cve": "CVE-2021-41228", + "id": "pyup.io-46016", + "more_info_path": "/vulnerabilities/CVE-2021-41228/46016", "specs": [ "<3.4.0" ], @@ -55340,9 +55718,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29565", - "id": "pyup.io-45928", - "more_info_path": "/vulnerabilities/CVE-2021-29565/45928", + "cve": "CVE-2021-29589", + "id": "pyup.io-45952", + "more_info_path": "/vulnerabilities/CVE-2021-29589/45952", "specs": [ "<3.4.0" ], @@ -55350,9 +55728,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29568", - "id": "pyup.io-45931", - "more_info_path": "/vulnerabilities/CVE-2021-29568/45931", + "cve": "CVE-2021-29579", + "id": "pyup.io-45942", + "more_info_path": "/vulnerabilities/CVE-2021-29579/45942", "specs": [ "<3.4.0" ], @@ -55360,9 +55738,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29575", - "id": "pyup.io-45938", - "more_info_path": "/vulnerabilities/CVE-2021-29575/45938", + "cve": "CVE-2020-26266", + "id": "pyup.io-45859", + "more_info_path": "/vulnerabilities/CVE-2020-26266/45859", "specs": [ "<3.4.0" ], @@ -55370,9 +55748,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29582", - "id": "pyup.io-45945", - "more_info_path": "/vulnerabilities/CVE-2021-29582/45945", + "cve": "CVE-2021-29610", + "id": "pyup.io-45973", + "more_info_path": "/vulnerabilities/CVE-2021-29610/45973", "specs": [ "<3.4.0" ], @@ -55380,9 +55758,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29590", - "id": "pyup.io-45953", - "more_info_path": "/vulnerabilities/CVE-2021-29590/45953", + "cve": "CVE-2021-41205", + "id": "pyup.io-45993", + "more_info_path": "/vulnerabilities/CVE-2021-41205/45993", "specs": [ "<3.4.0" ], @@ -55390,9 +55768,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29591", - "id": "pyup.io-45954", - "more_info_path": "/vulnerabilities/CVE-2021-29591/45954", + "cve": "CVE-2021-29540", + "id": "pyup.io-45903", + "more_info_path": "/vulnerabilities/CVE-2021-29540/45903", "specs": [ "<3.4.0" ], @@ -55400,9 +55778,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29583", - "id": "pyup.io-45946", - "more_info_path": "/vulnerabilities/CVE-2021-29583/45946", + "cve": "CVE-2021-29566", + "id": "pyup.io-45929", + "more_info_path": "/vulnerabilities/CVE-2021-29566/45929", "specs": [ "<3.4.0" ], @@ -55410,9 +55788,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29614", - "id": "pyup.io-45977", - "more_info_path": "/vulnerabilities/CVE-2021-29614/45977", + "cve": "CVE-2021-29602", + "id": "pyup.io-45965", + "more_info_path": "/vulnerabilities/CVE-2021-29602/45965", "specs": [ "<3.4.0" ], @@ -55420,9 +55798,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41215", - "id": "pyup.io-46003", - "more_info_path": "/vulnerabilities/CVE-2021-41215/46003", + "cve": "CVE-2021-29601", + "id": "pyup.io-45964", + "more_info_path": "/vulnerabilities/CVE-2021-29601/45964", "specs": [ "<3.4.0" ], @@ -55430,9 +55808,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41218", - "id": "pyup.io-46006", - "more_info_path": "/vulnerabilities/CVE-2021-41218/46006", + "cve": "CVE-2021-22925", + "id": "pyup.io-45873", + "more_info_path": "/vulnerabilities/CVE-2021-22925/45873", "specs": [ "<3.4.0" ], @@ -55440,9 +55818,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29528", - "id": "pyup.io-45891", - "more_info_path": "/vulnerabilities/CVE-2021-29528/45891", + "cve": "CVE-2021-29606", + "id": "pyup.io-45969", + "more_info_path": "/vulnerabilities/CVE-2021-29606/45969", "specs": [ "<3.4.0" ], @@ -55450,9 +55828,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29532", - "id": "pyup.io-45895", - "more_info_path": "/vulnerabilities/CVE-2021-29532/45895", + "cve": "CVE-2021-29616", + "id": "pyup.io-45979", + "more_info_path": "/vulnerabilities/CVE-2021-29616/45979", "specs": [ "<3.4.0" ], @@ -55460,9 +55838,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29558", - "id": "pyup.io-45921", - "more_info_path": "/vulnerabilities/CVE-2021-29558/45921", + "cve": "CVE-2021-29619", + "id": "pyup.io-45982", + "more_info_path": "/vulnerabilities/CVE-2021-29619/45982", "specs": [ "<3.4.0" ], @@ -55470,9 +55848,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41221", - "id": "pyup.io-46009", - "more_info_path": "/vulnerabilities/CVE-2021-41221/46009", + "cve": "CVE-2021-41200", + "id": "pyup.io-45988", + "more_info_path": "/vulnerabilities/CVE-2021-41200/45988", "specs": [ "<3.4.0" ], @@ -55480,9 +55858,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-26266", - "id": "pyup.io-45859", - "more_info_path": "/vulnerabilities/CVE-2020-26266/45859", + "cve": "CVE-2021-41209", + "id": "pyup.io-45997", + "more_info_path": "/vulnerabilities/CVE-2021-41209/45997", "specs": [ "<3.4.0" ], @@ -55490,9 +55868,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-26270", - "id": "pyup.io-45862", - "more_info_path": "/vulnerabilities/CVE-2020-26270/45862", + "cve": "CVE-2021-41221", + "id": "pyup.io-46009", + "more_info_path": "/vulnerabilities/CVE-2021-41221/46009", "specs": [ "<3.4.0" ], @@ -55500,9 +55878,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-26271", - "id": "pyup.io-45863", - "more_info_path": "/vulnerabilities/CVE-2020-26271/45863", + "cve": "CVE-2021-29599", + "id": "pyup.io-45962", + "more_info_path": "/vulnerabilities/CVE-2021-29599/45962", "specs": [ "<3.4.0" ], @@ -55510,9 +55888,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29541", - "id": "pyup.io-45904", - "more_info_path": "/vulnerabilities/CVE-2021-29541/45904", + "cve": "CVE-2021-41214", + "id": "pyup.io-46002", + "more_info_path": "/vulnerabilities/CVE-2021-41214/46002", "specs": [ "<3.4.0" ], @@ -55520,9 +55898,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29522", - "id": "pyup.io-45885", - "more_info_path": "/vulnerabilities/CVE-2021-29522/45885", + "cve": "CVE-2020-15266", + "id": "pyup.io-45858", + "more_info_path": "/vulnerabilities/CVE-2020-15266/45858", "specs": [ "<3.4.0" ], @@ -55530,9 +55908,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29524", - "id": "pyup.io-45887", - "more_info_path": "/vulnerabilities/CVE-2021-29524/45887", + "cve": "CVE-2021-29542", + "id": "pyup.io-45905", + "more_info_path": "/vulnerabilities/CVE-2021-29542/45905", "specs": [ "<3.4.0" ], @@ -55540,9 +55918,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29525", - "id": "pyup.io-45888", - "more_info_path": "/vulnerabilities/CVE-2021-29525/45888", + "cve": "CVE-2021-29537", + "id": "pyup.io-45900", + "more_info_path": "/vulnerabilities/CVE-2021-29537/45900", "specs": [ "<3.4.0" ], @@ -55550,9 +55928,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29526", - "id": "pyup.io-45889", - "more_info_path": "/vulnerabilities/CVE-2021-29526/45889", + "cve": "CVE-2021-29557", + "id": "pyup.io-45920", + "more_info_path": "/vulnerabilities/CVE-2021-29557/45920", "specs": [ "<3.4.0" ], @@ -55560,9 +55938,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29529", - "id": "pyup.io-45892", - "more_info_path": "/vulnerabilities/CVE-2021-29529/45892", + "cve": "CVE-2021-29596", + "id": "pyup.io-45959", + "more_info_path": "/vulnerabilities/CVE-2021-29596/45959", "specs": [ "<3.4.0" ], @@ -55570,9 +55948,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29536", - "id": "pyup.io-45899", - "more_info_path": "/vulnerabilities/CVE-2021-29536/45899", + "cve": "CVE-2021-41213", + "id": "pyup.io-46001", + "more_info_path": "/vulnerabilities/CVE-2021-41213/46001", "specs": [ "<3.4.0" ], @@ -55580,9 +55958,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29540", - "id": "pyup.io-45903", - "more_info_path": "/vulnerabilities/CVE-2021-29540/45903", + "cve": "CVE-2021-29600", + "id": "pyup.io-45963", + "more_info_path": "/vulnerabilities/CVE-2021-29600/45963", "specs": [ "<3.4.0" ], @@ -55590,9 +55968,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29545", - "id": "pyup.io-45908", - "more_info_path": "/vulnerabilities/CVE-2021-29545/45908", + "cve": "CVE-2021-29603", + "id": "pyup.io-45966", + "more_info_path": "/vulnerabilities/CVE-2021-29603/45966", "specs": [ "<3.4.0" ], @@ -55600,9 +55978,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29548", - "id": "pyup.io-45911", - "more_info_path": "/vulnerabilities/CVE-2021-29548/45911", + "cve": "CVE-2021-29573", + "id": "pyup.io-45936", + "more_info_path": "/vulnerabilities/CVE-2021-29573/45936", "specs": [ "<3.4.0" ], @@ -55610,9 +55988,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29549", - "id": "pyup.io-45912", - "more_info_path": "/vulnerabilities/CVE-2021-29549/45912", + "cve": "CVE-2021-29586", + "id": "pyup.io-45949", + "more_info_path": "/vulnerabilities/CVE-2021-29586/45949", "specs": [ "<3.4.0" ], @@ -55620,9 +55998,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29552", - "id": "pyup.io-45915", - "more_info_path": "/vulnerabilities/CVE-2021-29552/45915", + "cve": "CVE-2021-29546", + "id": "pyup.io-45909", + "more_info_path": "/vulnerabilities/CVE-2021-29546/45909", "specs": [ "<3.4.0" ], @@ -55630,9 +56008,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29554", - "id": "pyup.io-45917", - "more_info_path": "/vulnerabilities/CVE-2021-29554/45917", + "cve": "CVE-2021-29565", + "id": "pyup.io-45928", + "more_info_path": "/vulnerabilities/CVE-2021-29565/45928", "specs": [ "<3.4.0" ], @@ -55640,9 +56018,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29556", - "id": "pyup.io-45919", - "more_info_path": "/vulnerabilities/CVE-2021-29556/45919", + "cve": "CVE-2021-29590", + "id": "pyup.io-45953", + "more_info_path": "/vulnerabilities/CVE-2021-29590/45953", "specs": [ "<3.4.0" ], @@ -55650,9 +56028,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29559", - "id": "pyup.io-45922", - "more_info_path": "/vulnerabilities/CVE-2021-29559/45922", + "cve": "CVE-2021-29582", + "id": "pyup.io-45945", + "more_info_path": "/vulnerabilities/CVE-2021-29582/45945", "specs": [ "<3.4.0" ], @@ -55660,9 +56038,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29563", - "id": "pyup.io-45926", - "more_info_path": "/vulnerabilities/CVE-2021-29563/45926", + "cve": "CVE-2021-29607", + "id": "pyup.io-45970", + "more_info_path": "/vulnerabilities/CVE-2021-29607/45970", "specs": [ "<3.4.0" ], @@ -55670,9 +56048,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29566", - "id": "pyup.io-45929", - "more_info_path": "/vulnerabilities/CVE-2021-29566/45929", + "cve": "CVE-2020-8285", + "id": "pyup.io-45868", + "more_info_path": "/vulnerabilities/CVE-2020-8285/45868", "specs": [ "<3.4.0" ], @@ -55680,9 +56058,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29574", - "id": "pyup.io-45937", - "more_info_path": "/vulnerabilities/CVE-2021-29574/45937", + "cve": "CVE-2020-8177", + "id": "pyup.io-45865", + "more_info_path": "/vulnerabilities/CVE-2020-8177/45865", "specs": [ "<3.4.0" ], @@ -55690,9 +56068,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29577", - "id": "pyup.io-45940", - "more_info_path": "/vulnerabilities/CVE-2021-29577/45940", + "cve": "CVE-2021-29597", + "id": "pyup.io-45960", + "more_info_path": "/vulnerabilities/CVE-2021-29597/45960", "specs": [ "<3.4.0" ], @@ -55700,9 +56078,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29579", - "id": "pyup.io-45942", - "more_info_path": "/vulnerabilities/CVE-2021-29579/45942", + "cve": "CVE-2021-29572", + "id": "pyup.io-45935", + "more_info_path": "/vulnerabilities/CVE-2021-29572/45935", "specs": [ "<3.4.0" ], @@ -55720,9 +56098,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29584", - "id": "pyup.io-45947", - "more_info_path": "/vulnerabilities/CVE-2021-29584/45947", + "cve": "CVE-2021-29591", + "id": "pyup.io-45954", + "more_info_path": "/vulnerabilities/CVE-2021-29591/45954", "specs": [ "<3.4.0" ], @@ -55730,9 +56108,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29585", - "id": "pyup.io-45948", - "more_info_path": "/vulnerabilities/CVE-2021-29585/45948", + "cve": "CVE-2021-29587", + "id": "pyup.io-45950", + "more_info_path": "/vulnerabilities/CVE-2021-29587/45950", "specs": [ "<3.4.0" ], @@ -55740,9 +56118,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29587", - "id": "pyup.io-45950", - "more_info_path": "/vulnerabilities/CVE-2021-29587/45950", + "cve": "CVE-2021-29584", + "id": "pyup.io-45947", + "more_info_path": "/vulnerabilities/CVE-2021-29584/45947", "specs": [ "<3.4.0" ], @@ -55750,9 +56128,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29588", - "id": "pyup.io-45951", - "more_info_path": "/vulnerabilities/CVE-2021-29588/45951", + "cve": "CVE-2021-29583", + "id": "pyup.io-45946", + "more_info_path": "/vulnerabilities/CVE-2021-29583/45946", "specs": [ "<3.4.0" ], @@ -55760,9 +56138,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29592", - "id": "pyup.io-45955", - "more_info_path": "/vulnerabilities/CVE-2021-29592/45955", + "cve": "CVE-2021-29571", + "id": "pyup.io-45934", + "more_info_path": "/vulnerabilities/CVE-2021-29571/45934", "specs": [ "<3.4.0" ], @@ -55770,9 +56148,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29593", - "id": "pyup.io-45956", - "more_info_path": "/vulnerabilities/CVE-2021-29593/45956", + "cve": "CVE-2021-29578", + "id": "pyup.io-45941", + "more_info_path": "/vulnerabilities/CVE-2021-29578/45941", "specs": [ "<3.4.0" ], @@ -55780,9 +56158,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29595", - "id": "pyup.io-45958", - "more_info_path": "/vulnerabilities/CVE-2021-29595/45958", + "cve": "CVE-2021-29576", + "id": "pyup.io-45939", + "more_info_path": "/vulnerabilities/CVE-2021-29576/45939", "specs": [ "<3.4.0" ], @@ -55790,9 +56168,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29598", - "id": "pyup.io-45961", - "more_info_path": "/vulnerabilities/CVE-2021-29598/45961", + "cve": "CVE-2021-29575", + "id": "pyup.io-45938", + "more_info_path": "/vulnerabilities/CVE-2021-29575/45938", "specs": [ "<3.4.0" ], @@ -55800,9 +56178,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29606", - "id": "pyup.io-45969", - "more_info_path": "/vulnerabilities/CVE-2021-29606/45969", + "cve": "CVE-2021-29568", + "id": "pyup.io-45931", + "more_info_path": "/vulnerabilities/CVE-2021-29568/45931", "specs": [ "<3.4.0" ], @@ -55810,9 +56188,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29601", - "id": "pyup.io-45964", - "more_info_path": "/vulnerabilities/CVE-2021-29601/45964", + "cve": "CVE-2021-29567", + "id": "pyup.io-45930", + "more_info_path": "/vulnerabilities/CVE-2021-29567/45930", "specs": [ "<3.4.0" ], @@ -55820,9 +56198,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29604", - "id": "pyup.io-45967", - "more_info_path": "/vulnerabilities/CVE-2021-29604/45967", + "cve": "CVE-2021-29560", + "id": "pyup.io-45923", + "more_info_path": "/vulnerabilities/CVE-2021-29560/45923", "specs": [ "<3.4.0" ], @@ -55830,9 +56208,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29605", - "id": "pyup.io-45968", - "more_info_path": "/vulnerabilities/CVE-2021-29605/45968", + "cve": "CVE-2021-29551", + "id": "pyup.io-45914", + "more_info_path": "/vulnerabilities/CVE-2021-29551/45914", "specs": [ "<3.4.0" ], @@ -55840,9 +56218,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29609", - "id": "pyup.io-45972", - "more_info_path": "/vulnerabilities/CVE-2021-29609/45972", + "cve": "CVE-2021-41199", + "id": "pyup.io-45987", + "more_info_path": "/vulnerabilities/CVE-2021-41199/45987", "specs": [ "<3.4.0" ], @@ -55850,9 +56228,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29611", - "id": "pyup.io-45974", - "more_info_path": "/vulnerabilities/CVE-2021-29611/45974", + "cve": "CVE-2021-29556", + "id": "pyup.io-45919", + "more_info_path": "/vulnerabilities/CVE-2021-29556/45919", "specs": [ "<3.4.0" ], @@ -55860,9 +56238,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29616", - "id": "pyup.io-45979", - "more_info_path": "/vulnerabilities/CVE-2021-29616/45979", + "cve": "CVE-2021-29553", + "id": "pyup.io-45916", + "more_info_path": "/vulnerabilities/CVE-2021-29553/45916", "specs": [ "<3.4.0" ], @@ -55870,9 +56248,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29617", - "id": "pyup.io-45980", - "more_info_path": "/vulnerabilities/CVE-2021-29617/45980", + "cve": "CVE-2021-29547", + "id": "pyup.io-45910", + "more_info_path": "/vulnerabilities/CVE-2021-29547/45910", "specs": [ "<3.4.0" ], @@ -55880,9 +56258,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29619", - "id": "pyup.io-45982", - "more_info_path": "/vulnerabilities/CVE-2021-29619/45982", + "cve": "CVE-2021-29541", + "id": "pyup.io-45904", + "more_info_path": "/vulnerabilities/CVE-2021-29541/45904", "specs": [ "<3.4.0" ], @@ -55890,9 +56268,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41195", - "id": "pyup.io-45983", - "more_info_path": "/vulnerabilities/CVE-2021-41195/45983", + "cve": "CVE-2021-29595", + "id": "pyup.io-45958", + "more_info_path": "/vulnerabilities/CVE-2021-29595/45958", "specs": [ "<3.4.0" ], @@ -55900,9 +56278,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41196", - "id": "pyup.io-45984", - "more_info_path": "/vulnerabilities/CVE-2021-41196/45984", + "cve": "CVE-2021-29539", + "id": "pyup.io-45902", + "more_info_path": "/vulnerabilities/CVE-2021-29539/45902", "specs": [ "<3.4.0" ], @@ -55910,9 +56288,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41197", - "id": "pyup.io-45985", - "more_info_path": "/vulnerabilities/CVE-2021-41197/45985", + "cve": "CVE-2021-29535", + "id": "pyup.io-45898", + "more_info_path": "/vulnerabilities/CVE-2021-29535/45898", "specs": [ "<3.4.0" ], @@ -55920,9 +56298,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41198", - "id": "pyup.io-45986", - "more_info_path": "/vulnerabilities/CVE-2021-41198/45986", + "cve": "CVE-2021-29598", + "id": "pyup.io-45961", + "more_info_path": "/vulnerabilities/CVE-2021-29598/45961", "specs": [ "<3.4.0" ], @@ -55930,9 +56308,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41199", - "id": "pyup.io-45987", - "more_info_path": "/vulnerabilities/CVE-2021-41199/45987", + "cve": "CVE-2021-29531", + "id": "pyup.io-45894", + "more_info_path": "/vulnerabilities/CVE-2021-29531/45894", "specs": [ "<3.4.0" ], @@ -55940,9 +56318,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41200", - "id": "pyup.io-45988", - "more_info_path": "/vulnerabilities/CVE-2021-41200/45988", + "cve": "CVE-2021-29530", + "id": "pyup.io-45893", + "more_info_path": "/vulnerabilities/CVE-2021-29530/45893", "specs": [ "<3.4.0" ], @@ -55950,9 +56328,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41201", - "id": "pyup.io-45989", - "more_info_path": "/vulnerabilities/CVE-2021-41201/45989", + "cve": "CVE-2021-29529", + "id": "pyup.io-45892", + "more_info_path": "/vulnerabilities/CVE-2021-29529/45892", "specs": [ "<3.4.0" ], @@ -55960,9 +56338,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41204", - "id": "pyup.io-45992", - "more_info_path": "/vulnerabilities/CVE-2021-41204/45992", + "cve": "CVE-2021-41216", + "id": "pyup.io-46004", + "more_info_path": "/vulnerabilities/CVE-2021-41216/46004", "specs": [ "<3.4.0" ], @@ -55970,9 +56348,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41206", - "id": "pyup.io-45994", - "more_info_path": "/vulnerabilities/CVE-2021-41206/45994", + "cve": "CVE-2021-29525", + "id": "pyup.io-45888", + "more_info_path": "/vulnerabilities/CVE-2021-29525/45888", "specs": [ "<3.4.0" ], @@ -55980,9 +56358,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41209", - "id": "pyup.io-45997", - "more_info_path": "/vulnerabilities/CVE-2021-41209/45997", + "cve": "CVE-2021-29523", + "id": "pyup.io-45886", + "more_info_path": "/vulnerabilities/CVE-2021-29523/45886", "specs": [ "<3.4.0" ], @@ -55990,9 +56368,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41208", - "id": "pyup.io-45996", - "more_info_path": "/vulnerabilities/CVE-2021-41208/45996", + "cve": "CVE-2021-29534", + "id": "pyup.io-45897", + "more_info_path": "/vulnerabilities/CVE-2021-29534/45897", "specs": [ "<3.4.0" ], @@ -56000,9 +56378,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41210", - "id": "pyup.io-45998", - "more_info_path": "/vulnerabilities/CVE-2021-41210/45998", + "cve": "CVE-2021-29522", + "id": "pyup.io-45885", + "more_info_path": "/vulnerabilities/CVE-2021-29522/45885", "specs": [ "<3.4.0" ], @@ -56010,9 +56388,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41211", - "id": "pyup.io-45999", - "more_info_path": "/vulnerabilities/CVE-2021-41211/45999", + "cve": "CVE-2021-41197", + "id": "pyup.io-45985", + "more_info_path": "/vulnerabilities/CVE-2021-41197/45985", "specs": [ "<3.4.0" ], @@ -56020,9 +56398,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41217", - "id": "pyup.io-46005", - "more_info_path": "/vulnerabilities/CVE-2021-41217/46005", + "cve": "CVE-2021-41201", + "id": "pyup.io-45989", + "more_info_path": "/vulnerabilities/CVE-2021-41201/45989", "specs": [ "<3.4.0" ], @@ -56030,9 +56408,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41222", - "id": "pyup.io-46010", - "more_info_path": "/vulnerabilities/CVE-2021-41222/46010", + "cve": "CVE-2021-41204", + "id": "pyup.io-45992", + "more_info_path": "/vulnerabilities/CVE-2021-41204/45992", "specs": [ "<3.4.0" ], @@ -56040,9 +56418,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41223", - "id": "pyup.io-46011", - "more_info_path": "/vulnerabilities/CVE-2021-41223/46011", + "cve": "CVE-2021-41208", + "id": "pyup.io-45996", + "more_info_path": "/vulnerabilities/CVE-2021-41208/45996", "specs": [ "<3.4.0" ], @@ -56050,9 +56428,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-26268", - "id": "pyup.io-45861", - "more_info_path": "/vulnerabilities/CVE-2020-26268/45861", + "cve": "CVE-2021-29519", + "id": "pyup.io-45882", + "more_info_path": "/vulnerabilities/CVE-2021-29519/45882", "specs": [ "<3.4.0" ], @@ -56060,9 +56438,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29521", - "id": "pyup.io-45884", - "more_info_path": "/vulnerabilities/CVE-2021-29521/45884", + "cve": "CVE-2021-29518", + "id": "pyup.io-45881", + "more_info_path": "/vulnerabilities/CVE-2021-29518/45881", "specs": [ "<3.4.0" ], @@ -56070,9 +56448,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29527", - "id": "pyup.io-45890", - "more_info_path": "/vulnerabilities/CVE-2021-29527/45890", + "cve": "CVE-2021-29517", + "id": "pyup.io-45880", + "more_info_path": "/vulnerabilities/CVE-2021-29517/45880", "specs": [ "<3.4.0" ], @@ -56080,9 +56458,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29538", - "id": "pyup.io-45901", - "more_info_path": "/vulnerabilities/CVE-2021-29538/45901", + "cve": "CVE-2021-29515", + "id": "pyup.io-45878", + "more_info_path": "/vulnerabilities/CVE-2021-29515/45878", "specs": [ "<3.4.0" ], @@ -56090,9 +56468,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29535", - "id": "pyup.io-45898", - "more_info_path": "/vulnerabilities/CVE-2021-29535/45898", + "cve": "CVE-2021-29516", + "id": "pyup.io-45879", + "more_info_path": "/vulnerabilities/CVE-2021-29516/45879", "specs": [ "<3.4.0" ], @@ -56100,9 +56478,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29555", - "id": "pyup.io-45918", - "more_info_path": "/vulnerabilities/CVE-2021-29555/45918", + "cve": "CVE-2021-41222", + "id": "pyup.io-46010", + "more_info_path": "/vulnerabilities/CVE-2021-41222/46010", "specs": [ "<3.4.0" ], @@ -56110,9 +56488,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29551", - "id": "pyup.io-45914", - "more_info_path": "/vulnerabilities/CVE-2021-29551/45914", + "cve": "CVE-2021-29554", + "id": "pyup.io-45917", + "more_info_path": "/vulnerabilities/CVE-2021-29554/45917", "specs": [ "<3.4.0" ], @@ -56120,9 +56498,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29586", - "id": "pyup.io-45949", - "more_info_path": "/vulnerabilities/CVE-2021-29586/45949", + "cve": "CVE-2021-29549", + "id": "pyup.io-45912", + "more_info_path": "/vulnerabilities/CVE-2021-29549/45912", "specs": [ "<3.4.0" ], @@ -56130,9 +56508,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29569", - "id": "pyup.io-45932", - "more_info_path": "/vulnerabilities/CVE-2021-29569/45932", + "cve": "CVE-2020-8284", + "id": "pyup.io-45867", + "more_info_path": "/vulnerabilities/CVE-2020-8284/45867", "specs": [ "<3.4.0" ], @@ -56140,9 +56518,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29570", - "id": "pyup.io-45933", - "more_info_path": "/vulnerabilities/CVE-2021-29570/45933", + "cve": "CVE-2020-26270", + "id": "pyup.io-45862", + "more_info_path": "/vulnerabilities/CVE-2020-26270/45862", "specs": [ "<3.4.0" ], @@ -56150,9 +56528,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29597", - "id": "pyup.io-45960", - "more_info_path": "/vulnerabilities/CVE-2021-29597/45960", + "cve": "CVE-2020-26268", + "id": "pyup.io-45861", + "more_info_path": "/vulnerabilities/CVE-2020-26268/45861", "specs": [ "<3.4.0" ], @@ -56160,9 +56538,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29576", - "id": "pyup.io-45939", - "more_info_path": "/vulnerabilities/CVE-2021-29576/45939", + "cve": "CVE-2020-26271", + "id": "pyup.io-45863", + "more_info_path": "/vulnerabilities/CVE-2020-26271/45863", "specs": [ "<3.4.0" ], @@ -56170,9 +56548,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29572", - "id": "pyup.io-45935", - "more_info_path": "/vulnerabilities/CVE-2021-29572/45935", + "cve": "CVE-2020-8286", + "id": "pyup.io-45869", + "more_info_path": "/vulnerabilities/CVE-2020-8286/45869", "specs": [ "<3.4.0" ], @@ -56180,9 +56558,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29599", - "id": "pyup.io-45962", - "more_info_path": "/vulnerabilities/CVE-2021-29599/45962", + "cve": "CVE-2020-15265", + "id": "pyup.io-45857", + "more_info_path": "/vulnerabilities/CVE-2020-15265/45857", "specs": [ "<3.4.0" ], @@ -56190,9 +56568,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41207", - "id": "pyup.io-45995", - "more_info_path": "/vulnerabilities/CVE-2021-41207/45995", + "cve": "CVE-2020-15250", + "id": "pyup.io-45856", + "more_info_path": "/vulnerabilities/CVE-2020-15250/45856", "specs": [ "<3.4.0" ], @@ -56200,9 +56578,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41205", - "id": "pyup.io-45993", - "more_info_path": "/vulnerabilities/CVE-2021-41205/45993", + "cve": "CVE-2020-14155", + "id": "pyup.io-45855", + "more_info_path": "/vulnerabilities/CVE-2020-14155/45855", "specs": [ "<3.4.0" ], @@ -56210,9 +56588,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41214", - "id": "pyup.io-46002", - "more_info_path": "/vulnerabilities/CVE-2021-41214/46002", + "cve": "CVE-2021-41203", + "id": "pyup.io-45991", + "more_info_path": "/vulnerabilities/CVE-2021-41203/45991", "specs": [ "<3.4.0" ], @@ -56220,9 +56598,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29610", - "id": "pyup.io-45973", - "more_info_path": "/vulnerabilities/CVE-2021-29610/45973", + "cve": "CVE-2021-29533", + "id": "pyup.io-45896", + "more_info_path": "/vulnerabilities/CVE-2021-29533/45896", "specs": [ "<3.4.0" ], @@ -56230,9 +56608,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41227", - "id": "pyup.io-46015", - "more_info_path": "/vulnerabilities/CVE-2021-41227/46015", + "cve": "CVE-2021-41202", + "id": "pyup.io-45990", + "more_info_path": "/vulnerabilities/CVE-2021-41202/45990", "specs": [ "<3.4.0" ], @@ -56240,9 +56618,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29512", - "id": "pyup.io-45875", - "more_info_path": "/vulnerabilities/CVE-2021-29512/45875", + "cve": "CVE-2021-29594", + "id": "pyup.io-45957", + "more_info_path": "/vulnerabilities/CVE-2021-29594/45957", "specs": [ "<3.4.0" ], @@ -56250,9 +56628,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29516", - "id": "pyup.io-45879", - "more_info_path": "/vulnerabilities/CVE-2021-29516/45879", + "cve": "CVE-2021-29564", + "id": "pyup.io-45927", + "more_info_path": "/vulnerabilities/CVE-2021-29564/45927", "specs": [ "<3.4.0" ], @@ -56260,9 +56638,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29531", - "id": "pyup.io-45894", - "more_info_path": "/vulnerabilities/CVE-2021-29531/45894", + "cve": "CVE-2021-41212", + "id": "pyup.io-46000", + "more_info_path": "/vulnerabilities/CVE-2021-41212/46000", "specs": [ "<3.4.0" ], @@ -56270,9 +56648,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29578", - "id": "pyup.io-45941", - "more_info_path": "/vulnerabilities/CVE-2021-29578/45941", + "cve": "CVE-2021-41220", + "id": "pyup.io-46008", + "more_info_path": "/vulnerabilities/CVE-2021-41220/46008", "specs": [ "<3.4.0" ], @@ -56280,9 +56658,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29594", - "id": "pyup.io-45957", - "more_info_path": "/vulnerabilities/CVE-2021-29594/45957", + "cve": "CVE-2021-41198", + "id": "pyup.io-45986", + "more_info_path": "/vulnerabilities/CVE-2021-41198/45986", "specs": [ "<3.4.0" ], @@ -56290,9 +56668,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29600", - "id": "pyup.io-45963", - "more_info_path": "/vulnerabilities/CVE-2021-29600/45963", + "cve": "CVE-2021-41196", + "id": "pyup.io-45984", + "more_info_path": "/vulnerabilities/CVE-2021-41196/45984", "specs": [ "<3.4.0" ], @@ -56300,9 +56678,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41202", - "id": "pyup.io-45990", - "more_info_path": "/vulnerabilities/CVE-2021-41202/45990", + "cve": "CVE-2021-29548", + "id": "pyup.io-45911", + "more_info_path": "/vulnerabilities/CVE-2021-29548/45911", "specs": [ "<3.4.0" ], @@ -56310,9 +56688,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29543", - "id": "pyup.io-45906", - "more_info_path": "/vulnerabilities/CVE-2021-29543/45906", + "cve": "CVE-2021-22923", + "id": "pyup.io-45871", + "more_info_path": "/vulnerabilities/CVE-2021-22923/45871", "specs": [ "<3.4.0" ], @@ -56320,9 +56698,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29547", - "id": "pyup.io-45910", - "more_info_path": "/vulnerabilities/CVE-2021-29547/45910", + "cve": "CVE-2021-41219", + "id": "pyup.io-46007", + "more_info_path": "/vulnerabilities/CVE-2021-41219/46007", "specs": [ "<3.4.0" ], @@ -56330,9 +56708,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29603", - "id": "pyup.io-45966", - "more_info_path": "/vulnerabilities/CVE-2021-29603/45966", + "cve": "CVE-2021-41218", + "id": "pyup.io-46006", + "more_info_path": "/vulnerabilities/CVE-2021-41218/46006", "specs": [ "<3.4.0" ], @@ -56340,9 +56718,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41212", - "id": "pyup.io-46000", - "more_info_path": "/vulnerabilities/CVE-2021-41212/46000", + "cve": "CVE-2021-41207", + "id": "pyup.io-45995", + "more_info_path": "/vulnerabilities/CVE-2021-41207/45995", "specs": [ "<3.4.0" ], @@ -56350,9 +56728,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41219", - "id": "pyup.io-46007", - "more_info_path": "/vulnerabilities/CVE-2021-41219/46007", + "cve": "CVE-2021-29618", + "id": "pyup.io-45981", + "more_info_path": "/vulnerabilities/CVE-2021-29618/45981", "specs": [ "<3.4.0" ], @@ -56360,9 +56738,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29564", - "id": "pyup.io-45927", - "more_info_path": "/vulnerabilities/CVE-2021-29564/45927", + "cve": "CVE-2021-29514", + "id": "pyup.io-45877", + "more_info_path": "/vulnerabilities/CVE-2021-29514/45877", "specs": [ "<3.4.0" ], @@ -56370,9 +56748,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41220", - "id": "pyup.io-46008", - "more_info_path": "/vulnerabilities/CVE-2021-41220/46008", + "cve": "CVE-2021-41206", + "id": "pyup.io-45994", + "more_info_path": "/vulnerabilities/CVE-2021-41206/45994", "specs": [ "<3.4.0" ], @@ -56380,9 +56758,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41224", - "id": "pyup.io-46012", - "more_info_path": "/vulnerabilities/CVE-2021-41224/46012", + "cve": "CVE-2021-41227", + "id": "pyup.io-46015", + "more_info_path": "/vulnerabilities/CVE-2021-41227/46015", "specs": [ "<3.4.0" ], @@ -56390,9 +56768,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41225", - "id": "pyup.io-46013", - "more_info_path": "/vulnerabilities/CVE-2021-41225/46013", + "cve": "CVE-2021-29536", + "id": "pyup.io-45899", + "more_info_path": "/vulnerabilities/CVE-2021-29536/45899", "specs": [ "<3.4.0" ], @@ -56400,9 +56778,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41226", - "id": "pyup.io-46014", - "more_info_path": "/vulnerabilities/CVE-2021-41226/46014", + "cve": "CVE-2021-29524", + "id": "pyup.io-45887", + "more_info_path": "/vulnerabilities/CVE-2021-29524/45887", "specs": [ "<3.4.0" ], @@ -56410,9 +56788,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-8285", - "id": "pyup.io-45868", - "more_info_path": "/vulnerabilities/CVE-2020-8285/45868", + "cve": "CVE-2021-29526", + "id": "pyup.io-45889", + "more_info_path": "/vulnerabilities/CVE-2021-29526/45889", "specs": [ "<3.4.0" ], @@ -56420,9 +56798,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-8177", - "id": "pyup.io-45865", - "more_info_path": "/vulnerabilities/CVE-2020-8177/45865", + "cve": "CVE-2021-41226", + "id": "pyup.io-46014", + "more_info_path": "/vulnerabilities/CVE-2021-41226/46014", "specs": [ "<3.4.0" ], @@ -56430,9 +56808,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-8169", - "id": "pyup.io-45864", - "more_info_path": "/vulnerabilities/CVE-2020-8169/45864", + "cve": "CVE-2021-29545", + "id": "pyup.io-45908", + "more_info_path": "/vulnerabilities/CVE-2021-29545/45908", "specs": [ "<3.4.0" ], @@ -56440,9 +56818,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29613", - "id": "pyup.io-45976", - "more_info_path": "/vulnerabilities/CVE-2021-29613/45976", + "cve": "CVE-2021-22926", + "id": "pyup.io-45874", + "more_info_path": "/vulnerabilities/CVE-2021-22926/45874", "specs": [ "<3.4.0" ], @@ -56450,9 +56828,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-41203", - "id": "pyup.io-45991", - "more_info_path": "/vulnerabilities/CVE-2021-41203/45991", + "cve": "CVE-2021-29593", + "id": "pyup.io-45956", + "more_info_path": "/vulnerabilities/CVE-2021-29593/45956", "specs": [ "<3.4.0" ], @@ -56460,9 +56838,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-8284", - "id": "pyup.io-45867", - "more_info_path": "/vulnerabilities/CVE-2020-8284/45867", + "cve": "CVE-2021-41217", + "id": "pyup.io-46005", + "more_info_path": "/vulnerabilities/CVE-2021-41217/46005", "specs": [ "<3.4.0" ], @@ -56470,9 +56848,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-8286", - "id": "pyup.io-45869", - "more_info_path": "/vulnerabilities/CVE-2020-8286/45869", + "cve": "CVE-2021-41215", + "id": "pyup.io-46003", + "more_info_path": "/vulnerabilities/CVE-2021-41215/46003", "specs": [ "<3.4.0" ], @@ -56480,9 +56858,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-14155", - "id": "pyup.io-45855", - "more_info_path": "/vulnerabilities/CVE-2020-14155/45855", + "cve": "CVE-2021-41195", + "id": "pyup.io-45983", + "more_info_path": "/vulnerabilities/CVE-2021-41195/45983", "specs": [ "<3.4.0" ], @@ -56490,9 +56868,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-15250", - "id": "pyup.io-45856", - "more_info_path": "/vulnerabilities/CVE-2020-15250/45856", + "cve": "CVE-2021-29559", + "id": "pyup.io-45922", + "more_info_path": "/vulnerabilities/CVE-2021-29559/45922", "specs": [ "<3.4.0" ], @@ -56500,9 +56878,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2020-13790", - "id": "pyup.io-45854", - "more_info_path": "/vulnerabilities/CVE-2020-13790/45854", + "cve": "CVE-2021-29544", + "id": "pyup.io-45907", + "more_info_path": "/vulnerabilities/CVE-2021-29544/45907", "specs": [ "<3.4.0" ], @@ -56510,9 +56888,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-22925", - "id": "pyup.io-45873", - "more_info_path": "/vulnerabilities/CVE-2021-22925/45873", + "cve": "CVE-2021-22922", + "id": "pyup.io-45870", + "more_info_path": "/vulnerabilities/CVE-2021-22922/45870", "specs": [ "<3.4.0" ], @@ -56520,9 +56898,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-22923", - "id": "pyup.io-45871", - "more_info_path": "/vulnerabilities/CVE-2021-22923/45871", + "cve": "CVE-2021-29513", + "id": "pyup.io-45876", + "more_info_path": "/vulnerabilities/CVE-2021-29513/45876", "specs": [ "<3.4.0" ], @@ -56530,9 +56908,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-22924", - "id": "pyup.io-45872", - "more_info_path": "/vulnerabilities/CVE-2021-22924/45872", + "cve": "CVE-2020-8169", + "id": "pyup.io-45864", + "more_info_path": "/vulnerabilities/CVE-2020-8169/45864", "specs": [ "<3.4.0" ], @@ -56540,9 +56918,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-22922", - "id": "pyup.io-45870", - "more_info_path": "/vulnerabilities/CVE-2021-22922/45870", + "cve": "CVE-2020-8231", + "id": "pyup.io-45866", + "more_info_path": "/vulnerabilities/CVE-2020-8231/45866", "specs": [ "<3.4.0" ], @@ -56550,9 +56928,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-22926", - "id": "pyup.io-45874", - "more_info_path": "/vulnerabilities/CVE-2021-22926/45874", + "cve": "CVE-2021-41225", + "id": "pyup.io-46013", + "more_info_path": "/vulnerabilities/CVE-2021-41225/46013", "specs": [ "<3.4.0" ], @@ -56560,9 +56938,9 @@ }, { "advisory": "Hotaru 3.4.0 updates its dependency 'Tensorflow' minimum requirement to v2.6.1 to include security fixes.", - "cve": "CVE-2021-29544", - "id": "pyup.io-45907", - "more_info_path": "/vulnerabilities/CVE-2021-29544/45907", + "cve": "CVE-2021-41223", + "id": "pyup.io-46011", + "more_info_path": "/vulnerabilities/CVE-2021-41223/46011", "specs": [ "<3.4.0" ], @@ -56570,9 +56948,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21738", - "id": "pyup.io-46031", - "more_info_path": "/vulnerabilities/CVE-2022-21738/46031", + "cve": "CVE-2022-23575", + "id": "pyup.io-46053", + "more_info_path": "/vulnerabilities/CVE-2022-23575/46053", "specs": [ "<3.4.1" ], @@ -56580,9 +56958,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21735", - "id": "pyup.io-46028", - "more_info_path": "/vulnerabilities/CVE-2022-21735/46028", + "cve": "CVE-2022-21740", + "id": "pyup.io-46033", + "more_info_path": "/vulnerabilities/CVE-2022-21740/46033", "specs": [ "<3.4.1" ], @@ -56590,9 +56968,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23591", - "id": "pyup.io-46068", - "more_info_path": "/vulnerabilities/CVE-2022-23591/46068", + "cve": "CVE-2022-23570", + "id": "pyup.io-46048", + "more_info_path": "/vulnerabilities/CVE-2022-23570/46048", "specs": [ "<3.4.1" ], @@ -56600,9 +56978,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23575", - "id": "pyup.io-46053", - "more_info_path": "/vulnerabilities/CVE-2022-23575/46053", + "cve": "CVE-2022-21729", + "id": "pyup.io-46022", + "more_info_path": "/vulnerabilities/CVE-2022-21729/46022", "specs": [ "<3.4.1" ], @@ -56610,9 +56988,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21740", - "id": "pyup.io-46033", - "more_info_path": "/vulnerabilities/CVE-2022-21740/46033", + "cve": "CVE-2022-21727", + "id": "pyup.io-46020", + "more_info_path": "/vulnerabilities/CVE-2022-21727/46020", "specs": [ "<3.4.1" ], @@ -56620,9 +56998,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23577", - "id": "pyup.io-46055", - "more_info_path": "/vulnerabilities/CVE-2022-23577/46055", + "cve": "CVE-2022-21725", + "id": "pyup.io-46018", + "more_info_path": "/vulnerabilities/CVE-2022-21725/46018", "specs": [ "<3.4.1" ], @@ -56630,9 +57008,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23586", - "id": "pyup.io-46064", - "more_info_path": "/vulnerabilities/CVE-2022-23586/46064", + "cve": "CVE-2022-21730", + "id": "pyup.io-46023", + "more_info_path": "/vulnerabilities/CVE-2022-21730/46023", "specs": [ "<3.4.1" ], @@ -56640,9 +57018,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23563", - "id": "pyup.io-46041", - "more_info_path": "/vulnerabilities/CVE-2022-23563/46041", + "cve": "CVE-2022-23560", + "id": "pyup.io-46038", + "more_info_path": "/vulnerabilities/CVE-2022-23560/46038", "specs": [ "<3.4.1" ], @@ -56650,9 +57028,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23589", - "id": "pyup.io-46067", - "more_info_path": "/vulnerabilities/CVE-2022-23589/46067", + "cve": "CVE-2022-23562", + "id": "pyup.io-46040", + "more_info_path": "/vulnerabilities/CVE-2022-23562/46040", "specs": [ "<3.4.1" ], @@ -56660,9 +57038,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21731", - "id": "pyup.io-46024", - "more_info_path": "/vulnerabilities/CVE-2022-21731/46024", + "cve": "CVE-2022-23566", + "id": "pyup.io-46044", + "more_info_path": "/vulnerabilities/CVE-2022-23566/46044", "specs": [ "<3.4.1" ], @@ -56670,9 +57048,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23570", - "id": "pyup.io-46048", - "more_info_path": "/vulnerabilities/CVE-2022-23570/46048", + "cve": "CVE-2022-23574", + "id": "pyup.io-46052", + "more_info_path": "/vulnerabilities/CVE-2022-23574/46052", "specs": [ "<3.4.1" ], @@ -56680,9 +57058,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21726", - "id": "pyup.io-46019", - "more_info_path": "/vulnerabilities/CVE-2022-21726/46019", + "cve": "CVE-2022-23567", + "id": "pyup.io-46045", + "more_info_path": "/vulnerabilities/CVE-2022-23567/46045", "specs": [ "<3.4.1" ], @@ -56690,9 +57068,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21729", - "id": "pyup.io-46022", - "more_info_path": "/vulnerabilities/CVE-2022-21729/46022", + "cve": "CVE-2022-23571", + "id": "pyup.io-46049", + "more_info_path": "/vulnerabilities/CVE-2022-23571/46049", "specs": [ "<3.4.1" ], @@ -56700,9 +57078,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23565", - "id": "pyup.io-46043", - "more_info_path": "/vulnerabilities/CVE-2022-23565/46043", + "cve": "CVE-2022-23578", + "id": "pyup.io-46056", + "more_info_path": "/vulnerabilities/CVE-2022-23578/46056", "specs": [ "<3.4.1" ], @@ -56710,9 +57088,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21725", - "id": "pyup.io-46018", - "more_info_path": "/vulnerabilities/CVE-2022-21725/46018", + "cve": "CVE-2022-23585", + "id": "pyup.io-46063", + "more_info_path": "/vulnerabilities/CVE-2022-23585/46063", "specs": [ "<3.4.1" ], @@ -56720,9 +57098,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21727", - "id": "pyup.io-46020", - "more_info_path": "/vulnerabilities/CVE-2022-21727/46020", + "cve": "CVE-2022-21734", + "id": "pyup.io-46027", + "more_info_path": "/vulnerabilities/CVE-2022-21734/46027", "specs": [ "<3.4.1" ], @@ -56730,9 +57108,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21728", - "id": "pyup.io-46021", - "more_info_path": "/vulnerabilities/CVE-2022-21728/46021", + "cve": "CVE-2022-21733", + "id": "pyup.io-46026", + "more_info_path": "/vulnerabilities/CVE-2022-21733/46026", "specs": [ "<3.4.1" ], @@ -56740,9 +57118,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21730", - "id": "pyup.io-46023", - "more_info_path": "/vulnerabilities/CVE-2022-21730/46023", + "cve": "CVE-2022-23573", + "id": "pyup.io-46051", + "more_info_path": "/vulnerabilities/CVE-2022-23573/46051", "specs": [ "<3.4.1" ], @@ -56750,9 +57128,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21736", - "id": "pyup.io-46029", - "more_info_path": "/vulnerabilities/CVE-2022-21736/46029", + "cve": "CVE-2022-23587", + "id": "pyup.io-46065", + "more_info_path": "/vulnerabilities/CVE-2022-23587/46065", "specs": [ "<3.4.1" ], @@ -56760,9 +57138,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23560", - "id": "pyup.io-46038", - "more_info_path": "/vulnerabilities/CVE-2022-23560/46038", + "cve": "CVE-2022-23580", + "id": "pyup.io-46058", + "more_info_path": "/vulnerabilities/CVE-2022-23580/46058", "specs": [ "<3.4.1" ], @@ -56770,9 +57148,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21741", - "id": "pyup.io-46034", - "more_info_path": "/vulnerabilities/CVE-2022-21741/46034", + "cve": "CVE-2022-23572", + "id": "pyup.io-46050", + "more_info_path": "/vulnerabilities/CVE-2022-23572/46050", "specs": [ "<3.4.1" ], @@ -56780,9 +57158,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23558", - "id": "pyup.io-46036", - "more_info_path": "/vulnerabilities/CVE-2022-23558/46036", + "cve": "CVE-2020-10531", + "id": "pyup.io-46017", + "more_info_path": "/vulnerabilities/CVE-2020-10531/46017", "specs": [ "<3.4.1" ], @@ -56790,9 +57168,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23559", - "id": "pyup.io-46037", - "more_info_path": "/vulnerabilities/CVE-2022-23559/46037", + "cve": "CVE-2022-23595", + "id": "pyup.io-46069", + "more_info_path": "/vulnerabilities/CVE-2022-23595/46069", "specs": [ "<3.4.1" ], @@ -56800,9 +57178,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23561", - "id": "pyup.io-46039", - "more_info_path": "/vulnerabilities/CVE-2022-23561/46039", + "cve": "CVE-2022-23591", + "id": "pyup.io-46068", + "more_info_path": "/vulnerabilities/CVE-2022-23591/46068", "specs": [ "<3.4.1" ], @@ -56810,9 +57188,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23562", - "id": "pyup.io-46040", - "more_info_path": "/vulnerabilities/CVE-2022-23562/46040", + "cve": "CVE-2022-23589", + "id": "pyup.io-46067", + "more_info_path": "/vulnerabilities/CVE-2022-23589/46067", "specs": [ "<3.4.1" ], @@ -56820,9 +57198,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23566", - "id": "pyup.io-46044", - "more_info_path": "/vulnerabilities/CVE-2022-23566/46044", + "cve": "CVE-2022-23588", + "id": "pyup.io-46066", + "more_info_path": "/vulnerabilities/CVE-2022-23588/46066", "specs": [ "<3.4.1" ], @@ -56830,9 +57208,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23567", - "id": "pyup.io-46045", - "more_info_path": "/vulnerabilities/CVE-2022-23567/46045", + "cve": "CVE-2022-23586", + "id": "pyup.io-46064", + "more_info_path": "/vulnerabilities/CVE-2022-23586/46064", "specs": [ "<3.4.1" ], @@ -56840,9 +57218,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23569", - "id": "pyup.io-46047", - "more_info_path": "/vulnerabilities/CVE-2022-23569/46047", + "cve": "CVE-2022-23584", + "id": "pyup.io-46062", + "more_info_path": "/vulnerabilities/CVE-2022-23584/46062", "specs": [ "<3.4.1" ], @@ -56850,9 +57228,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23574", - "id": "pyup.io-46052", - "more_info_path": "/vulnerabilities/CVE-2022-23574/46052", + "cve": "CVE-2022-23583", + "id": "pyup.io-46061", + "more_info_path": "/vulnerabilities/CVE-2022-23583/46061", "specs": [ "<3.4.1" ], @@ -56860,9 +57238,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23571", - "id": "pyup.io-46049", - "more_info_path": "/vulnerabilities/CVE-2022-23571/46049", + "cve": "CVE-2022-23582", + "id": "pyup.io-46060", + "more_info_path": "/vulnerabilities/CVE-2022-23582/46060", "specs": [ "<3.4.1" ], @@ -56870,9 +57248,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23576", - "id": "pyup.io-46054", - "more_info_path": "/vulnerabilities/CVE-2022-23576/46054", + "cve": "CVE-2022-23581", + "id": "pyup.io-46059", + "more_info_path": "/vulnerabilities/CVE-2022-23581/46059", "specs": [ "<3.4.1" ], @@ -56880,9 +57258,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23578", - "id": "pyup.io-46056", - "more_info_path": "/vulnerabilities/CVE-2022-23578/46056", + "cve": "CVE-2022-23579", + "id": "pyup.io-46057", + "more_info_path": "/vulnerabilities/CVE-2022-23579/46057", "specs": [ "<3.4.1" ], @@ -56890,9 +57268,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23579", - "id": "pyup.io-46057", - "more_info_path": "/vulnerabilities/CVE-2022-23579/46057", + "cve": "CVE-2022-23577", + "id": "pyup.io-46055", + "more_info_path": "/vulnerabilities/CVE-2022-23577/46055", "specs": [ "<3.4.1" ], @@ -56900,9 +57278,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23581", - "id": "pyup.io-46059", - "more_info_path": "/vulnerabilities/CVE-2022-23581/46059", + "cve": "CVE-2022-23576", + "id": "pyup.io-46054", + "more_info_path": "/vulnerabilities/CVE-2022-23576/46054", "specs": [ "<3.4.1" ], @@ -56910,9 +57288,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23582", - "id": "pyup.io-46060", - "more_info_path": "/vulnerabilities/CVE-2022-23582/46060", + "cve": "CVE-2022-23565", + "id": "pyup.io-46043", + "more_info_path": "/vulnerabilities/CVE-2022-23565/46043", "specs": [ "<3.4.1" ], @@ -56920,9 +57298,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23584", - "id": "pyup.io-46062", - "more_info_path": "/vulnerabilities/CVE-2022-23584/46062", + "cve": "CVE-2022-23564", + "id": "pyup.io-46042", + "more_info_path": "/vulnerabilities/CVE-2022-23564/46042", "specs": [ "<3.4.1" ], @@ -56930,9 +57308,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23585", - "id": "pyup.io-46063", - "more_info_path": "/vulnerabilities/CVE-2022-23585/46063", + "cve": "CVE-2022-23563", + "id": "pyup.io-46041", + "more_info_path": "/vulnerabilities/CVE-2022-23563/46041", "specs": [ "<3.4.1" ], @@ -56940,9 +57318,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23588", - "id": "pyup.io-46066", - "more_info_path": "/vulnerabilities/CVE-2022-23588/46066", + "cve": "CVE-2022-23561", + "id": "pyup.io-46039", + "more_info_path": "/vulnerabilities/CVE-2022-23561/46039", "specs": [ "<3.4.1" ], @@ -56950,9 +57328,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21732", - "id": "pyup.io-46025", - "more_info_path": "/vulnerabilities/CVE-2022-21732/46025", + "cve": "CVE-2022-23559", + "id": "pyup.io-46037", + "more_info_path": "/vulnerabilities/CVE-2022-23559/46037", "specs": [ "<3.4.1" ], @@ -56960,9 +57338,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21733", - "id": "pyup.io-46026", - "more_info_path": "/vulnerabilities/CVE-2022-21733/46026", + "cve": "CVE-2022-23558", + "id": "pyup.io-46036", + "more_info_path": "/vulnerabilities/CVE-2022-23558/46036", "specs": [ "<3.4.1" ], @@ -56980,9 +57358,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23568", - "id": "pyup.io-46046", - "more_info_path": "/vulnerabilities/CVE-2022-23568/46046", + "cve": "CVE-2022-21741", + "id": "pyup.io-46034", + "more_info_path": "/vulnerabilities/CVE-2022-21741/46034", "specs": [ "<3.4.1" ], @@ -56990,9 +57368,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21734", - "id": "pyup.io-46027", - "more_info_path": "/vulnerabilities/CVE-2022-21734/46027", + "cve": "CVE-2022-21739", + "id": "pyup.io-46032", + "more_info_path": "/vulnerabilities/CVE-2022-21739/46032", "specs": [ "<3.4.1" ], @@ -57000,9 +57378,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23573", - "id": "pyup.io-46051", - "more_info_path": "/vulnerabilities/CVE-2022-23573/46051", + "cve": "CVE-2022-21738", + "id": "pyup.io-46031", + "more_info_path": "/vulnerabilities/CVE-2022-21738/46031", "specs": [ "<3.4.1" ], @@ -57010,9 +57388,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23595", - "id": "pyup.io-46069", - "more_info_path": "/vulnerabilities/CVE-2022-23595/46069", + "cve": "CVE-2022-21737", + "id": "pyup.io-46030", + "more_info_path": "/vulnerabilities/CVE-2022-21737/46030", "specs": [ "<3.4.1" ], @@ -57020,9 +57398,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23587", - "id": "pyup.io-46065", - "more_info_path": "/vulnerabilities/CVE-2022-23587/46065", + "cve": "CVE-2022-23569", + "id": "pyup.io-46047", + "more_info_path": "/vulnerabilities/CVE-2022-23569/46047", "specs": [ "<3.4.1" ], @@ -57030,9 +57408,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21737", - "id": "pyup.io-46030", - "more_info_path": "/vulnerabilities/CVE-2022-21737/46030", + "cve": "CVE-2022-21735", + "id": "pyup.io-46028", + "more_info_path": "/vulnerabilities/CVE-2022-21735/46028", "specs": [ "<3.4.1" ], @@ -57040,9 +57418,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-21739", - "id": "pyup.io-46032", - "more_info_path": "/vulnerabilities/CVE-2022-21739/46032", + "cve": "CVE-2022-23568", + "id": "pyup.io-46046", + "more_info_path": "/vulnerabilities/CVE-2022-23568/46046", "specs": [ "<3.4.1" ], @@ -57050,9 +57428,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23564", - "id": "pyup.io-46042", - "more_info_path": "/vulnerabilities/CVE-2022-23564/46042", + "cve": "CVE-2022-21736", + "id": "pyup.io-46029", + "more_info_path": "/vulnerabilities/CVE-2022-21736/46029", "specs": [ "<3.4.1" ], @@ -57060,9 +57438,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23580", - "id": "pyup.io-46058", - "more_info_path": "/vulnerabilities/CVE-2022-23580/46058", + "cve": "CVE-2022-21732", + "id": "pyup.io-46025", + "more_info_path": "/vulnerabilities/CVE-2022-21732/46025", "specs": [ "<3.4.1" ], @@ -57070,9 +57448,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23583", - "id": "pyup.io-46061", - "more_info_path": "/vulnerabilities/CVE-2022-23583/46061", + "cve": "CVE-2022-21731", + "id": "pyup.io-46024", + "more_info_path": "/vulnerabilities/CVE-2022-21731/46024", "specs": [ "<3.4.1" ], @@ -57080,9 +57458,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2022-23572", - "id": "pyup.io-46050", - "more_info_path": "/vulnerabilities/CVE-2022-23572/46050", + "cve": "CVE-2022-21728", + "id": "pyup.io-46021", + "more_info_path": "/vulnerabilities/CVE-2022-21728/46021", "specs": [ "<3.4.1" ], @@ -57090,9 +57468,9 @@ }, { "advisory": "Hotaru 3.4.1 updates its dependency 'TensorFlow' minimum requirement to v2.8.0 to include security fixes.", - "cve": "CVE-2020-10531", - "id": "pyup.io-46017", - "more_info_path": "/vulnerabilities/CVE-2020-10531/46017", + "cve": "CVE-2022-21726", + "id": "pyup.io-46019", + "more_info_path": "/vulnerabilities/CVE-2022-21726/46019", "specs": [ "<3.4.1" ], @@ -57297,20 +57675,20 @@ "v": "==3.2.2" }, { - "advisory": "HTTPie is a command-line HTTP client. HTTPie has the practical concept of sessions, which help users to persistently store some of the state that belongs to the outgoing requests and incoming responses on the disk for further usage. Before 3.1.0, HTTPie didn't distinguish between cookies and hosts they belonged. This behavior resulted in the exposure of some cookies when there are redirects originating from the actual host to a third party website. Users are advised to upgrade. There are no known workarounds.", - "cve": "CVE-2022-24737", - "id": "pyup.io-54711", - "more_info_path": "/vulnerabilities/CVE-2022-24737/54711", + "advisory": "Exposure of Sensitive Information to an Unauthorized Actor in GitHub repository httpie/httpie prior to 3.1.0.", + "cve": "CVE-2022-0430", + "id": "pyup.io-54707", + "more_info_path": "/vulnerabilities/CVE-2022-0430/54707", "specs": [ ">=0,<3.1.0" ], "v": ">=0,<3.1.0" }, { - "advisory": "Exposure of Sensitive Information to an Unauthorized Actor in GitHub repository httpie/httpie prior to 3.1.0.", - "cve": "CVE-2022-0430", - "id": "pyup.io-54707", - "more_info_path": "/vulnerabilities/CVE-2022-0430/54707", + "advisory": "HTTPie is a command-line HTTP client. HTTPie has the practical concept of sessions, which help users to persistently store some of the state that belongs to the outgoing requests and incoming responses on the disk for further usage. Before 3.1.0, HTTPie didn't distinguish between cookies and hosts they belonged. This behavior resulted in the exposure of some cookies when there are redirects originating from the actual host to a third party website. Users are advised to upgrade. There are no known workarounds.", + "cve": "CVE-2022-24737", + "id": "pyup.io-54711", + "more_info_path": "/vulnerabilities/CVE-2022-24737/54711", "specs": [ ">=0,<3.1.0" ], @@ -57381,10 +57759,10 @@ "v": "<2.1.3" }, { - "advisory": "Httprunner 3.1.7 updates its dependency 'pyyaml' to v5.4.1 to include security fixes.", - "cve": "CVE-2020-14343", - "id": "pyup.io-47848", - "more_info_path": "/vulnerabilities/CVE-2020-14343/47848", + "advisory": "Httprunner 3.1.7 updates its dependency 'uvicorn' to v0.11.7 to include security fixes.", + "cve": "CVE-2020-7694", + "id": "pyup.io-47850", + "more_info_path": "/vulnerabilities/CVE-2020-7694/47850", "specs": [ "<3.1.7" ], @@ -57401,20 +57779,20 @@ "v": "<3.1.7" }, { - "advisory": "Httprunner 3.1.7 updates its dependency 'fastapi' to v0.70.0 to include a security fix.", - "cve": "CVE-2021-32677", - "id": "pyup.io-47028", - "more_info_path": "/vulnerabilities/CVE-2021-32677/47028", + "advisory": "Httprunner 3.1.7 updates its dependency 'pyyaml' to v5.4.1 to include security fixes.", + "cve": "CVE-2020-14343", + "id": "pyup.io-47848", + "more_info_path": "/vulnerabilities/CVE-2020-14343/47848", "specs": [ "<3.1.7" ], "v": "<3.1.7" }, { - "advisory": "Httprunner 3.1.7 updates its dependency 'uvicorn' to v0.11.7 to include security fixes.", - "cve": "CVE-2020-7694", - "id": "pyup.io-47850", - "more_info_path": "/vulnerabilities/CVE-2020-7694/47850", + "advisory": "Httprunner 3.1.7 updates its dependency 'fastapi' to v0.70.0 to include a security fix.", + "cve": "CVE-2021-32677", + "id": "pyup.io-47028", + "more_info_path": "/vulnerabilities/CVE-2021-32677/47028", "specs": [ "<3.1.7" ], @@ -57422,9 +57800,9 @@ }, { "advisory": "Httprunner 3.1.7 updates its dependency 'pyyaml' to v5.4.1 to include security fixes.", - "cve": "CVE-2020-1747", - "id": "pyup.io-47849", - "more_info_path": "/vulnerabilities/CVE-2020-1747/47849", + "cve": "CVE-2019-20477", + "id": "pyup.io-47847", + "more_info_path": "/vulnerabilities/CVE-2019-20477/47847", "specs": [ "<3.1.7" ], @@ -57432,9 +57810,9 @@ }, { "advisory": "Httprunner 3.1.7 updates its dependency 'pyyaml' to v5.4.1 to include security fixes.", - "cve": "CVE-2019-20477", - "id": "pyup.io-47847", - "more_info_path": "/vulnerabilities/CVE-2019-20477/47847", + "cve": "CVE-2020-1747", + "id": "pyup.io-47849", + "more_info_path": "/vulnerabilities/CVE-2020-1747/47849", "specs": [ "<3.1.7" ], @@ -57480,9 +57858,9 @@ "httptools": [ { "advisory": "Httptools 0.5.0 updates the bundled 'llhttp' library to v6.0.9 to include security fixes.", - "cve": "CVE-2022-32214", - "id": "pyup.io-61883", - "more_info_path": "/vulnerabilities/CVE-2022-32214/61883", + "cve": "CVE-2022-32215", + "id": "pyup.io-61884", + "more_info_path": "/vulnerabilities/CVE-2022-32215/61884", "specs": [ "<0.5.0" ], @@ -57490,9 +57868,9 @@ }, { "advisory": "Httptools 0.5.0 updates the bundled 'llhttp' library to v6.0.9 to include security fixes.", - "cve": "CVE-2022-32215", - "id": "pyup.io-61884", - "more_info_path": "/vulnerabilities/CVE-2022-32215/61884", + "cve": "CVE-2022-32214", + "id": "pyup.io-61883", + "more_info_path": "/vulnerabilities/CVE-2022-32214/61883", "specs": [ "<0.5.0" ], @@ -57535,20 +57913,20 @@ ], "hubitatmaker": [ { - "advisory": "Hubitatmaker 0.5.4 updates its dependency 'pyyaml' to v5.4 to include a security fix.", - "cve": "CVE-2020-14343", - "id": "pyup.io-40101", - "more_info_path": "/vulnerabilities/CVE-2020-14343/40101", + "advisory": "Hubitatmaker 0.5.4 updates its dependency 'aiohttp' to v3.7.4 to include a security fix.", + "cve": "CVE-2021-21330", + "id": "pyup.io-44626", + "more_info_path": "/vulnerabilities/CVE-2021-21330/44626", "specs": [ "<0.5.4" ], "v": "<0.5.4" }, { - "advisory": "Hubitatmaker 0.5.4 updates its dependency 'aiohttp' to v3.7.4 to include a security fix.", - "cve": "CVE-2021-21330", - "id": "pyup.io-44626", - "more_info_path": "/vulnerabilities/CVE-2021-21330/44626", + "advisory": "Hubitatmaker 0.5.4 updates its dependency 'pyyaml' to v5.4 to include a security fix.", + "cve": "CVE-2020-14343", + "id": "pyup.io-40101", + "more_info_path": "/vulnerabilities/CVE-2020-14343/40101", "specs": [ "<0.5.4" ], @@ -58218,9 +58596,9 @@ }, { "advisory": "Imageedit 2021 updates its dependency 'pillow' to version >= 8.1.1 to include security fixes.", - "cve": "CVE-2020-35655", - "id": "pyup.io-42640", - "more_info_path": "/vulnerabilities/CVE-2020-35655/42640", + "cve": "CVE-2020-35654", + "id": "pyup.io-42636", + "more_info_path": "/vulnerabilities/CVE-2020-35654/42636", "specs": [ "<2021" ], @@ -58228,9 +58606,9 @@ }, { "advisory": "Imageedit 2021 updates its dependency 'pillow' to version >= 8.1.1 to include security fixes.", - "cve": "CVE-2020-35654", - "id": "pyup.io-42636", - "more_info_path": "/vulnerabilities/CVE-2020-35654/42636", + "cve": "CVE-2020-35655", + "id": "pyup.io-42640", + "more_info_path": "/vulnerabilities/CVE-2020-35655/42640", "specs": [ "<2021" ], @@ -58523,19 +58901,19 @@ }, { "advisory": "Indico 2.2.8 updates its dependency 'bleach' to v3.1.4 to include security fixes.", - "cve": "CVE-2020-6816", - "id": "pyup.io-43467", - "more_info_path": "/vulnerabilities/CVE-2020-6816/43467", + "cve": "CVE-2020-6817", + "id": "pyup.io-43466", + "more_info_path": "/vulnerabilities/CVE-2020-6817/43466", "specs": [ "<2.2.8" ], "v": "<2.2.8" }, { - "advisory": "Indico 2.2.8 updates its dependency 'pillow' to v6.2.2 to include security fixes.", - "cve": "CVE-2020-5312", - "id": "pyup.io-43463", - "more_info_path": "/vulnerabilities/CVE-2020-5312/43463", + "advisory": "Indico 2.2.8 updates its dependency 'bleach' to v3.1.4 to include security fixes.", + "cve": "CVE-2020-6816", + "id": "pyup.io-43467", + "more_info_path": "/vulnerabilities/CVE-2020-6816/43467", "specs": [ "<2.2.8" ], @@ -58543,9 +58921,9 @@ }, { "advisory": "Indico 2.2.8 updates its dependency 'pillow' to v6.2.2 to include security fixes.", - "cve": "CVE-2019-19911", - "id": "pyup.io-43465", - "more_info_path": "/vulnerabilities/CVE-2019-19911/43465", + "cve": "CVE-2020-5310", + "id": "pyup.io-38163", + "more_info_path": "/vulnerabilities/CVE-2020-5310/38163", "specs": [ "<2.2.8" ], @@ -58553,9 +58931,9 @@ }, { "advisory": "Indico 2.2.8 updates its dependency 'pillow' to v6.2.2 to include security fixes.", - "cve": "CVE-2020-5310", - "id": "pyup.io-38163", - "more_info_path": "/vulnerabilities/CVE-2020-5310/38163", + "cve": "CVE-2020-5311", + "id": "pyup.io-43462", + "more_info_path": "/vulnerabilities/CVE-2020-5311/43462", "specs": [ "<2.2.8" ], @@ -58563,9 +58941,9 @@ }, { "advisory": "Indico 2.2.8 updates its dependency 'pillow' to v6.2.2 to include security fixes.", - "cve": "CVE-2020-5311", - "id": "pyup.io-43462", - "more_info_path": "/vulnerabilities/CVE-2020-5311/43462", + "cve": "CVE-2019-19911", + "id": "pyup.io-43465", + "more_info_path": "/vulnerabilities/CVE-2019-19911/43465", "specs": [ "<2.2.8" ], @@ -58582,10 +58960,10 @@ "v": "<2.2.8" }, { - "advisory": "Indico 2.2.8 updates its dependency 'bleach' to v3.1.4 to include security fixes.", - "cve": "CVE-2020-6817", - "id": "pyup.io-43466", - "more_info_path": "/vulnerabilities/CVE-2020-6817/43466", + "advisory": "Indico 2.2.8 updates its dependency 'pillow' to v6.2.2 to include security fixes.", + "cve": "CVE-2020-5312", + "id": "pyup.io-43463", + "more_info_path": "/vulnerabilities/CVE-2020-5312/43463", "specs": [ "<2.2.8" ], @@ -58651,6 +59029,16 @@ ], "v": "<3.0rc1" }, + { + "advisory": "Indico 3.2.3 updates its dependency 'werkzeug' to include a security fix.", + "cve": "CVE-2023-25577", + "id": "pyup.io-53451", + "more_info_path": "/vulnerabilities/CVE-2023-25577/53451", + "specs": [ + "<3.2.3" + ], + "v": "<3.2.3" + }, { "advisory": "Indico 3.2.3 updates its dependency 'cryptography ' to include a security fix.", "cve": "CVE-2023-0286", @@ -58671,16 +59059,6 @@ ], "v": "<3.2.3" }, - { - "advisory": "Indico 3.2.3 updates its dependency 'werkzeug' to include a security fix.", - "cve": "CVE-2023-25577", - "id": "pyup.io-53451", - "more_info_path": "/vulnerabilities/CVE-2023-25577/53451", - "specs": [ - "<3.2.3" - ], - "v": "<3.2.3" - }, { "advisory": "Indico 3.2.5 includes a fix for a XSS vulnerability.\r\nhttps://github.com/indico/indico/pull/5818", "cve": "PVE-2023-59202", @@ -58971,10 +59349,10 @@ "v": "<1.3.2" }, { - "advisory": "Influxable 1.3.2 updates to 'python:3.9.2-buster' in Dockerfile to include security fixes.", - "cve": "CVE-2020-26116", - "id": "pyup.io-50310", - "more_info_path": "/vulnerabilities/CVE-2020-26116/50310", + "advisory": "Influxable 1.3.2 updates its dependency 'jinja2' to v3.1.2 to include a security fix.", + "cve": "CVE-2020-28493", + "id": "pyup.io-50293", + "more_info_path": "/vulnerabilities/CVE-2020-28493/50293", "specs": [ "<1.3.2" ], @@ -58990,6 +59368,16 @@ ], "v": "<1.3.2" }, + { + "advisory": "Influxable 1.3.2 updates to 'python:3.9.2-buster' in Dockerfile to include security fixes.", + "cve": "CVE-2020-8492", + "id": "pyup.io-50307", + "more_info_path": "/vulnerabilities/CVE-2020-8492/50307", + "specs": [ + "<1.3.2" + ], + "v": "<1.3.2" + }, { "advisory": "Influxable 1.3.2 updates to 'python:3.9.2-buster' in Dockerfile to include security fixes.", "cve": "CVE-2019-9948", @@ -59002,19 +59390,19 @@ }, { "advisory": "Influxable 1.3.2 updates to 'python:3.9.2-buster' in Dockerfile to include security fixes.", - "cve": "CVE-2020-8492", - "id": "pyup.io-50307", - "more_info_path": "/vulnerabilities/CVE-2020-8492/50307", + "cve": "CVE-2020-27619", + "id": "pyup.io-50323", + "more_info_path": "/vulnerabilities/CVE-2020-27619/50323", "specs": [ "<1.3.2" ], "v": "<1.3.2" }, { - "advisory": "Influxable 1.3.2 updates its dependency 'jinja2' to v3.1.2 to include a security fix.", - "cve": "CVE-2020-28493", - "id": "pyup.io-50293", - "more_info_path": "/vulnerabilities/CVE-2020-28493/50293", + "advisory": "Influxable 1.3.2 updates to 'python:3.9.2-buster' in Dockerfile to include security fixes.", + "cve": "CVE-2020-26116", + "id": "pyup.io-50310", + "more_info_path": "/vulnerabilities/CVE-2020-26116/50310", "specs": [ "<1.3.2" ], @@ -59049,16 +59437,6 @@ "<1.3.2" ], "v": "<1.3.2" - }, - { - "advisory": "Influxable 1.3.2 updates to 'python:3.9.2-buster' in Dockerfile to include security fixes.", - "cve": "CVE-2020-27619", - "id": "pyup.io-50323", - "more_info_path": "/vulnerabilities/CVE-2020-27619/50323", - "specs": [ - "<1.3.2" - ], - "v": "<1.3.2" } ], "infobip-api-python-sdk": [ @@ -59401,10 +59779,10 @@ "v": "<1.15.3,>=2.0.0a0,<2.0.2,>=2.1.0rc0,<2.1.1" }, { - "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15206: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's \"SavedModel\" protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using \"tensorflow-serving\" or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d. However, this was not enough, as #41097 reported a different failure mode. The issue was finally patched in commit df095206f25471e864a8e63a0f1caef53a0e3a6", - "cve": "CVE-2020-15206", - "id": "pyup.io-57035", - "more_info_path": "/vulnerabilities/CVE-2020-15206/57035", + "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15203: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the 'fill' argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a 'printf' call is constructed. This may result in segmentation fault.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79", + "cve": "CVE-2020-15203", + "id": "pyup.io-57042", + "more_info_path": "/vulnerabilities/CVE-2020-15203/57042", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -59415,10 +59793,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15209: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a \"nullptr\" buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one. The runtime assumes that these buffers are written to before a possible read, hence they are initialized with \"nullptr\". However, by changing the buffer index for a tensor and implicitly converting that tensor to be a read-write one, as there is nothing in the model that writes to it, we get a null pointer dereference. The issue was patched in commit 0b5662bc.", - "cve": "CVE-2020-15209", - "id": "pyup.io-57040", - "more_info_path": "/vulnerabilities/CVE-2020-15209/57040", + "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15190: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the \"tf.raw_ops.Switch\" operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. However, the eager runtime traverses all tensors in the output. Since only one of the tensors is defined, the other one is \"nullptr\", hence we are binding a reference to \"nullptr\". This is undefined behavior and reported as an error if compiling with \"-fsanitize=null\". In this case, this results in a segmentation fault The issue was patched in commit da8558533d925694483d2c136a9220d6d49d843c\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4g9f-63rx-5cw4", + "cve": "CVE-2020-15190", + "id": "pyup.io-57038", + "more_info_path": "/vulnerabilities/CVE-2020-15190/57038", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -59429,10 +59807,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15203: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the 'fill' argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a 'printf' call is constructed. This may result in segmentation fault.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79", - "cve": "CVE-2020-15203", - "id": "pyup.io-57042", - "more_info_path": "/vulnerabilities/CVE-2020-15203/57042", + "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15209: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a \"nullptr\" buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one. The runtime assumes that these buffers are written to before a possible read, hence they are initialized with \"nullptr\". However, by changing the buffer index for a tensor and implicitly converting that tensor to be a read-write one, as there is nothing in the model that writes to it, we get a null pointer dereference. The issue was patched in commit 0b5662bc.", + "cve": "CVE-2020-15209", + "id": "pyup.io-57040", + "more_info_path": "/vulnerabilities/CVE-2020-15209/57040", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -59443,10 +59821,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15208: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a \"DCHECK\" which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue was patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d", - "cve": "CVE-2020-15208", - "id": "pyup.io-57037", - "more_info_path": "/vulnerabilities/CVE-2020-15208/57037", + "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15207: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses 'ResolveAxis' to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the 'DCHECK' does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-q4qf-3fc6-8x34", + "cve": "CVE-2020-15207", + "id": "pyup.io-57043", + "more_info_path": "/vulnerabilities/CVE-2020-15207/57043", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -59457,10 +59835,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15202: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the 'Shard' API in TensorFlow expects the last argument to be a function taking two 'int64' (i.e., 'long long') arguments. However, there are several places in TensorFlow where a lambda taking 'int' or 'int32' arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6fg-mjxg-hqq4", - "cve": "CVE-2020-15202", - "id": "pyup.io-57039", - "more_info_path": "/vulnerabilities/CVE-2020-15202/57039", + "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15206: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's \"SavedModel\" protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using \"tensorflow-serving\" or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d. However, this was not enough, as #41097 reported a different failure mode. The issue was finally patched in commit df095206f25471e864a8e63a0f1caef53a0e3a6", + "cve": "CVE-2020-15206", + "id": "pyup.io-57035", + "more_info_path": "/vulnerabilities/CVE-2020-15206/57035", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -59471,10 +59849,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15204: In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling \"tf.raw_ops.GetSessionHandle\" or \"tf.raw_ops.GetSessionHandleV2\" results in a null pointer dereference In linked snippet, in eager mode, \"ctx->session_state()\" returns \"nullptr\". Since code immediately dereferences this, we get a segmentation fault. The issue was patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1", - "cve": "CVE-2020-15204", - "id": "pyup.io-57033", - "more_info_path": "/vulnerabilities/CVE-2020-15204/57033", + "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15205: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the 'data_splits' argument of 'tf.raw_ops.StringNGrams' lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after 'ee ff' are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g7p5-5759-qv46", + "cve": "CVE-2020-15205", + "id": "pyup.io-57041", + "more_info_path": "/vulnerabilities/CVE-2020-15205/57041", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -59485,10 +59863,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15195: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of 'SparseFillEmptyRowsGrad' uses a double indexing pattern. It is possible for 'reverse_index_map(i)' to be an index outside of bounds of 'grad_values', thus resulting in a heap buffer overflow.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-63xm-rx5p-xvqr", - "cve": "CVE-2020-15195", - "id": "pyup.io-57034", - "more_info_path": "/vulnerabilities/CVE-2020-15195/57034", + "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15204: In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling \"tf.raw_ops.GetSessionHandle\" or \"tf.raw_ops.GetSessionHandleV2\" results in a null pointer dereference In linked snippet, in eager mode, \"ctx->session_state()\" returns \"nullptr\". Since code immediately dereferences this, we get a segmentation fault. The issue was patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1", + "cve": "CVE-2020-15204", + "id": "pyup.io-57033", + "more_info_path": "/vulnerabilities/CVE-2020-15204/57033", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -59499,10 +59877,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15211: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative \"-1\" value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the \"-1\" index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue was patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83). A potential workaround would be to add a custom \"Verifier\" to the model loading code to ensure that only operators which accept optional inputs use the \"-1\" special value and only for the tensors that they expect to be optional. Since this allow-list type approach is error-prone, it's advised upgrading to the patched code.", - "cve": "CVE-2020-15211", - "id": "pyup.io-57036", - "more_info_path": "/vulnerabilities/CVE-2020-15211/57036", + "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15202: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the 'Shard' API in TensorFlow expects the last argument to be a function taking two 'int64' (i.e., 'long long') arguments. However, there are several places in TensorFlow where a lambda taking 'int' or 'int32' arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6fg-mjxg-hqq4", + "cve": "CVE-2020-15202", + "id": "pyup.io-57039", + "more_info_path": "/vulnerabilities/CVE-2020-15202/57039", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -59513,10 +59891,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15190: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the \"tf.raw_ops.Switch\" operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. However, the eager runtime traverses all tensors in the output. Since only one of the tensors is defined, the other one is \"nullptr\", hence we are binding a reference to \"nullptr\". This is undefined behavior and reported as an error if compiling with \"-fsanitize=null\". In this case, this results in a segmentation fault The issue was patched in commit da8558533d925694483d2c136a9220d6d49d843c\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4g9f-63rx-5cw4", - "cve": "CVE-2020-15190", - "id": "pyup.io-57038", - "more_info_path": "/vulnerabilities/CVE-2020-15190/57038", + "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15195: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of 'SparseFillEmptyRowsGrad' uses a double indexing pattern. It is possible for 'reverse_index_map(i)' to be an index outside of bounds of 'grad_values', thus resulting in a heap buffer overflow.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-63xm-rx5p-xvqr", + "cve": "CVE-2020-15195", + "id": "pyup.io-57034", + "more_info_path": "/vulnerabilities/CVE-2020-15195/57034", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -59527,10 +59905,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15207: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses 'ResolveAxis' to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the 'DCHECK' does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-q4qf-3fc6-8x34", - "cve": "CVE-2020-15207", - "id": "pyup.io-57043", - "more_info_path": "/vulnerabilities/CVE-2020-15207/57043", + "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15211: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative \"-1\" value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the \"-1\" index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue was patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83). A potential workaround would be to add a custom \"Verifier\" to the model loading code to ensure that only operators which accept optional inputs use the \"-1\" special value and only for the tensors that they expect to be optional. Since this allow-list type approach is error-prone, it's advised upgrading to the patched code.", + "cve": "CVE-2020-15211", + "id": "pyup.io-57036", + "more_info_path": "/vulnerabilities/CVE-2020-15211/57036", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -59541,10 +59919,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15205: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the 'data_splits' argument of 'tf.raw_ops.StringNGrams' lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after 'ee ff' are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g7p5-5759-qv46", - "cve": "CVE-2020-15205", - "id": "pyup.io-57041", - "more_info_path": "/vulnerabilities/CVE-2020-15205/57041", + "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15208: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a \"DCHECK\" which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue was patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d", + "cve": "CVE-2020-15208", + "id": "pyup.io-57037", + "more_info_path": "/vulnerabilities/CVE-2020-15208/57037", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -59567,19 +59945,6 @@ ], "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0rc0,<2.1.2,>=2.2.0rc0,<2.2.1" }, - { - "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency \"SQLite\" to handle CVE-2020-13630.", - "cve": "CVE-2020-13630", - "id": "pyup.io-57024", - "more_info_path": "/vulnerabilities/CVE-2020-13630/57024", - "specs": [ - "<1.15.4", - ">=2.0.0a0,<2.0.3", - ">=2.1.0rc0,<2.1.2", - ">=2.2.0rc0,<2.2.1" - ], - "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0rc0,<2.1.2,>=2.2.0rc0,<2.2.1" - }, { "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 update its dependency \"SQLite\" to handle CVE-2020-11656.", "cve": "CVE-2020-11656", @@ -59645,6 +60010,19 @@ ], "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0rc0,<2.1.2,>=2.2.0rc0,<2.2.1" }, + { + "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency \"SQLite\" to handle CVE-2020-13630.", + "cve": "CVE-2020-13630", + "id": "pyup.io-57024", + "more_info_path": "/vulnerabilities/CVE-2020-13630/57024", + "specs": [ + "<1.15.4", + ">=2.0.0a0,<2.0.3", + ">=2.1.0rc0,<2.1.2", + ">=2.2.0rc0,<2.2.1" + ], + "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0rc0,<2.1.2,>=2.2.0rc0,<2.2.1" + }, { "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2 and 2.2.1 updates its dependency \"SQLite\" to handle CVE-2020-13871.", "cve": "CVE-2020-13871", @@ -59659,10 +60037,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0rc0,<2.1.2,>=2.2.0rc0,<2.2.1" }, { - "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15210: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x9j7-x98r-r4w2", - "cve": "CVE-2020-15210", - "id": "pyup.io-57031", - "more_info_path": "/vulnerabilities/CVE-2020-15210/57031", + "advisory": "Intel-tensorflow 2.4.0 includes a fix for CVE-2020-15194: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.\"", + "cve": "CVE-2020-15194", + "id": "pyup.io-57032", + "more_info_path": "/vulnerabilities/CVE-2020-15194/57032", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -59673,10 +60051,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0rc0,<2.1.2,>=2.2.0rc0,<2.2.1,>=2.3.0rc0,<2.3.1" }, { - "advisory": "Intel-tensorflow 2.4.0 includes a fix for CVE-2020-15194: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.\"", - "cve": "CVE-2020-15194", - "id": "pyup.io-57032", - "more_info_path": "/vulnerabilities/CVE-2020-15194/57032", + "advisory": "Intel-tensorflow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15210: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x9j7-x98r-r4w2", + "cve": "CVE-2020-15210", + "id": "pyup.io-57031", + "more_info_path": "/vulnerabilities/CVE-2020-15210/57031", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -59687,10 +60065,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0rc0,<2.1.2,>=2.2.0rc0,<2.2.1,>=2.3.0rc0,<2.3.1" }, { - "advisory": "Intel-tensorflow versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 update its dependency \"PCRE\" to fix CVE-2019-20838.", - "cve": "CVE-2019-20838", - "id": "pyup.io-57009", - "more_info_path": "/vulnerabilities/CVE-2019-20838/57009", + "advisory": "Intel-tensorflow versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 updates its dependency \"Libjpeg-turbo\" to handle CVE-2020-13790.", + "cve": "CVE-2020-13790", + "id": "pyup.io-57012", + "more_info_path": "/vulnerabilities/CVE-2020-13790/57012", "specs": [ "<1.15.5", ">=2.0.0a0,<2.0.4", @@ -59701,10 +60079,10 @@ "v": "<1.15.5,>=2.0.0a0,<2.0.4,>=2.1.0rc0,<2.1.3,>=2.2.0rc0,<2.2.2,>=2.3.0rc0,<2.3.2" }, { - "advisory": "Intel-tensorflow versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 update its dependency 'Junit4' to v4.13.1 to include a security fix.", - "cve": "CVE-2020-15250", - "id": "pyup.io-57010", - "more_info_path": "/vulnerabilities/CVE-2020-15250/57010", + "advisory": "Intel-tensorflow versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 update its dependency \"PCRE\" to fix CVE-2019-20838.", + "cve": "CVE-2019-20838", + "id": "pyup.io-57009", + "more_info_path": "/vulnerabilities/CVE-2019-20838/57009", "specs": [ "<1.15.5", ">=2.0.0a0,<2.0.4", @@ -59715,10 +60093,10 @@ "v": "<1.15.5,>=2.0.0a0,<2.0.4,>=2.1.0rc0,<2.1.3,>=2.2.0rc0,<2.2.2,>=2.3.0rc0,<2.3.2" }, { - "advisory": "Intel-tensorflow versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 updates its dependency \"Libjpeg-turbo\" to handle CVE-2020-13790.", - "cve": "CVE-2020-13790", - "id": "pyup.io-57012", - "more_info_path": "/vulnerabilities/CVE-2020-13790/57012", + "advisory": "Intel-tensorflow versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 update its dependency 'Junit4' to v4.13.1 to include a security fix.", + "cve": "CVE-2020-15250", + "id": "pyup.io-57010", + "more_info_path": "/vulnerabilities/CVE-2020-15250/57010", "specs": [ "<1.15.5", ">=2.0.0a0,<2.0.4", @@ -59818,10 +60196,10 @@ "v": "<1.15.5,>=2.0.0a0,<2.0.4,>=2.1.0rc0,<2.1.3,>=2.2.0rc0,<2.2.2,>=2.3.0rc0,<2.3.2,>=2.4.0rc0,<2.4.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25676: When running versions prior to 2.12.0 and 2.11.1 with XLA, 'tf.raw_ops.ParallelConcat' segfaults with a nullptr dereference when given a parameter 'shape' with rank that is not greater than zero.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6wfh-89q8-44jq", - "cve": "CVE-2023-25676", - "id": "pyup.io-56601", - "more_info_path": "/vulnerabilities/CVE-2023-25676/56601", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-27579: Constructing a tflite model with a paramater 'filter_input_channel' of less than 1 gives a FPE.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5w96-866f-6rm8", + "cve": "CVE-2023-27579", + "id": "pyup.io-56609", + "more_info_path": "/vulnerabilities/CVE-2023-27579/56609", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -59840,10 +60218,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25670: Versions prior to 2.12.0 and 2.11.1 have a null point error in QuantizedMatMulWithBiasAndDequantize with MKL enabled.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rq-hwc3-x77w", - "cve": "CVE-2023-25670", - "id": "pyup.io-56611", - "more_info_path": "/vulnerabilities/CVE-2023-25670/56611", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25676: When running versions prior to 2.12.0 and 2.11.1 with XLA, 'tf.raw_ops.ParallelConcat' segfaults with a nullptr dereference when given a parameter 'shape' with rank that is not greater than zero.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6wfh-89q8-44jq", + "cve": "CVE-2023-25676", + "id": "pyup.io-56601", + "more_info_path": "/vulnerabilities/CVE-2023-25676/56601", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -59851,10 +60229,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25673: Versions prior to 2.12.0 and 2.11.1 have a Floating Point Exception in TensorListSplit with XLA. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.", - "cve": "CVE-2023-25673", - "id": "pyup.io-56599", - "more_info_path": "/vulnerabilities/CVE-2023-25673/56599", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25674: Versions prior to 2.12.0 and 2.11.1 have a null pointer error in RandomShuffle with XLA enabled.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf97-q72m-7579", + "cve": "CVE-2023-25674", + "id": "pyup.io-56604", + "more_info_path": "/vulnerabilities/CVE-2023-25674/56604", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -59862,10 +60240,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25661: In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the 'Convolution3DTranspose' function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a 'Convolution3DTranspose' call.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq", - "cve": "CVE-2023-25661", - "id": "pyup.io-56605", - "more_info_path": "/vulnerabilities/CVE-2023-25661/56605", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25673: Versions prior to 2.12.0 and 2.11.1 have a Floating Point Exception in TensorListSplit with XLA. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.", + "cve": "CVE-2023-25673", + "id": "pyup.io-56599", + "more_info_path": "/vulnerabilities/CVE-2023-25673/56599", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -59884,10 +60262,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25674: Versions prior to 2.12.0 and 2.11.1 have a null pointer error in RandomShuffle with XLA enabled.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf97-q72m-7579", - "cve": "CVE-2023-25674", - "id": "pyup.io-56604", - "more_info_path": "/vulnerabilities/CVE-2023-25674/56604", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25667: Prior to versions 2.12.0 and 2.11.1, integer overflow occurs when '2^31 <= num_frames * height * width * channels < 2^32', for example Full HD screencast of at least 346 frames.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fqm2-gh8w-gr68", + "cve": "CVE-2023-25667", + "id": "pyup.io-56603", + "more_info_path": "/vulnerabilities/CVE-2023-25667/56603", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -59906,10 +60284,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25666: Prior to versions 2.12.0 and 2.11.1, there is a floating point exception in AudioSpectrogram. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f637-vh3r-vfh2", - "cve": "CVE-2023-25666", - "id": "pyup.io-56602", - "more_info_path": "/vulnerabilities/CVE-2023-25666/56602", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25664: Prior to versions 2.12.0 and 2.11.1, there is a heap buffer overflow in TAvgPoolGrad.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6hg6-5c2q-7rcr", + "cve": "CVE-2023-25664", + "id": "pyup.io-56614", + "more_info_path": "/vulnerabilities/CVE-2023-25664/56614", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -59917,10 +60295,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25658: Prior to versions 2.12.0 and 2.11.1, an out of bounds read is in GRUBlockCellGrad.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-68v3-g9cm-rmm6", - "cve": "CVE-2023-25658", - "id": "pyup.io-56619", - "more_info_path": "/vulnerabilities/CVE-2023-25658/56619", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25662: Versions prior to 2.12.0 and 2.11.1 are vulnerable to integer overflow in EditDistance.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7jvm-xxmr-v5cw", + "cve": "CVE-2023-25662", + "id": "pyup.io-56616", + "more_info_path": "/vulnerabilities/CVE-2023-25662/56616", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -59928,10 +60306,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25675: When running versions prior to 2.12.0 and 2.11.1 with XLA, 'tf.raw_ops.Bincount' segfaults when given a parameter 'weights' that is neither the same shape as parameter 'arr' nor a length-0 tensor.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7x4v-9gxg-9hwj", - "cve": "CVE-2023-25675", - "id": "pyup.io-56607", - "more_info_path": "/vulnerabilities/CVE-2023-25675/56607", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25660: Prior to versions 2.12.0 and 2.11.1, when the parameter 'summarize' of 'tf.raw_ops.Print' is zero, the new method 'SummarizeArray' will reference to a nullptr, leading to a seg fault.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qjqc-vqcf-5qvj", + "cve": "CVE-2023-25660", + "id": "pyup.io-56617", + "more_info_path": "/vulnerabilities/CVE-2023-25660/56617", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -59950,10 +60328,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25668: Attackers using Tensorflow prior to 2.12.0 or 2.11.1 can access heap memory which is not in the control of user, leading to a crash or remote code execution.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gw97-ff7c-9v96", - "cve": "CVE-2023-25668", - "id": "pyup.io-56613", - "more_info_path": "/vulnerabilities/CVE-2023-25668/56613", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25670: Versions prior to 2.12.0 and 2.11.1 have a null point error in QuantizedMatMulWithBiasAndDequantize with MKL enabled.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rq-hwc3-x77w", + "cve": "CVE-2023-25670", + "id": "pyup.io-56611", + "more_info_path": "/vulnerabilities/CVE-2023-25670/56611", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -59961,10 +60339,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25672: The function 'tf.raw_ops.LookupTableImportV2' cannot handle scalars in the 'values' parameter and gives an NPE. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.", - "cve": "CVE-2023-25672", - "id": "pyup.io-56600", - "more_info_path": "/vulnerabilities/CVE-2023-25672/56600", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25661: In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the 'Convolution3DTranspose' function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a 'Convolution3DTranspose' call.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq", + "cve": "CVE-2023-25661", + "id": "pyup.io-56605", + "more_info_path": "/vulnerabilities/CVE-2023-25661/56605", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -59972,10 +60350,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25669: Prior to versions 2.12.0 and 2.11.1, if the stride and window size are not positive for 'tf.raw_ops.AvgPoolGrad', it can give a floating point exception.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rcf8-g8jv-vg6p", - "cve": "CVE-2023-25669", - "id": "pyup.io-56612", - "more_info_path": "/vulnerabilities/CVE-2023-25669/56612", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25666: Prior to versions 2.12.0 and 2.11.1, there is a floating point exception in AudioSpectrogram. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f637-vh3r-vfh2", + "cve": "CVE-2023-25666", + "id": "pyup.io-56602", + "more_info_path": "/vulnerabilities/CVE-2023-25666/56602", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -59983,10 +60361,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-27579: Constructing a tflite model with a paramater 'filter_input_channel' of less than 1 gives a FPE.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5w96-866f-6rm8", - "cve": "CVE-2023-27579", - "id": "pyup.io-56609", - "more_info_path": "/vulnerabilities/CVE-2023-27579/56609", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25658: Prior to versions 2.12.0 and 2.11.1, an out of bounds read is in GRUBlockCellGrad.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-68v3-g9cm-rmm6", + "cve": "CVE-2023-25658", + "id": "pyup.io-56619", + "more_info_path": "/vulnerabilities/CVE-2023-25658/56619", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -59994,10 +60372,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25667: Prior to versions 2.12.0 and 2.11.1, integer overflow occurs when '2^31 <= num_frames * height * width * channels < 2^32', for example Full HD screencast of at least 346 frames.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fqm2-gh8w-gr68", - "cve": "CVE-2023-25667", - "id": "pyup.io-56603", - "more_info_path": "/vulnerabilities/CVE-2023-25667/56603", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25675: When running versions prior to 2.12.0 and 2.11.1 with XLA, 'tf.raw_ops.Bincount' segfaults when given a parameter 'weights' that is neither the same shape as parameter 'arr' nor a length-0 tensor.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7x4v-9gxg-9hwj", + "cve": "CVE-2023-25675", + "id": "pyup.io-56607", + "more_info_path": "/vulnerabilities/CVE-2023-25675/56607", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -60005,10 +60383,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25664: Prior to versions 2.12.0 and 2.11.1, there is a heap buffer overflow in TAvgPoolGrad.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6hg6-5c2q-7rcr", - "cve": "CVE-2023-25664", - "id": "pyup.io-56614", - "more_info_path": "/vulnerabilities/CVE-2023-25664/56614", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25668: Attackers using Tensorflow prior to 2.12.0 or 2.11.1 can access heap memory which is not in the control of user, leading to a crash or remote code execution.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gw97-ff7c-9v96", + "cve": "CVE-2023-25668", + "id": "pyup.io-56613", + "more_info_path": "/vulnerabilities/CVE-2023-25668/56613", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -60016,10 +60394,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25662: Versions prior to 2.12.0 and 2.11.1 are vulnerable to integer overflow in EditDistance.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7jvm-xxmr-v5cw", - "cve": "CVE-2023-25662", - "id": "pyup.io-56616", - "more_info_path": "/vulnerabilities/CVE-2023-25662/56616", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25672: The function 'tf.raw_ops.LookupTableImportV2' cannot handle scalars in the 'values' parameter and gives an NPE. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.", + "cve": "CVE-2023-25672", + "id": "pyup.io-56600", + "more_info_path": "/vulnerabilities/CVE-2023-25672/56600", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -60027,10 +60405,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25660: Prior to versions 2.12.0 and 2.11.1, when the parameter 'summarize' of 'tf.raw_ops.Print' is zero, the new method 'SummarizeArray' will reference to a nullptr, leading to a seg fault.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qjqc-vqcf-5qvj", - "cve": "CVE-2023-25660", - "id": "pyup.io-56617", - "more_info_path": "/vulnerabilities/CVE-2023-25660/56617", + "advisory": "Intel-tensorflow 2.11.1 and 2.12.0 include a fix for CVE-2023-25669: Prior to versions 2.12.0 and 2.11.1, if the stride and window size are not positive for 'tf.raw_ops.AvgPoolGrad', it can give a floating point exception.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rcf8-g8jv-vg6p", + "cve": "CVE-2023-25669", + "id": "pyup.io-56612", + "more_info_path": "/vulnerabilities/CVE-2023-25669/56612", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -60070,20 +60448,20 @@ "v": "<2.12.1,>=2.13.0rc0,<2.13.0" }, { - "advisory": "intel-tensorflow updates its curl dependency from version 8.2.1 to 8.4.0 to address CVE-2023-38546.", - "cve": "CVE-2023-38546", - "id": "pyup.io-73086", - "more_info_path": "/vulnerabilities/CVE-2023-38546/73086", + "advisory": "Intel-tensorflow 2.14.0 updates its curl dependency from version 8.2.1 to 8.4.0 to address CVE-2023-38545.", + "cve": "CVE-2023-38545", + "id": "pyup.io-73085", + "more_info_path": "/vulnerabilities/CVE-2023-38545/73085", "specs": [ "<2.14.0" ], "v": "<2.14.0" }, { - "advisory": "Intel-tensorflow 2.14.0 updates its curl dependency from version 8.2.1 to 8.4.0 to address CVE-2023-38545.", - "cve": "CVE-2023-38545", - "id": "pyup.io-73085", - "more_info_path": "/vulnerabilities/CVE-2023-38545/73085", + "advisory": "intel-tensorflow updates its curl dependency from version 8.2.1 to 8.4.0 to address CVE-2023-38546.", + "cve": "CVE-2023-38546", + "id": "pyup.io-73086", + "more_info_path": "/vulnerabilities/CVE-2023-38546/73086", "specs": [ "<2.14.0" ], @@ -60100,10 +60478,10 @@ "v": "<2.4.0" }, { - "advisory": "Intel-tensorflow 2.4.0 includes a fix for CVE-2020-15265: In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.", - "cve": "CVE-2020-15265", - "id": "pyup.io-57018", - "more_info_path": "/vulnerabilities/CVE-2020-15265/57018", + "advisory": "Intel-tensorflow 2.4.0 includes a fix for CVE-2020-15266: In Tensorflow before version 2.4.0, when the 'boxes' argument of 'tf.image.crop_and_resize' has a very large value, the CPU kernel implementation receives it as a C++ 'nan' floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault.\nhttps://github.com/tensorflow/tensorflow/issues/42129\nhttps://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xwhf-g6j5-j5gc", + "cve": "CVE-2020-15266", + "id": "pyup.io-57019", + "more_info_path": "/vulnerabilities/CVE-2020-15266/57019", "specs": [ "<2.4.0" ], @@ -60112,28 +60490,28 @@ { "advisory": "Intel-tensorflow 2.4.0 includes a fix for CVE-2020-15265: In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.", "cve": "CVE-2020-15265", - "id": "pyup.io-57021", - "more_info_path": "/vulnerabilities/CVE-2020-15265/57021", + "id": "pyup.io-57018", + "more_info_path": "/vulnerabilities/CVE-2020-15265/57018", "specs": [ "<2.4.0" ], "v": "<2.4.0" }, { - "advisory": "Intel-tensorflow 2.4.0 includes a fix for CVE-2020-15266: In Tensorflow before version 2.4.0, when the 'boxes' argument of 'tf.image.crop_and_resize' has a very large value, the CPU kernel implementation receives it as a C++ 'nan' floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault.\nhttps://github.com/tensorflow/tensorflow/issues/42129\nhttps://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xwhf-g6j5-j5gc", - "cve": "CVE-2020-15266", - "id": "pyup.io-57019", - "more_info_path": "/vulnerabilities/CVE-2020-15266/57019", + "advisory": "Intel-tensorflow 2.4.0 includes a fix for CVE-2020-15265: In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.", + "cve": "CVE-2020-15265", + "id": "pyup.io-57021", + "more_info_path": "/vulnerabilities/CVE-2020-15265/57021", "specs": [ "<2.4.0" ], "v": "<2.4.0" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41205: In affected versions, the shape inference functions for the 'QuantizeAndDequantizeV*' operations can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rx-x2rw-pc6f\nhttps://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d", - "cve": "CVE-2021-41205", - "id": "pyup.io-56812", - "more_info_path": "/vulnerabilities/CVE-2021-41205/56812", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22926.", + "cve": "CVE-2021-22926", + "id": "pyup.io-56807", + "more_info_path": "/vulnerabilities/CVE-2021-22926/56807", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60142,10 +60520,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41209: In affected versions, the implementations for convolution operators trigger a division by 0 if passed empty filter tensor arguments. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6hpv-v2rx-c5g6\nhttps://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235", - "cve": "CVE-2021-41209", - "id": "pyup.io-56804", - "more_info_path": "/vulnerabilities/CVE-2021-41209/56804", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22924.", + "cve": "CVE-2021-22924", + "id": "pyup.io-56815", + "more_info_path": "/vulnerabilities/CVE-2021-22924/56815", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60154,10 +60532,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41210: In affected versions, the shape inference functions for 'SparseCountSparseOutput' can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m342-ff57-4jcc\r\nhttps://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2", - "cve": "CVE-2021-41210", - "id": "pyup.io-56831", - "more_info_path": "/vulnerabilities/CVE-2021-41210/56831", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22922.", + "cve": "CVE-2021-22922", + "id": "pyup.io-56799", + "more_info_path": "/vulnerabilities/CVE-2021-22922/56799", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60166,10 +60544,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41215: In affected versions, the shape inference code for 'DeserializeSparse' can trigger a null pointer dereference. This is because the shape inference function assumes that the 'serialize_sparse' tensor is a tensor with positive rank (and having '3' as the last dimension). The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x3v8-c8qx-3j3r\nhttps://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850", - "cve": "CVE-2021-41215", - "id": "pyup.io-56808", - "more_info_path": "/vulnerabilities/CVE-2021-41215/56808", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41205: In affected versions, the shape inference functions for the 'QuantizeAndDequantizeV*' operations can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rx-x2rw-pc6f\nhttps://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d", + "cve": "CVE-2021-41205", + "id": "pyup.io-56812", + "more_info_path": "/vulnerabilities/CVE-2021-41205/56812", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60178,10 +60556,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22923.", - "cve": "CVE-2021-22923", - "id": "pyup.io-56816", - "more_info_path": "/vulnerabilities/CVE-2021-22923/56816", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41224: In affected versions, the implementation of 'SparseFillEmptyRows' can be made to trigger a heap OOB access. This occurs whenever the size of 'indices' does not match the size of 'values'. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rg3m-hqc5-344v\nhttps://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b", + "cve": "CVE-2021-41224", + "id": "pyup.io-56810", + "more_info_path": "/vulnerabilities/CVE-2021-41224/56810", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60190,10 +60568,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22926.", - "cve": "CVE-2021-22926", - "id": "pyup.io-56807", - "more_info_path": "/vulnerabilities/CVE-2021-22926/56807", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41212: In affected versions, the shape inference code for 'tf.ragged.cross' can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fr77-rrx3-cp7g\nhttps://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8", + "cve": "CVE-2021-41212", + "id": "pyup.io-56828", + "more_info_path": "/vulnerabilities/CVE-2021-41212/56828", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60202,10 +60580,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22922.", - "cve": "CVE-2021-22922", - "id": "pyup.io-56799", - "more_info_path": "/vulnerabilities/CVE-2021-22922/56799", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41223: In affected versions, the implementation of 'FusedBatchNorm' kernels is vulnerable to a heap OOB access. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f54p-f6jp-4rhr\nhttps://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda", + "cve": "CVE-2021-41223", + "id": "pyup.io-56809", + "more_info_path": "/vulnerabilities/CVE-2021-41223/56809", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60214,10 +60592,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41198: In affected versions, if 'tf.tile' is called with a large input argument, then the TensorFlow process will crash due to a 'CHECK'-failure caused by an overflow. The number of elements in the output tensor is too much for the 'int64_t' type and the overflow is detected via a 'CHECK' statement. This aborts the process. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-2p25-55c9-h58q", - "cve": "CVE-2021-41198", - "id": "pyup.io-56830", - "more_info_path": "/vulnerabilities/CVE-2021-41198/56830", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41218: In affected versions, the shape inference code for 'AllToAll' can be made to execute a division by 0. This occurs whenever the 'split_count' argument is 0. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9crf-c6qr-r273\nhttps://github.com/tensorflow/tensorflow/commit/a8ad3e5e79c75f36edb81e0ba3f3c0c5442aeddc", + "cve": "CVE-2021-41218", + "id": "pyup.io-56801", + "more_info_path": "/vulnerabilities/CVE-2021-41218/56801", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60226,10 +60604,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41224: In affected versions, the implementation of 'SparseFillEmptyRows' can be made to trigger a heap OOB access. This occurs whenever the size of 'indices' does not match the size of 'values'. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rg3m-hqc5-344v\nhttps://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b", - "cve": "CVE-2021-41224", - "id": "pyup.io-56810", - "more_info_path": "/vulnerabilities/CVE-2021-41224/56810", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41203: In affected versions, an attacker can trigger undefined behavior, integer overflows, segfaults and 'CHECK'-fail crashes if they can change saved checkpoints from outside of TensorFlow. This is because the checkpoints loading infrastructure is missing validation for invalid file formats.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7pxj-m4jf-r6h2", + "cve": "CVE-2021-41203", + "id": "pyup.io-56823", + "more_info_path": "/vulnerabilities/CVE-2021-41203/56823", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60238,10 +60616,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41226: In affected versions, the implementation of 'SparseBinCount' is vulnerable to a heap OOB access. This is because of missing validation between the elements of the 'values' argument and the shape of the sparse output. The fix is also included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-374m-jm66-3vj8\nhttps://github.com/tensorflow/tensorflow/commit/f410212e373eb2aec4c9e60bf3702eba99a38aba", - "cve": "CVE-2021-41226", - "id": "pyup.io-56821", - "more_info_path": "/vulnerabilities/CVE-2021-41226/56821", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41221: In affected versions, the shape inference code for the 'Cudnn*' operations can be tricked into accessing invalid memory via a heap buffer overflow. This occurs because the ranks of the 'input', 'input_h' and 'input_c' parameters are not validated, but code assumes they have certain values. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqv6-3phm-hcwx\nhttps://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6", + "cve": "CVE-2021-41221", + "id": "pyup.io-56827", + "more_info_path": "/vulnerabilities/CVE-2021-41221/56827", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60250,10 +60628,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41225: In affected versions, TensorFlow's Grappler optimizer has a use of unitialized variable. If the 'train_nodes' vector (obtained from the saved model that gets optimized) does not contain a 'Dequeue' node, then 'dequeue_node' is left unitialized. The fix is also included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7r94-xv9v-63jw\nhttps://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3", - "cve": "CVE-2021-41225", - "id": "pyup.io-56829", - "more_info_path": "/vulnerabilities/CVE-2021-41225/56829", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41200: In affected versions, if 'tf.summary.create_file_writer' is called with non-scalar arguments, code crashes due to a 'CHECK'-fail. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gh8h-7j2j-qv4f", + "cve": "CVE-2021-41200", + "id": "pyup.io-56826", + "more_info_path": "/vulnerabilities/CVE-2021-41200/56826", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60262,10 +60640,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41212: In affected versions, the shape inference code for 'tf.ragged.cross' can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fr77-rrx3-cp7g\nhttps://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8", - "cve": "CVE-2021-41212", - "id": "pyup.io-56828", - "more_info_path": "/vulnerabilities/CVE-2021-41212/56828", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41195: In affected versions, the implementation of 'tf.math.segment_*' operations results in a 'CHECK'-fail related abort (and denial of service) if a segment id in 'segment_ids' is large. This is similar to CVE-2021-29584 (and similar to other reported vulnerabilities in TensorFlow localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using 'AddDim'. However, if the number of elements in the tensor overflows an 'int64_t' value, 'AddDim' results in a 'CHECK' failure which provokes a 'std::abort'. Instead, code should use 'AddDimWithStatus'. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cq76-mxrc-vchh", + "cve": "CVE-2021-41195", + "id": "pyup.io-56805", + "more_info_path": "/vulnerabilities/CVE-2021-41195/56805", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60274,10 +60652,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41214: In affected versions, the shape inference code for 'tf.ragged.cross' has an undefined behavior due to binding a reference to 'nullptr'. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vwhq-49r4-gj9v\nhttps://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8", - "cve": "CVE-2021-41214", - "id": "pyup.io-56813", - "more_info_path": "/vulnerabilities/CVE-2021-41214/56813", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41207: In affected versions, the implementation of 'ParallelConcat' misses some input validation and can produce a division by 0. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7v94-64hj-m82h\nhttps://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235", + "cve": "CVE-2021-41207", + "id": "pyup.io-56818", + "more_info_path": "/vulnerabilities/CVE-2021-41207/56818", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60286,10 +60664,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41223: In affected versions, the implementation of 'FusedBatchNorm' kernels is vulnerable to a heap OOB access. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f54p-f6jp-4rhr\nhttps://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda", - "cve": "CVE-2021-41223", - "id": "pyup.io-56809", - "more_info_path": "/vulnerabilities/CVE-2021-41223/56809", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41204: In affected versions, during TensorFlow's Grappler optimizer phase, constant folding might attempt to deep copy a resource tensor. This results in a segfault, as these tensors are supposed to not change. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-786j-5qwq-r36x\nhttps://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659", + "cve": "CVE-2021-41204", + "id": "pyup.io-56820", + "more_info_path": "/vulnerabilities/CVE-2021-41204/56820", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60298,10 +60676,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41217: In affected versions, the process of building the control flow graph for a TensorFlow model is vulnerable to a null pointer exception when nodes that should be paired are not. This occurs because the code assumes that the first node in the pairing (e.g., an 'Enter' node) always exists when encountering the second node (e.g., an 'Exit' node). When this is not the case, 'parent' is 'nullptr' so dereferencing it causes a crash. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5crj-c72x-m7gq\nhttps://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff", - "cve": "CVE-2021-41217", - "id": "pyup.io-56811", - "more_info_path": "/vulnerabilities/CVE-2021-41217/56811", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41219: In affected versions, the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to 'nullptr'. This occurs whenever the dimensions of 'a' or 'b' are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, it should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4f99-p9c2-3j8x\nhttps://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae", + "cve": "CVE-2021-41219", + "id": "pyup.io-56814", + "more_info_path": "/vulnerabilities/CVE-2021-41219/56814", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60310,10 +60688,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41218: In affected versions, the shape inference code for 'AllToAll' can be made to execute a division by 0. This occurs whenever the 'split_count' argument is 0. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9crf-c6qr-r273\nhttps://github.com/tensorflow/tensorflow/commit/a8ad3e5e79c75f36edb81e0ba3f3c0c5442aeddc", - "cve": "CVE-2021-41218", - "id": "pyup.io-56801", - "more_info_path": "/vulnerabilities/CVE-2021-41218/56801", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41210: In affected versions, the shape inference functions for 'SparseCountSparseOutput' can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m342-ff57-4jcc\r\nhttps://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2", + "cve": "CVE-2021-41210", + "id": "pyup.io-56831", + "more_info_path": "/vulnerabilities/CVE-2021-41210/56831", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60322,10 +60700,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41228: In affected versions, TensorFlow's 'saved_model_cli' tool is vulnerable to a code injection as it calls 'eval' on user supplied strings. This can be used by attackers to run arbitrary code on the plaform where the CLI tool runs. However, given that the tool is always run manually, the impact of this is not severe. The issue has been patched by adding a 'safe' flag which defaults to 'True' and an explicit warning for users.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-3rcw-9p9x-582v\nhttps://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7", - "cve": "CVE-2021-41228", - "id": "pyup.io-56806", - "more_info_path": "/vulnerabilities/CVE-2021-41228/56806", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41225: In affected versions, TensorFlow's Grappler optimizer has a use of unitialized variable. If the 'train_nodes' vector (obtained from the saved model that gets optimized) does not contain a 'Dequeue' node, then 'dequeue_node' is left unitialized. The fix is also included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7r94-xv9v-63jw\nhttps://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3", + "cve": "CVE-2021-41225", + "id": "pyup.io-56829", + "more_info_path": "/vulnerabilities/CVE-2021-41225/56829", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60334,10 +60712,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41207: In affected versions, the implementation of 'ParallelConcat' misses some input validation and can produce a division by 0. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7v94-64hj-m82h\nhttps://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235", - "cve": "CVE-2021-41207", - "id": "pyup.io-56818", - "more_info_path": "/vulnerabilities/CVE-2021-41207/56818", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41222: In affected versions, the implementation of 'SplitV' can trigger a segfault if an attacker supplies negative arguments. This occurs whenever 'size_splits' contains more than one value and at least one value is negative. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cpf4-wx82-gxp6\nhttps://github.com/tensorflow/tensorflow/commit/25d622ffc432acc736b14ca3904177579e733cc6", + "cve": "CVE-2021-41222", + "id": "pyup.io-56825", + "more_info_path": "/vulnerabilities/CVE-2021-41222/56825", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60346,10 +60724,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41204: In affected versions, during TensorFlow's Grappler optimizer phase, constant folding might attempt to deep copy a resource tensor. This results in a segfault, as these tensors are supposed to not change. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-786j-5qwq-r36x\nhttps://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659", - "cve": "CVE-2021-41204", - "id": "pyup.io-56820", - "more_info_path": "/vulnerabilities/CVE-2021-41204/56820", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22923.", + "cve": "CVE-2021-22923", + "id": "pyup.io-56816", + "more_info_path": "/vulnerabilities/CVE-2021-22923/56816", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60358,10 +60736,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41213: In affected versions, the code behind 'tf.function' API can be made to deadlock when two 'tf.function' decorated Python functions are mutually recursive. This occurs due to using a non-reentrant 'Lock' Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive 'tf.function', although this is not a frequent scenario.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf\nhttps://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7", - "cve": "CVE-2021-41213", - "id": "pyup.io-56824", - "more_info_path": "/vulnerabilities/CVE-2021-41213/56824", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41201: In affected versions, during execution, 'EinsumHelper::ParseEquation()' is supposed to set the flags in 'input_has_ellipsis' vector and '*output_has_ellipsis' boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to 'true' and never assigns 'false'. This results in unitialized variable access if callers assume that 'EinsumHelper::ParseEquation()' always sets these flags. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-j86v-p27c-73fm\nhttps://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6", + "cve": "CVE-2021-41201", + "id": "pyup.io-56803", + "more_info_path": "/vulnerabilities/CVE-2021-41201/56803", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60370,10 +60748,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41222: In affected versions, the implementation of 'SplitV' can trigger a segfault if an attacker supplies negative arguments. This occurs whenever 'size_splits' contains more than one value and at least one value is negative. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cpf4-wx82-gxp6\nhttps://github.com/tensorflow/tensorflow/commit/25d622ffc432acc736b14ca3904177579e733cc6", - "cve": "CVE-2021-41222", - "id": "pyup.io-56825", - "more_info_path": "/vulnerabilities/CVE-2021-41222/56825", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41226: In affected versions, the implementation of 'SparseBinCount' is vulnerable to a heap OOB access. This is because of missing validation between the elements of the 'values' argument and the shape of the sparse output. The fix is also included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-374m-jm66-3vj8\nhttps://github.com/tensorflow/tensorflow/commit/f410212e373eb2aec4c9e60bf3702eba99a38aba", + "cve": "CVE-2021-41226", + "id": "pyup.io-56821", + "more_info_path": "/vulnerabilities/CVE-2021-41226/56821", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60382,10 +60760,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41200: In affected versions, if 'tf.summary.create_file_writer' is called with non-scalar arguments, code crashes due to a 'CHECK'-fail. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gh8h-7j2j-qv4f", - "cve": "CVE-2021-41200", - "id": "pyup.io-56826", - "more_info_path": "/vulnerabilities/CVE-2021-41200/56826", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41214: In affected versions, the shape inference code for 'tf.ragged.cross' has an undefined behavior due to binding a reference to 'nullptr'. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vwhq-49r4-gj9v\nhttps://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8", + "cve": "CVE-2021-41214", + "id": "pyup.io-56813", + "more_info_path": "/vulnerabilities/CVE-2021-41214/56813", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60394,10 +60772,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41221: In affected versions, the shape inference code for the 'Cudnn*' operations can be tricked into accessing invalid memory via a heap buffer overflow. This occurs because the ranks of the 'input', 'input_h' and 'input_c' parameters are not validated, but code assumes they have certain values. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqv6-3phm-hcwx\nhttps://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6", - "cve": "CVE-2021-41221", - "id": "pyup.io-56827", - "more_info_path": "/vulnerabilities/CVE-2021-41221/56827", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41228: In affected versions, TensorFlow's 'saved_model_cli' tool is vulnerable to a code injection as it calls 'eval' on user supplied strings. This can be used by attackers to run arbitrary code on the plaform where the CLI tool runs. However, given that the tool is always run manually, the impact of this is not severe. The issue has been patched by adding a 'safe' flag which defaults to 'True' and an explicit warning for users.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-3rcw-9p9x-582v\nhttps://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7", + "cve": "CVE-2021-41228", + "id": "pyup.io-56806", + "more_info_path": "/vulnerabilities/CVE-2021-41228/56806", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60406,10 +60784,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41203: In affected versions, an attacker can trigger undefined behavior, integer overflows, segfaults and 'CHECK'-fail crashes if they can change saved checkpoints from outside of TensorFlow. This is because the checkpoints loading infrastructure is missing validation for invalid file formats.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7pxj-m4jf-r6h2", - "cve": "CVE-2021-41203", - "id": "pyup.io-56823", - "more_info_path": "/vulnerabilities/CVE-2021-41203/56823", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41227: In affected versions, the 'ImmutableConst' operation in TensorFlow can be tricked into reading arbitrary memory contents. This is because the 'tstring' TensorFlow string class has a special case for memory mapped strings but the operation itself does not offer any support for this datatype. The fix is also included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8c8-67vp-6mx7\nhttps://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b\nhttps://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585", + "cve": "CVE-2021-41227", + "id": "pyup.io-56800", + "more_info_path": "/vulnerabilities/CVE-2021-41227/56800", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60418,10 +60796,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41196: In affected versions, the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m539-j985-hcr8", - "cve": "CVE-2021-41196", - "id": "pyup.io-56802", - "more_info_path": "/vulnerabilities/CVE-2021-41196/56802", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41216: In affected versions, the shape inference function for 'Transpose' is vulnerable to a heap buffer overflow. This occurs whenever 'perm' contains negative elements. The shape inference function does not validate that the indices in 'perm' are all valid. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-3ff2-r28g-w7h9\nhttps://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14", + "cve": "CVE-2021-41216", + "id": "pyup.io-56817", + "more_info_path": "/vulnerabilities/CVE-2021-41216/56817", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60430,10 +60808,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41195: In affected versions, the implementation of 'tf.math.segment_*' operations results in a 'CHECK'-fail related abort (and denial of service) if a segment id in 'segment_ids' is large. This is similar to CVE-2021-29584 (and similar to other reported vulnerabilities in TensorFlow localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using 'AddDim'. However, if the number of elements in the tensor overflows an 'int64_t' value, 'AddDim' results in a 'CHECK' failure which provokes a 'std::abort'. Instead, code should use 'AddDimWithStatus'. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cq76-mxrc-vchh", - "cve": "CVE-2021-41195", - "id": "pyup.io-56805", - "more_info_path": "/vulnerabilities/CVE-2021-41195/56805", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41213: In affected versions, the code behind 'tf.function' API can be made to deadlock when two 'tf.function' decorated Python functions are mutually recursive. This occurs due to using a non-reentrant 'Lock' Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive 'tf.function', although this is not a frequent scenario.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf\nhttps://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7", + "cve": "CVE-2021-41213", + "id": "pyup.io-56824", + "more_info_path": "/vulnerabilities/CVE-2021-41213/56824", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60442,10 +60820,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41216: In affected versions, the shape inference function for 'Transpose' is vulnerable to a heap buffer overflow. This occurs whenever 'perm' contains negative elements. The shape inference function does not validate that the indices in 'perm' are all valid. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-3ff2-r28g-w7h9\nhttps://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14", - "cve": "CVE-2021-41216", - "id": "pyup.io-56817", - "more_info_path": "/vulnerabilities/CVE-2021-41216/56817", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41202: In affected versions, while calculating the size of the output within the 'tf.range' kernel, there is a conditional statement of type 'int64 = condition ? int64 : double'. Due to C++ implicit conversion rules, both branches of the condition will be cast to 'double' and the result would be truncated before the assignment. This result in overflows. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xrqm-fpgr-6hhx", + "cve": "CVE-2021-41202", + "id": "pyup.io-56819", + "more_info_path": "/vulnerabilities/CVE-2021-41202/56819", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60454,10 +60832,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41202: In affected versions, while calculating the size of the output within the 'tf.range' kernel, there is a conditional statement of type 'int64 = condition ? int64 : double'. Due to C++ implicit conversion rules, both branches of the condition will be cast to 'double' and the result would be truncated before the assignment. This result in overflows. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xrqm-fpgr-6hhx", - "cve": "CVE-2021-41202", - "id": "pyup.io-56819", - "more_info_path": "/vulnerabilities/CVE-2021-41202/56819", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41217: In affected versions, the process of building the control flow graph for a TensorFlow model is vulnerable to a null pointer exception when nodes that should be paired are not. This occurs because the code assumes that the first node in the pairing (e.g., an 'Enter' node) always exists when encountering the second node (e.g., an 'Exit' node). When this is not the case, 'parent' is 'nullptr' so dereferencing it causes a crash. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5crj-c72x-m7gq\nhttps://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff", + "cve": "CVE-2021-41217", + "id": "pyup.io-56811", + "more_info_path": "/vulnerabilities/CVE-2021-41217/56811", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60466,10 +60844,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41199: In affected versions, if 'tf.image.resize' is called with a large input argument then the TensorFlow process will crash due to a 'CHECK'-failure caused by an overflow. The number of elements in the output tensor is too much for the 'int64_t' type and the overflow is detected via a 'CHECK' statement. This aborts the process. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hx2-qx8j-qjqm", - "cve": "CVE-2021-41199", - "id": "pyup.io-56822", - "more_info_path": "/vulnerabilities/CVE-2021-41199/56822", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41209: In affected versions, the implementations for convolution operators trigger a division by 0 if passed empty filter tensor arguments. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6hpv-v2rx-c5g6\nhttps://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235", + "cve": "CVE-2021-41209", + "id": "pyup.io-56804", + "more_info_path": "/vulnerabilities/CVE-2021-41209/56804", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60478,10 +60856,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41227: In affected versions, the 'ImmutableConst' operation in TensorFlow can be tricked into reading arbitrary memory contents. This is because the 'tstring' TensorFlow string class has a special case for memory mapped strings but the operation itself does not offer any support for this datatype. The fix is also included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8c8-67vp-6mx7\nhttps://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b\nhttps://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585", - "cve": "CVE-2021-41227", - "id": "pyup.io-56800", - "more_info_path": "/vulnerabilities/CVE-2021-41227/56800", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41215: In affected versions, the shape inference code for 'DeserializeSparse' can trigger a null pointer dereference. This is because the shape inference function assumes that the 'serialize_sparse' tensor is a tensor with positive rank (and having '3' as the last dimension). The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x3v8-c8qx-3j3r\nhttps://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850", + "cve": "CVE-2021-41215", + "id": "pyup.io-56808", + "more_info_path": "/vulnerabilities/CVE-2021-41215/56808", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60490,10 +60868,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41201: In affected versions, during execution, 'EinsumHelper::ParseEquation()' is supposed to set the flags in 'input_has_ellipsis' vector and '*output_has_ellipsis' boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to 'true' and never assigns 'false'. This results in unitialized variable access if callers assume that 'EinsumHelper::ParseEquation()' always sets these flags. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-j86v-p27c-73fm\nhttps://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6", - "cve": "CVE-2021-41201", - "id": "pyup.io-56803", - "more_info_path": "/vulnerabilities/CVE-2021-41201/56803", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41199: In affected versions, if 'tf.image.resize' is called with a large input argument then the TensorFlow process will crash due to a 'CHECK'-failure caused by an overflow. The number of elements in the output tensor is too much for the 'int64_t' type and the overflow is detected via a 'CHECK' statement. This aborts the process. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hx2-qx8j-qjqm", + "cve": "CVE-2021-41199", + "id": "pyup.io-56822", + "more_info_path": "/vulnerabilities/CVE-2021-41199/56822", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60502,10 +60880,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41219: In affected versions, the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to 'nullptr'. This occurs whenever the dimensions of 'a' or 'b' are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, it should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4f99-p9c2-3j8x\nhttps://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae", - "cve": "CVE-2021-41219", - "id": "pyup.io-56814", - "more_info_path": "/vulnerabilities/CVE-2021-41219/56814", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41198: In affected versions, if 'tf.tile' is called with a large input argument, then the TensorFlow process will crash due to a 'CHECK'-failure caused by an overflow. The number of elements in the output tensor is too much for the 'int64_t' type and the overflow is detected via a 'CHECK' statement. This aborts the process. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-2p25-55c9-h58q", + "cve": "CVE-2021-41198", + "id": "pyup.io-56830", + "more_info_path": "/vulnerabilities/CVE-2021-41198/56830", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60514,10 +60892,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22925.", - "cve": "CVE-2021-22925", - "id": "pyup.io-56798", - "more_info_path": "/vulnerabilities/CVE-2021-22925/56798", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41196: In affected versions, the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m539-j985-hcr8", + "cve": "CVE-2021-41196", + "id": "pyup.io-56802", + "more_info_path": "/vulnerabilities/CVE-2021-41196/56802", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60526,10 +60904,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22924.", - "cve": "CVE-2021-22924", - "id": "pyup.io-56815", - "more_info_path": "/vulnerabilities/CVE-2021-22924/56815", + "advisory": "Intel-tensorflow versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22925.", + "cve": "CVE-2021-22925", + "id": "pyup.io-56798", + "more_info_path": "/vulnerabilities/CVE-2021-22925/56798", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -60576,10 +60954,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21730: The implementation of 'FractionalAvgPoolGrad' does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vjg4-v33c-ggc4", - "cve": "CVE-2022-21730", - "id": "pyup.io-56749", - "more_info_path": "/vulnerabilities/CVE-2022-21730/56749", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23565: An attacker can trigger denial of service via assertion failure by altering a 'SavedModel' on disk such that 'AttrDef's of some operation are duplicated.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4v5p-v5h9-6xjx", + "cve": "CVE-2022-23565", + "id": "pyup.io-56782", + "more_info_path": "/vulnerabilities/CVE-2022-23565/56782", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60589,10 +60967,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23564: When decoding a resource handle tensor from protobuf, a TensorFlow process can encounter cases where a 'CHECK' assertion is invalidated based on user controlled arguments. This allows attackers to cause denial of services in TensorFlow processes.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-8rcj-c8pj-v3m3", - "cve": "CVE-2022-23564", - "id": "pyup.io-56774", - "more_info_path": "/vulnerabilities/CVE-2022-23564/56774", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23561: An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c78-vcq7-7vxq", + "cve": "CVE-2022-23561", + "id": "pyup.io-56796", + "more_info_path": "/vulnerabilities/CVE-2022-23561/56796", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60602,10 +60980,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23565: An attacker can trigger denial of service via assertion failure by altering a 'SavedModel' on disk such that 'AttrDef's of some operation are duplicated.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4v5p-v5h9-6xjx", - "cve": "CVE-2022-23565", - "id": "pyup.io-56782", - "more_info_path": "/vulnerabilities/CVE-2022-23565/56782", + "advisory": "Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a 'SavedModel' such that any binary op would trigger 'CHECK' failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the 'dtype' no longer matches the 'dtype' expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved. If 'Tin' and 'Tout' don't match the type of data in 'out' and 'input_*' tensors then 'flat<*>' would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a 'CHECK' crash, hence a denial of service. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23583", + "id": "pyup.io-56757", + "more_info_path": "/vulnerabilities/CVE-2022-23583/56757", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60615,10 +60993,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. There is a typo in TensorFlow's `SpecializeType` which results in heap OOB read/write. Due to a typo, `arg` is initialized to the `i`th mutable argument in a loop where the loop index is `j`. Hence it is possible to assign to `arg` from outside the vector of arguments. Since this is a mutable proto value, it allows both read and write to outside of bounds data. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23574", - "id": "pyup.io-56763", - "more_info_path": "/vulnerabilities/CVE-2022-23574/56763", + "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. If a graph node is invalid, TensorFlow can leak memory in the implementation of `ImmutableExecutorState::Initialize`. Here, we set `item->kernel` to `nullptr` but it is a simple `OpKernel*` pointer so the memory that was previously allocated to it would leak. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23578", + "id": "pyup.io-56759", + "more_info_path": "/vulnerabilities/CVE-2022-23578/56759", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60628,10 +61006,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23582: A malicious user can cause a denial of service by altering a 'SavedModel' such that 'TensorByteSize' would trigger 'CHECK' failures. 'TensorShape' constructor throws a 'CHECK'-fail if shape is partial or has a number of elements that would overflow the size of an 'int'. The 'PartialTensorShape' constructor instead does not cause a 'CHECK'-abort if the shape is partial, which is exactly what this function needs to be able to return '-1'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4j82-5ccr-4r8v", - "cve": "CVE-2022-23582", - "id": "pyup.io-56794", - "more_info_path": "/vulnerabilities/CVE-2022-23582/56794", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23558: An attacker can craft a TFLite model that would cause an integer overflow in 'TfLiteIntArrayCreate'. The 'TfLiteIntArrayGetSizeInBytes' returns an 'int' instead of a 'size_t'. An attacker can control model inputs such that 'computed_size' overflows the size of 'int' datatype.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9gwq-6cwj-47h3", + "cve": "CVE-2022-23558", + "id": "pyup.io-56766", + "more_info_path": "/vulnerabilities/CVE-2022-23558/56766", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60641,10 +61019,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23561: An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c78-vcq7-7vxq", - "cve": "CVE-2022-23561", - "id": "pyup.io-56796", - "more_info_path": "/vulnerabilities/CVE-2022-23561/56796", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21735: The implementation of 'FractionalMaxPool' can be made to crash a TensorFlow process via a division by 0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-87v6-crgm-2gfj", + "cve": "CVE-2022-21735", + "id": "pyup.io-56779", + "more_info_path": "/vulnerabilities/CVE-2022-21735/56779", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60654,10 +61032,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21725: The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f", - "cve": "CVE-2022-21725", - "id": "pyup.io-56778", - "more_info_path": "/vulnerabilities/CVE-2022-21725/56778", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21740: The implementation of 'SparseCountSparseOutput' is vulnerable to a heap overflow.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-44qp-9wwf-734r", + "cve": "CVE-2022-21740", + "id": "pyup.io-56787", + "more_info_path": "/vulnerabilities/CVE-2022-21740/56787", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60667,10 +61045,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a `SavedModel` file (fixing the first one would trigger the same dereference in the second place). First, during constant folding, the `GraphDef` might not have the required nodes for the binary operation. If a node is missing, the correposning `mul_*child` would be null, and the dereference in the subsequent line would be incorrect. We have a similar issue during `IsIdentityConsumingSwitch`. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23589", - "id": "pyup.io-56760", - "more_info_path": "/vulnerabilities/CVE-2022-23589/56760", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21732: The implementation of 'ThreadPoolHandle' can be used to trigger a denial of service attack by allocating too much memory. This is because the 'num_threads' argument is only checked to not be negative, but there is no upper bound on its value.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-c582-c96p-r5cq", + "cve": "CVE-2022-21732", + "id": "pyup.io-56786", + "more_info_path": "/vulnerabilities/CVE-2022-21732/56786", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60680,10 +61058,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23580: During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-627q-g293-49q7", - "cve": "CVE-2022-23580", - "id": "pyup.io-56783", - "more_info_path": "/vulnerabilities/CVE-2022-23580/56783", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23560: An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvf-hxvg-f67v", + "cve": "CVE-2022-23560", + "id": "pyup.io-56776", + "more_info_path": "/vulnerabilities/CVE-2022-23560/56776", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60693,10 +61071,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, TensorFlow might do a null-dereference if attributes of some mutable arguments to some operations are missing from the proto. This is guarded by a `DCHECK`. However, `DCHECK` is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the dereferencing of the null pointer, whereas in the second case it results in a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23570", - "id": "pyup.io-56753", - "more_info_path": "/vulnerabilities/CVE-2022-23570/56753", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23567: The implementations of 'Sparse*Cwise*' ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or 'CHECK'-fails when building new 'TensorShape' objects (so, assert failures based denial of service). There are missing some validation on the shapes of the input tensors as well as directly constructing a large 'TensorShape' with user-provided dimensions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrx2-r989-2c43", + "cve": "CVE-2022-23567", + "id": "pyup.io-56767", + "more_info_path": "/vulnerabilities/CVE-2022-23567/56767", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60706,10 +61084,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. TensorFlow is vulnerable to a heap OOB write in `Grappler`. The `set_output` function writes to an array at the specified index. Hence, this gives a malicious user a write primitive. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23566", - "id": "pyup.io-56788", - "more_info_path": "/vulnerabilities/CVE-2022-23566/56788", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21739: The implementation of 'QuantizedMaxPool' has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-3mw4-6rj6-74g5", + "cve": "CVE-2022-21739", + "id": "pyup.io-56768", + "more_info_path": "/vulnerabilities/CVE-2022-21739/56768", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60719,10 +61097,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a 'SavedModel' such that any binary op would trigger 'CHECK' failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the 'dtype' no longer matches the 'dtype' expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved. If 'Tin' and 'Tout' don't match the type of data in 'out' and 'input_*' tensors then 'flat<*>' would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a 'CHECK' crash, hence a denial of service. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23583", - "id": "pyup.io-56757", - "more_info_path": "/vulnerabilities/CVE-2022-23583/56757", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23595: When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference. In the default scenario, all devices are allowed, so 'flr->config_proto' is 'nullptr'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fpcp-9h7m-ffpx", + "cve": "CVE-2022-23595", + "id": "pyup.io-56751", + "more_info_path": "/vulnerabilities/CVE-2022-23595/56751", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60732,10 +61110,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, TensorFlow can fail to specialize a type during shape inference. This case is covered by the 'DCHECK' function however, 'DCHECK' is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the 'ValueOrDie' line. This results in an assertion failure as 'ret' contains an error 'Status', not a value. In the second case we also get a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23572", - "id": "pyup.io-56781", - "more_info_path": "/vulnerabilities/CVE-2022-23572/56781", + "advisory": "Tensorflow is an Open Source Machine Learning Framework. The 'GraphDef' format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a 'GraphDef' containing a fragment such as the following can be consumed when loading a 'SavedModel'. This would result in a stack overflow during execution as resolving each 'NodeDef' means resolving the function itself and its nodes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23591", + "id": "pyup.io-56785", + "more_info_path": "/vulnerabilities/CVE-2022-23591/56785", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60745,10 +61123,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21734: The implementation of 'MapStage' is vulnerable to a 'CHECK'-fail if the key tensor is not a scalar.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gcvh-66ff-4mwm", - "cve": "CVE-2022-21734", - "id": "pyup.io-56754", - "more_info_path": "/vulnerabilities/CVE-2022-21734/56754", + "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a `SavedModel` file (fixing the first one would trigger the same dereference in the second place). First, during constant folding, the `GraphDef` might not have the required nodes for the binary operation. If a node is missing, the correposning `mul_*child` would be null, and the dereference in the subsequent line would be incorrect. We have a similar issue during `IsIdentityConsumingSwitch`. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23589", + "id": "pyup.io-56760", + "more_info_path": "/vulnerabilities/CVE-2022-23589/56760", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60758,10 +61136,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. When decoding PNG images TensorFlow can produce a memory leak if the image is invalid. After calling `png::CommonInitDecode(..., &decode)`, the `decode` value contains allocated buffers which can only be freed by calling `png::CommonFreeDecode(&decode)`. However, several error case in the function implementation invoke the `OP_REQUIRES` macro which immediately terminates the execution of the function, without allowing for the memory free to occur. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23585", - "id": "pyup.io-56755", - "more_info_path": "/vulnerabilities/CVE-2022-23585/56755", + "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that Grappler optimizer would attempt to build a tensor using a reference `dtype`. This would result in a crash due to a `CHECK`-fail in the `Tensor` constructor as reference types are not allowed. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23588", + "id": "pyup.io-56792", + "more_info_path": "/vulnerabilities/CVE-2022-23588/56792", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60771,10 +61149,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21729: The implementation of 'UnravelIndex' is vulnerable to a division by zero caused by an integer overflow bug.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-34f9-hjfq-rr8j", - "cve": "CVE-2022-21729", - "id": "pyup.io-56770", - "more_info_path": "/vulnerabilities/CVE-2022-21729/56770", + "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23587", + "id": "pyup.io-56777", + "more_info_path": "/vulnerabilities/CVE-2022-23587/56777", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60784,10 +61162,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21738: The implementation of 'SparseCountSparseOutput' can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4qx-4fjv-hmw6", - "cve": "CVE-2022-21738", - "id": "pyup.io-56791", - "more_info_path": "/vulnerabilities/CVE-2022-21738/56791", + "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that assertions in `function.cc` would be falsified and crash the Python interpreter. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23586", + "id": "pyup.io-56771", + "more_info_path": "/vulnerabilities/CVE-2022-23586/56771", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60797,10 +61175,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23568: The implementation of 'AddManySparseToTensorsMap' is vulnerable to an integer overflow which results in a 'CHECK'-fail when building new 'TensorShape' objects (so, an assert failure based denial of service). There are missing some validation on the shapes of the input tensors as well as directly constructing a large 'TensorShape' with user-provided dimensions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6445-fm66-fvq2", - "cve": "CVE-2022-23568", - "id": "pyup.io-56746", - "more_info_path": "/vulnerabilities/CVE-2022-23568/56746", + "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. When decoding PNG images TensorFlow can produce a memory leak if the image is invalid. After calling `png::CommonInitDecode(..., &decode)`, the `decode` value contains allocated buffers which can only be freed by calling `png::CommonFreeDecode(&decode)`. However, several error case in the function implementation invoke the `OP_REQUIRES` macro which immediately terminates the execution of the function, without allowing for the memory free to occur. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23585", + "id": "pyup.io-56755", + "more_info_path": "/vulnerabilities/CVE-2022-23585/56755", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60810,10 +61188,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23595: When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference. In the default scenario, all devices are allowed, so 'flr->config_proto' is 'nullptr'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fpcp-9h7m-ffpx", - "cve": "CVE-2022-23595", - "id": "pyup.io-56751", - "more_info_path": "/vulnerabilities/CVE-2022-23595/56751", + "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a use after free behavior when decoding PNG images. After `png::CommonFreeDecode(&decode)` gets called, the values of `decode.width` and `decode.height` are in an unspecified state. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23584", + "id": "pyup.io-56793", + "more_info_path": "/vulnerabilities/CVE-2022-23584/56793", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60823,10 +61201,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. If a graph node is invalid, TensorFlow can leak memory in the implementation of `ImmutableExecutorState::Initialize`. Here, we set `item->kernel` to `nullptr` but it is a simple `OpKernel*` pointer so the memory that was previously allocated to it would leak. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23578", - "id": "pyup.io-56759", - "more_info_path": "/vulnerabilities/CVE-2022-23578/56759", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23582: A malicious user can cause a denial of service by altering a 'SavedModel' such that 'TensorByteSize' would trigger 'CHECK' failures. 'TensorShape' constructor throws a 'CHECK'-fail if shape is partial or has a number of elements that would overflow the size of an 'int'. The 'PartialTensorShape' constructor instead does not cause a 'CHECK'-abort if the shape is partial, which is exactly what this function needs to be able to return '-1'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4j82-5ccr-4r8v", + "cve": "CVE-2022-23582", + "id": "pyup.io-56794", + "more_info_path": "/vulnerabilities/CVE-2022-23582/56794", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60836,10 +61214,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21737: The implementation of '*Bincount' operations allows malicious users to cause denial of service by passing in arguments which would trigger a 'CHECK'-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in 'CHECK' failures later when the output tensors get allocated.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2vv-v9cg-qhh7", - "cve": "CVE-2022-21737", - "id": "pyup.io-56762", - "more_info_path": "/vulnerabilities/CVE-2022-21737/56762", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23581: The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a 'SavedModel' such that 'IsSimplifiableReshape' would trigger 'CHECK' failures.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fq86-3f29-px2c", + "cve": "CVE-2022-23581", + "id": "pyup.io-56761", + "more_info_path": "/vulnerabilities/CVE-2022-23581/56761", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60849,10 +61227,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23559: An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both 'embedding_size' and 'lookup_size' are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-98p5-x8x4-c9m5", - "cve": "CVE-2022-23559", - "id": "pyup.io-56769", - "more_info_path": "/vulnerabilities/CVE-2022-23559/56769", + "advisory": "Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23580: During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-627q-g293-49q7", + "cve": "CVE-2022-23580", + "id": "pyup.io-56783", + "more_info_path": "/vulnerabilities/CVE-2022-23580/56783", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60862,10 +61240,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23558: An attacker can craft a TFLite model that would cause an integer overflow in 'TfLiteIntArrayCreate'. The 'TfLiteIntArrayGetSizeInBytes' returns an 'int' instead of a 'size_t'. An attacker can control model inputs such that 'computed_size' overflows the size of 'int' datatype.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9gwq-6cwj-47h3", - "cve": "CVE-2022-23558", - "id": "pyup.io-56766", - "more_info_path": "/vulnerabilities/CVE-2022-23558/56766", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23579: The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a 'SavedModel' such that 'SafeToRemoveIdentity' would trigger 'CHECK' failures.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5f2r-qp73-37mr", + "cve": "CVE-2022-23579", + "id": "pyup.io-56789", + "more_info_path": "/vulnerabilities/CVE-2022-23579/56789", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60875,10 +61253,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a `CHECK` assertion is invalidated based on user controlled arguments, if the tensors have an invalid `dtype` and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow processes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23571", - "id": "pyup.io-56773", - "more_info_path": "/vulnerabilities/CVE-2022-23571/56773", + "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. The implementation of `GetInitOp` is vulnerable to a crash caused by dereferencing a null pointer. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23577", + "id": "pyup.io-56784", + "more_info_path": "/vulnerabilities/CVE-2022-23577/56784", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60888,10 +61266,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23557: An attacker can craft a TFLite model that would trigger a division by zero in 'BiasAndClamp' implementation. There is no check that the 'bias_size' is non zero.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf2j-f278-xh4v", - "cve": "CVE-2022-23557", - "id": "pyup.io-56780", - "more_info_path": "/vulnerabilities/CVE-2022-23557/56780", + "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateOutputSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve tensors with large enough number of elements. We can have a large enough number of dimensions in `output_shape.dim()` or just a small number of dimensions being large enough to cause an overflow in the multiplication. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23576", + "id": "pyup.io-56756", + "more_info_path": "/vulnerabilities/CVE-2022-23576/56756", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60901,10 +61279,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21735: The implementation of 'FractionalMaxPool' can be made to crash a TensorFlow process via a division by 0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-87v6-crgm-2gfj", - "cve": "CVE-2022-21735", - "id": "pyup.io-56779", - "more_info_path": "/vulnerabilities/CVE-2022-21735/56779", + "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateTensorSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23575", + "id": "pyup.io-56765", + "more_info_path": "/vulnerabilities/CVE-2022-23575/56765", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60914,10 +61292,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23562: The implementation of 'Range' suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qx3f-p745-w4hr", - "cve": "CVE-2022-23562", - "id": "pyup.io-56790", - "more_info_path": "/vulnerabilities/CVE-2022-23562/56790", + "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. There is a typo in TensorFlow's `SpecializeType` which results in heap OOB read/write. Due to a typo, `arg` is initialized to the `i`th mutable argument in a loop where the loop index is `j`. Hence it is possible to assign to `arg` from outside the vector of arguments. Since this is a mutable proto value, it allows both read and write to outside of bounds data. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23574", + "id": "pyup.io-56763", + "more_info_path": "/vulnerabilities/CVE-2022-23574/56763", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60927,10 +61305,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21740: The implementation of 'SparseCountSparseOutput' is vulnerable to a heap overflow.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-44qp-9wwf-734r", - "cve": "CVE-2022-21740", - "id": "pyup.io-56787", - "more_info_path": "/vulnerabilities/CVE-2022-21740/56787", + "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. The implementation of `AssignOp` can result in copying uninitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23573", + "id": "pyup.io-56758", + "more_info_path": "/vulnerabilities/CVE-2022-23573/56758", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60940,10 +61318,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21726: The implementation of 'Dequantize' does not fully validate the value of 'axis' and can result in heap OOB accesses. The 'axis' argument can be '-1' (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72", - "cve": "CVE-2022-21726", - "id": "pyup.io-56795", - "more_info_path": "/vulnerabilities/CVE-2022-21726/56795", + "advisory": "Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, TensorFlow can fail to specialize a type during shape inference. This case is covered by the 'DCHECK' function however, 'DCHECK' is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the 'ValueOrDie' line. This results in an assertion failure as 'ret' contains an error 'Status', not a value. In the second case we also get a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23572", + "id": "pyup.io-56781", + "more_info_path": "/vulnerabilities/CVE-2022-23572/56781", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60953,10 +61331,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. The implementation of `AssignOp` can result in copying uninitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23573", - "id": "pyup.io-56758", - "more_info_path": "/vulnerabilities/CVE-2022-23573/56758", + "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a `CHECK` assertion is invalidated based on user controlled arguments, if the tensors have an invalid `dtype` and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow processes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23571", + "id": "pyup.io-56773", + "more_info_path": "/vulnerabilities/CVE-2022-23571/56773", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60966,10 +61344,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a use after free behavior when decoding PNG images. After `png::CommonFreeDecode(&decode)` gets called, the values of `decode.width` and `decode.height` are in an unspecified state. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23584", - "id": "pyup.io-56793", - "more_info_path": "/vulnerabilities/CVE-2022-23584/56793", + "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, TensorFlow might do a null-dereference if attributes of some mutable arguments to some operations are missing from the proto. This is guarded by a `DCHECK`. However, `DCHECK` is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the dereferencing of the null pointer, whereas in the second case it results in a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23570", + "id": "pyup.io-56753", + "more_info_path": "/vulnerabilities/CVE-2022-23570/56753", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60979,10 +61357,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23569: Multiple operations in TensorFlow can be used to trigger a denial of service via 'CHECK'-fails (i.e., assertion failures). This is similar to CVE-2021-41197 and has a similar fix. It is possible that other similar instances exist.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qj5r-f9mv-rffh", - "cve": "CVE-2022-23569", - "id": "pyup.io-56764", - "more_info_path": "/vulnerabilities/CVE-2022-23569/56764", + "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. TensorFlow is vulnerable to a heap OOB write in `Grappler`. The `set_output` function writes to an array at the specified index. Hence, this gives a malicious user a write primitive. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23566", + "id": "pyup.io-56788", + "more_info_path": "/vulnerabilities/CVE-2022-23566/56788", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -60992,10 +61370,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21727: The implementation of shape inference for 'Dequantize' is vulnerable to an integer overflow weakness. The 'axis' argument can be '-1' (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes 'axis + 1', an attacker can trigger an integer overflow.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-c6fh-56w7-fvjw", - "cve": "CVE-2022-21727", - "id": "pyup.io-56772", - "more_info_path": "/vulnerabilities/CVE-2022-21727/56772", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23564: When decoding a resource handle tensor from protobuf, a TensorFlow process can encounter cases where a 'CHECK' assertion is invalidated based on user controlled arguments. This allows attackers to cause denial of services in TensorFlow processes.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-8rcj-c8pj-v3m3", + "cve": "CVE-2022-23564", + "id": "pyup.io-56774", + "more_info_path": "/vulnerabilities/CVE-2022-23564/56774", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61005,10 +61383,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23579: The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a 'SavedModel' such that 'SafeToRemoveIdentity' would trigger 'CHECK' failures.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5f2r-qp73-37mr", - "cve": "CVE-2022-23579", - "id": "pyup.io-56789", - "more_info_path": "/vulnerabilities/CVE-2022-23579/56789", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23562: The implementation of 'Range' suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qx3f-p745-w4hr", + "cve": "CVE-2022-23562", + "id": "pyup.io-56790", + "more_info_path": "/vulnerabilities/CVE-2022-23562/56790", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61018,10 +61396,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21732: The implementation of 'ThreadPoolHandle' can be used to trigger a denial of service attack by allocating too much memory. This is because the 'num_threads' argument is only checked to not be negative, but there is no upper bound on its value.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-c582-c96p-r5cq", - "cve": "CVE-2022-21732", - "id": "pyup.io-56786", - "more_info_path": "/vulnerabilities/CVE-2022-21732/56786", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23559: An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both 'embedding_size' and 'lookup_size' are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-98p5-x8x4-c9m5", + "cve": "CVE-2022-23559", + "id": "pyup.io-56769", + "more_info_path": "/vulnerabilities/CVE-2022-23559/56769", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61031,10 +61409,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23560: An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvf-hxvg-f67v", - "cve": "CVE-2022-23560", - "id": "pyup.io-56776", - "more_info_path": "/vulnerabilities/CVE-2022-23560/56776", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23557: An attacker can craft a TFLite model that would trigger a division by zero in 'BiasAndClamp' implementation. There is no check that the 'bias_size' is non zero.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf2j-f278-xh4v", + "cve": "CVE-2022-23557", + "id": "pyup.io-56780", + "more_info_path": "/vulnerabilities/CVE-2022-23557/56780", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61044,10 +61422,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateTensorSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23575", - "id": "pyup.io-56765", - "more_info_path": "/vulnerabilities/CVE-2022-23575/56765", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21741: An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-428x-9xc2-m8mj", + "cve": "CVE-2022-21741", + "id": "pyup.io-56752", + "more_info_path": "/vulnerabilities/CVE-2022-21741/56752", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61057,10 +61435,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Tensorflow is an Open Source Machine Learning Framework. The 'GraphDef' format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a 'GraphDef' containing a fragment such as the following can be consumed when loading a 'SavedModel'. This would result in a stack overflow during execution as resolving each 'NodeDef' means resolving the function itself and its nodes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23591", - "id": "pyup.io-56785", - "more_info_path": "/vulnerabilities/CVE-2022-23591/56785", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21738: The implementation of 'SparseCountSparseOutput' can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4qx-4fjv-hmw6", + "cve": "CVE-2022-21738", + "id": "pyup.io-56791", + "more_info_path": "/vulnerabilities/CVE-2022-21738/56791", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61070,10 +61448,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21736: The implementation of 'SparseTensorSliceDataset' has an undefined behavior: under certain conditions, it can be made to dereference a 'nullptr' value. The 3 input arguments to 'SparseTensorSliceDataset' represent a sparse tensor. However, there are some preconditions that these arguments must satisfy, but these are not validated in the implementation.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pfjj-m3jj-9jc9", - "cve": "CVE-2022-21736", - "id": "pyup.io-56750", - "more_info_path": "/vulnerabilities/CVE-2022-21736/56750", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21737: The implementation of '*Bincount' operations allows malicious users to cause denial of service by passing in arguments which would trigger a 'CHECK'-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in 'CHECK' failures later when the output tensors get allocated.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2vv-v9cg-qhh7", + "cve": "CVE-2022-21737", + "id": "pyup.io-56762", + "more_info_path": "/vulnerabilities/CVE-2022-21737/56762", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61083,10 +61461,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that assertions in `function.cc` would be falsified and crash the Python interpreter. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23586", - "id": "pyup.io-56771", - "more_info_path": "/vulnerabilities/CVE-2022-23586/56771", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23569: Multiple operations in TensorFlow can be used to trigger a denial of service via 'CHECK'-fails (i.e., assertion failures). This is similar to CVE-2021-41197 and has a similar fix. It is possible that other similar instances exist.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qj5r-f9mv-rffh", + "cve": "CVE-2022-23569", + "id": "pyup.io-56764", + "more_info_path": "/vulnerabilities/CVE-2022-23569/56764", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61096,10 +61474,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23567: The implementations of 'Sparse*Cwise*' ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or 'CHECK'-fails when building new 'TensorShape' objects (so, assert failures based denial of service). There are missing some validation on the shapes of the input tensors as well as directly constructing a large 'TensorShape' with user-provided dimensions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrx2-r989-2c43", - "cve": "CVE-2022-23567", - "id": "pyup.io-56767", - "more_info_path": "/vulnerabilities/CVE-2022-23567/56767", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21734: The implementation of 'MapStage' is vulnerable to a 'CHECK'-fail if the key tensor is not a scalar.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gcvh-66ff-4mwm", + "cve": "CVE-2022-21734", + "id": "pyup.io-56754", + "more_info_path": "/vulnerabilities/CVE-2022-21734/56754", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61109,10 +61487,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21731: The implementation of shape inference for 'ConcatV2' can be used to trigger a denial of service attack via a segfault caused by a type confusion. The 'axis' argument is translated into 'concat_dim' in the 'ConcatShapeHelper' helper function. Then, a value for 'min_rank' is computed based on 'concat_dim'. This is then used to validate that the 'values' tensor has at least the required rank. However, 'WithRankAtLeast' receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented. Due to the fact that 'min_rank' is a 32-bits value and the value of 'axis', the 'rank' argument is a negative value, so the error check is bypassed.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m4hf-j54p-p353", - "cve": "CVE-2022-21731", - "id": "pyup.io-56797", - "more_info_path": "/vulnerabilities/CVE-2022-21731/56797", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21729: The implementation of 'UnravelIndex' is vulnerable to a division by zero caused by an integer overflow bug.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-34f9-hjfq-rr8j", + "cve": "CVE-2022-21729", + "id": "pyup.io-56770", + "more_info_path": "/vulnerabilities/CVE-2022-21729/56770", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61122,10 +61500,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21741: An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-428x-9xc2-m8mj", - "cve": "CVE-2022-21741", - "id": "pyup.io-56752", - "more_info_path": "/vulnerabilities/CVE-2022-21741/56752", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21725: The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f", + "cve": "CVE-2022-21725", + "id": "pyup.io-56778", + "more_info_path": "/vulnerabilities/CVE-2022-21725/56778", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61135,10 +61513,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateOutputSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve tensors with large enough number of elements. We can have a large enough number of dimensions in `output_shape.dim()` or just a small number of dimensions being large enough to cause an overflow in the multiplication. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23576", - "id": "pyup.io-56756", - "more_info_path": "/vulnerabilities/CVE-2022-23576/56756", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23568: The implementation of 'AddManySparseToTensorsMap' is vulnerable to an integer overflow which results in a 'CHECK'-fail when building new 'TensorShape' objects (so, an assert failure based denial of service). There are missing some validation on the shapes of the input tensors as well as directly constructing a large 'TensorShape' with user-provided dimensions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6445-fm66-fvq2", + "cve": "CVE-2022-23568", + "id": "pyup.io-56746", + "more_info_path": "/vulnerabilities/CVE-2022-23568/56746", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61148,10 +61526,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23581: The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a 'SavedModel' such that 'IsSimplifiableReshape' would trigger 'CHECK' failures.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fq86-3f29-px2c", - "cve": "CVE-2022-23581", - "id": "pyup.io-56761", - "more_info_path": "/vulnerabilities/CVE-2022-23581/56761", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21736: The implementation of 'SparseTensorSliceDataset' has an undefined behavior: under certain conditions, it can be made to dereference a 'nullptr' value. The 3 input arguments to 'SparseTensorSliceDataset' represent a sparse tensor. However, there are some preconditions that these arguments must satisfy, but these are not validated in the implementation.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pfjj-m3jj-9jc9", + "cve": "CVE-2022-21736", + "id": "pyup.io-56750", + "more_info_path": "/vulnerabilities/CVE-2022-21736/56750", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61161,10 +61539,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23587", - "id": "pyup.io-56777", - "more_info_path": "/vulnerabilities/CVE-2022-23587/56777", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21733: The implementation of 'StringNGrams' can be used to trigger a denial of service attack by causing an out of memory condition after an integer overflow. There is missing a validation on 'pad_witdh' and that result in computing a negative value for 'ngram_width' which is later used to allocate parts of the output.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-98j8-c9q4-r38g", + "cve": "CVE-2022-21733", + "id": "pyup.io-56775", + "more_info_path": "/vulnerabilities/CVE-2022-21733/56775", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61174,10 +61552,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that Grappler optimizer would attempt to build a tensor using a reference `dtype`. This would result in a crash due to a `CHECK`-fail in the `Tensor` constructor as reference types are not allowed. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23588", - "id": "pyup.io-56792", - "more_info_path": "/vulnerabilities/CVE-2022-23588/56792", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21731: The implementation of shape inference for 'ConcatV2' can be used to trigger a denial of service attack via a segfault caused by a type confusion. The 'axis' argument is translated into 'concat_dim' in the 'ConcatShapeHelper' helper function. Then, a value for 'min_rank' is computed based on 'concat_dim'. This is then used to validate that the 'values' tensor has at least the required rank. However, 'WithRankAtLeast' receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented. Due to the fact that 'min_rank' is a 32-bits value and the value of 'axis', the 'rank' argument is a negative value, so the error check is bypassed.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m4hf-j54p-p353", + "cve": "CVE-2022-21731", + "id": "pyup.io-56797", + "more_info_path": "/vulnerabilities/CVE-2022-21731/56797", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61187,10 +61565,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21728: The implementation of shape inference for 'ReverseSequence' does not fully validate the value of 'batch_dim' and can result in a heap OOB read. There is a check to make sure the value of 'batch_dim' does not go over the rank of the input, but there is no check for negative values. Negative dimensions are allowed in some cases to mimic Python's negative indexing (i.e., indexing from the end of the array), however if the value is too negative then the implementation of 'Dim' would access elements before the start of an array.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6gmv-pjp9-p8w8", - "cve": "CVE-2022-21728", - "id": "pyup.io-56747", - "more_info_path": "/vulnerabilities/CVE-2022-21728/56747", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21730: The implementation of 'FractionalAvgPoolGrad' does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vjg4-v33c-ggc4", + "cve": "CVE-2022-21730", + "id": "pyup.io-56749", + "more_info_path": "/vulnerabilities/CVE-2022-21730/56749", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61200,10 +61578,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow is an Open Source Machine Learning Framework. The implementation of `GetInitOp` is vulnerable to a crash caused by dereferencing a null pointer. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23577", - "id": "pyup.io-56784", - "more_info_path": "/vulnerabilities/CVE-2022-23577/56784", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21728: The implementation of shape inference for 'ReverseSequence' does not fully validate the value of 'batch_dim' and can result in a heap OOB read. There is a check to make sure the value of 'batch_dim' does not go over the rank of the input, but there is no check for negative values. Negative dimensions are allowed in some cases to mimic Python's negative indexing (i.e., indexing from the end of the array), however if the value is too negative then the implementation of 'Dim' would access elements before the start of an array.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6gmv-pjp9-p8w8", + "cve": "CVE-2022-21728", + "id": "pyup.io-56747", + "more_info_path": "/vulnerabilities/CVE-2022-21728/56747", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61213,10 +61591,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21739: The implementation of 'QuantizedMaxPool' has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-3mw4-6rj6-74g5", - "cve": "CVE-2022-21739", - "id": "pyup.io-56768", - "more_info_path": "/vulnerabilities/CVE-2022-21739/56768", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21727: The implementation of shape inference for 'Dequantize' is vulnerable to an integer overflow weakness. The 'axis' argument can be '-1' (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes 'axis + 1', an attacker can trigger an integer overflow.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-c6fh-56w7-fvjw", + "cve": "CVE-2022-21727", + "id": "pyup.io-56772", + "more_info_path": "/vulnerabilities/CVE-2022-21727/56772", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61226,10 +61604,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21733: The implementation of 'StringNGrams' can be used to trigger a denial of service attack by causing an out of memory condition after an integer overflow. There is missing a validation on 'pad_witdh' and that result in computing a negative value for 'ngram_width' which is later used to allocate parts of the output.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-98j8-c9q4-r38g", - "cve": "CVE-2022-21733", - "id": "pyup.io-56775", - "more_info_path": "/vulnerabilities/CVE-2022-21733/56775", + "advisory": "Intel-tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21726: The implementation of 'Dequantize' does not fully validate the value of 'axis' and can result in heap OOB accesses. The 'axis' argument can be '-1' (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72", + "cve": "CVE-2022-21726", + "id": "pyup.io-56795", + "more_info_path": "/vulnerabilities/CVE-2022-21726/56795", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -61276,10 +61654,10 @@ "v": "<2.5.3,>=2.6.0rc0,<2.6.3,>=2.7.0rc0,<2.7.1,>=2.8.0rc0,<2.8.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29212: Core dump when loading TFLite models with quantization.", - "cve": "CVE-2022-29212", - "id": "pyup.io-56738", - "more_info_path": "/vulnerabilities/CVE-2022-29212/56738", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27779.", + "cve": "CVE-2022-27779", + "id": "pyup.io-56719", + "more_info_path": "/vulnerabilities/CVE-2022-27779/56719", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61289,10 +61667,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29202: Denial of service in 'tf.ragged.constant' due to lack of validation.", - "cve": "CVE-2022-29202", - "id": "pyup.io-56724", - "more_info_path": "/vulnerabilities/CVE-2022-29202/56724", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27775.", + "cve": "CVE-2022-27775", + "id": "pyup.io-56704", + "more_info_path": "/vulnerabilities/CVE-2022-27775/56704", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61302,10 +61680,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27775.", - "cve": "CVE-2022-27775", - "id": "pyup.io-56704", - "more_info_path": "/vulnerabilities/CVE-2022-27775/56704", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'zlib' to v1.2.12 to handle CVE-2018-25032.", + "cve": "CVE-2018-25032", + "id": "pyup.io-56722", + "more_info_path": "/vulnerabilities/CVE-2018-25032/56722", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61315,10 +61693,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29216: Code injection in 'saved_model_cli'.", - "cve": "CVE-2022-29216", - "id": "pyup.io-56705", - "more_info_path": "/vulnerabilities/CVE-2022-29216/56705", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29194: Missing validation which causes denial of service via 'DeleteSessionTensor'.", + "cve": "CVE-2022-29194", + "id": "pyup.io-56715", + "more_info_path": "/vulnerabilities/CVE-2022-29194/56715", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61328,10 +61706,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29192: missing validation which crashes 'QuantizeAndDequantizeV4Grad'.", - "cve": "CVE-2022-29192", - "id": "pyup.io-56729", - "more_info_path": "/vulnerabilities/CVE-2022-29192/56729", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29206: Missing validation which results in undefined behavior in 'SparseTensorDenseAdd'.", + "cve": "CVE-2022-29206", + "id": "pyup.io-56726", + "more_info_path": "/vulnerabilities/CVE-2022-29206/56726", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61341,10 +61719,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27781.", - "cve": "CVE-2022-27781", - "id": "pyup.io-56720", - "more_info_path": "/vulnerabilities/CVE-2022-27781/56720", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29211: Segfault when 'tf.histogram_fixed_width' is called with NaN values.", + "cve": "CVE-2022-29211", + "id": "pyup.io-56728", + "more_info_path": "/vulnerabilities/CVE-2022-29211/56728", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61354,10 +61732,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'zlib' to v1.2.12 to handle CVE-2018-25032.", - "cve": "CVE-2018-25032", - "id": "pyup.io-56722", - "more_info_path": "/vulnerabilities/CVE-2018-25032/56722", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29197: Missing validation which causes denial of service via 'UnsortedSegmentJoin'.", + "cve": "CVE-2022-29197", + "id": "pyup.io-56712", + "more_info_path": "/vulnerabilities/CVE-2022-29197/56712", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61367,10 +61745,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29194: Missing validation which causes denial of service via 'DeleteSessionTensor'.", - "cve": "CVE-2022-29194", - "id": "pyup.io-56715", - "more_info_path": "/vulnerabilities/CVE-2022-29194/56715", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27774.", + "cve": "CVE-2022-27774", + "id": "pyup.io-56717", + "more_info_path": "/vulnerabilities/CVE-2022-27774/56717", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61380,10 +61758,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29207: Issues arising from undefined behavior stemming from users supplying invalid resource handles.", - "cve": "CVE-2022-29207", - "id": "pyup.io-56711", - "more_info_path": "/vulnerabilities/CVE-2022-29207/56711", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-30115.", + "cve": "CVE-2022-30115", + "id": "pyup.io-56721", + "more_info_path": "/vulnerabilities/CVE-2022-30115/56721", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61393,10 +61771,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29206: Missing validation which results in undefined behavior in 'SparseTensorDenseAdd'.", - "cve": "CVE-2022-29206", - "id": "pyup.io-56726", - "more_info_path": "/vulnerabilities/CVE-2022-29206/56726", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27778.", + "cve": "CVE-2022-27778", + "id": "pyup.io-56718", + "more_info_path": "/vulnerabilities/CVE-2022-27778/56718", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61406,10 +61784,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29211: Segfault when 'tf.histogram_fixed_width' is called with NaN values.", - "cve": "CVE-2022-29211", - "id": "pyup.io-56728", - "more_info_path": "/vulnerabilities/CVE-2022-29211/56728", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27781.", + "cve": "CVE-2022-27781", + "id": "pyup.io-56720", + "more_info_path": "/vulnerabilities/CVE-2022-27781/56720", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61419,10 +61797,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29204: Missing validation which causes denial of service via 'Conv3DBackpropFilterV2'.", - "cve": "CVE-2022-29204", - "id": "pyup.io-56727", - "more_info_path": "/vulnerabilities/CVE-2022-29204/56727", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29216: Code injection in 'saved_model_cli'.", + "cve": "CVE-2022-29216", + "id": "pyup.io-56705", + "more_info_path": "/vulnerabilities/CVE-2022-29216/56705", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61432,10 +61810,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29209: Type confusion leading to 'CHECK'-failure based denial of service.", - "cve": "CVE-2022-29209", - "id": "pyup.io-56735", - "more_info_path": "/vulnerabilities/CVE-2022-29209/56735", + "advisory": "Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29213: Crashes stemming from incomplete validation in signal ops.", + "cve": "CVE-2022-29213", + "id": "pyup.io-56733", + "more_info_path": "/vulnerabilities/CVE-2022-29213/56733", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61445,10 +61823,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29191: Missing validation which causes denial of service via 'GetSessionTensor'.", - "cve": "CVE-2022-29191", - "id": "pyup.io-56737", - "more_info_path": "/vulnerabilities/CVE-2022-29191/56737", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29212: Core dump when loading TFLite models with quantization.", + "cve": "CVE-2022-29212", + "id": "pyup.io-56738", + "more_info_path": "/vulnerabilities/CVE-2022-29212/56738", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61458,10 +61836,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29203: Integer overflow in 'SpaceToBatchND'.", - "cve": "CVE-2022-29203", - "id": "pyup.io-56723", - "more_info_path": "/vulnerabilities/CVE-2022-29203/56723", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29209: Type confusion leading to 'CHECK'-failure based denial of service.", + "cve": "CVE-2022-29209", + "id": "pyup.io-56735", + "more_info_path": "/vulnerabilities/CVE-2022-29209/56735", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61471,10 +61849,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29213: Crashes stemming from incomplete validation in signal ops.", - "cve": "CVE-2022-29213", - "id": "pyup.io-56733", - "more_info_path": "/vulnerabilities/CVE-2022-29213/56733", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29204: Missing validation which causes denial of service via 'Conv3DBackpropFilterV2'.", + "cve": "CVE-2022-29204", + "id": "pyup.io-56727", + "more_info_path": "/vulnerabilities/CVE-2022-29204/56727", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61484,10 +61862,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29200: Missing validation which causes denial of service via 'LSTMBlockCell'.", - "cve": "CVE-2022-29200", - "id": "pyup.io-56708", - "more_info_path": "/vulnerabilities/CVE-2022-29200/56708", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29203: Integer overflow in 'SpaceToBatchND'.", + "cve": "CVE-2022-29203", + "id": "pyup.io-56723", + "more_info_path": "/vulnerabilities/CVE-2022-29203/56723", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61497,10 +61875,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29193: missing validation which causes 'TensorSummaryV2' to crash.", - "cve": "CVE-2022-29193", - "id": "pyup.io-56706", - "more_info_path": "/vulnerabilities/CVE-2022-29193/56706", + "advisory": "Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29202: Denial of service in 'tf.ragged.constant' due to lack of validation.", + "cve": "CVE-2022-29202", + "id": "pyup.io-56724", + "more_info_path": "/vulnerabilities/CVE-2022-29202/56724", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61510,10 +61888,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29197: Missing validation which causes denial of service via 'UnsortedSegmentJoin'.", - "cve": "CVE-2022-29197", - "id": "pyup.io-56712", - "more_info_path": "/vulnerabilities/CVE-2022-29197/56712", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29207: Issues arising from undefined behavior stemming from users supplying invalid resource handles.", + "cve": "CVE-2022-29207", + "id": "pyup.io-56711", + "more_info_path": "/vulnerabilities/CVE-2022-29207/56711", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61523,10 +61901,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27774.", - "cve": "CVE-2022-27774", - "id": "pyup.io-56717", - "more_info_path": "/vulnerabilities/CVE-2022-27774/56717", + "advisory": "Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29200: Missing validation which causes denial of service via 'LSTMBlockCell'.", + "cve": "CVE-2022-29200", + "id": "pyup.io-56708", + "more_info_path": "/vulnerabilities/CVE-2022-29200/56708", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61536,10 +61914,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-30115.", - "cve": "CVE-2022-30115", - "id": "pyup.io-56721", - "more_info_path": "/vulnerabilities/CVE-2022-30115/56721", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29195: Missing validation which causes denial of service via 'StagePeek'.", + "cve": "CVE-2022-29195", + "id": "pyup.io-56714", + "more_info_path": "/vulnerabilities/CVE-2022-29195/56714", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61549,10 +61927,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27778.", - "cve": "CVE-2022-27778", - "id": "pyup.io-56718", - "more_info_path": "/vulnerabilities/CVE-2022-27778/56718", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27780.", + "cve": "CVE-2022-27780", + "id": "pyup.io-56730", + "more_info_path": "/vulnerabilities/CVE-2022-27780/56730", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61562,10 +61940,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27780.", - "cve": "CVE-2022-27780", - "id": "pyup.io-56730", - "more_info_path": "/vulnerabilities/CVE-2022-27780/56730", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29193: missing validation which causes 'TensorSummaryV2' to crash.", + "cve": "CVE-2022-29193", + "id": "pyup.io-56706", + "more_info_path": "/vulnerabilities/CVE-2022-29193/56706", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61575,10 +61953,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27779.", - "cve": "CVE-2022-27779", - "id": "pyup.io-56719", - "more_info_path": "/vulnerabilities/CVE-2022-27779/56719", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29192: missing validation which crashes 'QuantizeAndDequantizeV4Grad'.", + "cve": "CVE-2022-29192", + "id": "pyup.io-56729", + "more_info_path": "/vulnerabilities/CVE-2022-29192/56729", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61601,10 +61979,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29195: Missing validation which causes denial of service via 'StagePeek'.", - "cve": "CVE-2022-29195", - "id": "pyup.io-56714", - "more_info_path": "/vulnerabilities/CVE-2022-29195/56714", + "advisory": "Intel-tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29191: Missing validation which causes denial of service via 'GetSessionTensor'.", + "cve": "CVE-2022-29191", + "id": "pyup.io-56737", + "more_info_path": "/vulnerabilities/CVE-2022-29191/56737", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -61730,18 +62108,6 @@ ], "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, - { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35979: Segfault in 'QuantizedRelu' and 'QuantizedRelu6'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-v7vw-577f-vp8x", - "cve": "CVE-2022-35979", - "id": "pyup.io-56651", - "more_info_path": "/vulnerabilities/CVE-2022-35979/56651", - "specs": [ - "<2.7.4", - ">=2.8.0rc0,<2.8.3", - ">=2.9.0rc0,<2.9.2" - ], - "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" - }, { "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35996: Floating point exception in 'Conv2D'. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-q5jv-m6qw-5g37", "cve": "CVE-2022-35996", @@ -61755,10 +62121,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35998: 'CHECK' fail in 'EmptyTensorList'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhw4-wwr7-gjc5", - "cve": "CVE-2022-35998", - "id": "pyup.io-56667", - "more_info_path": "/vulnerabilities/CVE-2022-35998/56667", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36019: 'CHECK' fail in 'FakeQuantWithMinMaxVarsPerChannel'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9j4v-pp28-mxv7", + "cve": "CVE-2022-36019", + "id": "pyup.io-56688", + "more_info_path": "/vulnerabilities/CVE-2022-36019/56688", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61767,10 +62133,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36001: 'CHECK' fail in 'DrawBoundingBoxes'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-jqm7-m5q7-3hm5", - "cve": "CVE-2022-36001", - "id": "pyup.io-56694", - "more_info_path": "/vulnerabilities/CVE-2022-36001/56694", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35982: Segfault in 'SparseBincount'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-397c-5g2j-qxpv", + "cve": "CVE-2022-35982", + "id": "pyup.io-56680", + "more_info_path": "/vulnerabilities/CVE-2022-35982/56680", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61779,10 +62145,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36002: 'CHECK' fail in 'Unbatch'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mh3m-62v7-68xg", - "cve": "CVE-2022-36002", - "id": "pyup.io-56658", - "more_info_path": "/vulnerabilities/CVE-2022-36002/56658", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35972: Segfault in 'QuantizedBiasAdd'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4pc4-m9mj-v2r9", + "cve": "CVE-2022-35972", + "id": "pyup.io-56681", + "more_info_path": "/vulnerabilities/CVE-2022-35972/56681", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61791,10 +62157,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35965: Segfault in 'LowerBound' and 'UpperBound'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qxpx-j395-pw36", - "cve": "CVE-2022-35965", - "id": "pyup.io-56687", - "more_info_path": "/vulnerabilities/CVE-2022-35965/56687", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36000: 'CHECK' fail in 'Eig'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fqxc-pvf8-2w9v", + "cve": "CVE-2022-36000", + "id": "pyup.io-56670", + "more_info_path": "/vulnerabilities/CVE-2022-36000/56670", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61803,10 +62169,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35963: 'CHECK' failures in 'FractionalAvgPoolGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-84jm-4cf3-9jfm", - "cve": "CVE-2022-35963", - "id": "pyup.io-56695", - "more_info_path": "/vulnerabilities/CVE-2022-35963/56695", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35966: Segfault in 'QuantizedAvgPool'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4w68-4x85-mjj9", + "cve": "CVE-2022-35966", + "id": "pyup.io-56677", + "more_info_path": "/vulnerabilities/CVE-2022-35966/56677", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61815,10 +62181,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36012: Assertion fail on MLIR empty edge names.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-jvhc-5hhr-w3v5", - "cve": "CVE-2022-36012", - "id": "pyup.io-56659", - "more_info_path": "/vulnerabilities/CVE-2022-36012/56659", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35941: 'CHECK' failure in 'AvgPoolOp'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mgmh-g2v6-mqw5", + "cve": "CVE-2022-35941", + "id": "pyup.io-56684", + "more_info_path": "/vulnerabilities/CVE-2022-35941/56684", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61827,10 +62193,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36026: 'CHECK' fail in 'QuantizeAndDequantizeV3'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9cr2-8pwr-fhfq", - "cve": "CVE-2022-36026", - "id": "pyup.io-56676", - "more_info_path": "/vulnerabilities/CVE-2022-36026/56676", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36013: Null-dereference in 'mlir::tfg::GraphDefImporter::ConvertNodeDef'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-828c-5j5q-vrjq", + "cve": "CVE-2022-36013", + "id": "pyup.io-56668", + "more_info_path": "/vulnerabilities/CVE-2022-36013/56668", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61839,10 +62205,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35989: 'CHECK' fail in 'MaxPool'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-j43h-pgmg-5hjq", - "cve": "CVE-2022-35989", - "id": "pyup.io-56701", - "more_info_path": "/vulnerabilities/CVE-2022-35989/56701", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35964: Segfault in 'BlockLSTMGradV2'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f7r5-q7cx-h668", + "cve": "CVE-2022-35964", + "id": "pyup.io-56665", + "more_info_path": "/vulnerabilities/CVE-2022-35964/56665", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61851,10 +62217,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35940: Int overflow in 'RaggedRangeOp'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x989-q2pq-4q5x", - "cve": "CVE-2022-35940", - "id": "pyup.io-56693", - "more_info_path": "/vulnerabilities/CVE-2022-35940/56693", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35937: OOB read in 'Gather_nd' op in TF Lite.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pxrw-j2fv-hx3h", + "cve": "CVE-2022-35937", + "id": "pyup.io-56647", + "more_info_path": "/vulnerabilities/CVE-2022-35937/56647", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61863,10 +62229,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35992: 'CHECK' fail in 'TensorListFromTensor'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9v8w-xmr4-wgxp", - "cve": "CVE-2022-35992", - "id": "pyup.io-56671", - "more_info_path": "/vulnerabilities/CVE-2022-35992/56671", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36027: Segfault TFLite converter on per-channel quantized transposed convolutions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-79h2-q768-fpxr", + "cve": "CVE-2022-36027", + "id": "pyup.io-56679", + "more_info_path": "/vulnerabilities/CVE-2022-36027/56679", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61875,10 +62241,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36019: 'CHECK' fail in 'FakeQuantWithMinMaxVarsPerChannel'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9j4v-pp28-mxv7", - "cve": "CVE-2022-36019", - "id": "pyup.io-56688", - "more_info_path": "/vulnerabilities/CVE-2022-36019/56688", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36017: Segfault in 'Requantize'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-wqmc-pm8c-2jhc", + "cve": "CVE-2022-36017", + "id": "pyup.io-56652", + "more_info_path": "/vulnerabilities/CVE-2022-36017/56652", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61887,10 +62253,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35999: 'CHECK' fail in 'Conv2DBackpropInput'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-37jf-mjv6-xfqw", - "cve": "CVE-2022-35999", - "id": "pyup.io-56696", - "more_info_path": "/vulnerabilities/CVE-2022-35999/56696", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36016: 'CHECK'-fail in 'tensorflow::full_type::SubstituteFromAttrs'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g468-qj8g-vcjc", + "cve": "CVE-2022-36016", + "id": "pyup.io-56685", + "more_info_path": "/vulnerabilities/CVE-2022-36016/56685", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61899,10 +62265,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35995: 'CHECK' fail in 'AudioSummaryV2'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g9h5-vr8m-x2h4", - "cve": "CVE-2022-35995", - "id": "pyup.io-56697", - "more_info_path": "/vulnerabilities/CVE-2022-35995/56697", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36015: Integer overflow in math ops. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rh87-q4vg-m45j", + "cve": "CVE-2022-36015", + "id": "pyup.io-56653", + "more_info_path": "/vulnerabilities/CVE-2022-36015/56653", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61911,10 +62277,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36005: 'CHECK' fail in 'FakeQuantWithMinMaxVarsGradient'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-r26c-679w-mrjm", - "cve": "CVE-2022-36005", - "id": "pyup.io-56702", - "more_info_path": "/vulnerabilities/CVE-2022-36005/56702", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36014: Null-dereference in 'mlir::tfg::TFOp::nameAttr'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7j3m-8g3c-9qqq", + "cve": "CVE-2022-36014", + "id": "pyup.io-56683", + "more_info_path": "/vulnerabilities/CVE-2022-36014/56683", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61923,10 +62289,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35952: 'CHECK' failures in 'UnbatchGradOp'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h5vq-gw2c-pq47", - "cve": "CVE-2022-35952", - "id": "pyup.io-56649", - "more_info_path": "/vulnerabilities/CVE-2022-35952/56649", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36012: Assertion fail on MLIR empty edge names.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-jvhc-5hhr-w3v5", + "cve": "CVE-2022-36012", + "id": "pyup.io-56659", + "more_info_path": "/vulnerabilities/CVE-2022-36012/56659", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61935,10 +62301,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35983: 'CHECK' fail in 'Save' and 'SaveSlices'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m6vp-8q9j-whx4", - "cve": "CVE-2022-35983", - "id": "pyup.io-56660", - "more_info_path": "/vulnerabilities/CVE-2022-35983/56660", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36011: Null dereference on MLIR on empty function attributes.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fv43-93gv-vm8f", + "cve": "CVE-2022-36011", + "id": "pyup.io-56678", + "more_info_path": "/vulnerabilities/CVE-2022-36011/56678", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61947,10 +62313,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35984: 'CHECK' fail in 'ParameterizedTruncatedNormal'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2xf-8hgm-hpw5", - "cve": "CVE-2022-35984", - "id": "pyup.io-56661", - "more_info_path": "/vulnerabilities/CVE-2022-35984/56661", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36005: 'CHECK' fail in 'FakeQuantWithMinMaxVarsGradient'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-r26c-679w-mrjm", + "cve": "CVE-2022-36005", + "id": "pyup.io-56702", + "more_info_path": "/vulnerabilities/CVE-2022-36005/56702", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61959,10 +62325,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36011: Null dereference on MLIR on empty function attributes.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fv43-93gv-vm8f", - "cve": "CVE-2022-36011", - "id": "pyup.io-56678", - "more_info_path": "/vulnerabilities/CVE-2022-36011/56678", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36004: 'CHECK' fail in 'tf.random.gamma'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv8m-8x97-937q", + "cve": "CVE-2022-36004", + "id": "pyup.io-56699", + "more_info_path": "/vulnerabilities/CVE-2022-36004/56699", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61971,10 +62337,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36027: Segfault TFLite converter on per-channel quantized transposed convolutions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-79h2-q768-fpxr", - "cve": "CVE-2022-36027", - "id": "pyup.io-56679", - "more_info_path": "/vulnerabilities/CVE-2022-36027/56679", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36003: 'CHECK' fail in 'RandomPoissonV2'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cv2p-32v3-vhwq", + "cve": "CVE-2022-36003", + "id": "pyup.io-56654", + "more_info_path": "/vulnerabilities/CVE-2022-36003/56654", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61983,10 +62349,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35982: Segfault in 'SparseBincount'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-397c-5g2j-qxpv", - "cve": "CVE-2022-35982", - "id": "pyup.io-56680", - "more_info_path": "/vulnerabilities/CVE-2022-35982/56680", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36002: 'CHECK' fail in 'Unbatch'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mh3m-62v7-68xg", + "cve": "CVE-2022-36002", + "id": "pyup.io-56658", + "more_info_path": "/vulnerabilities/CVE-2022-36002/56658", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -61995,10 +62361,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35994: 'CHECK' fail in 'CollectiveGather'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fhfc-2q7x-929f", - "cve": "CVE-2022-35994", - "id": "pyup.io-56692", - "more_info_path": "/vulnerabilities/CVE-2022-35994/56692", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36001: 'CHECK' fail in 'DrawBoundingBoxes'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-jqm7-m5q7-3hm5", + "cve": "CVE-2022-36001", + "id": "pyup.io-56694", + "more_info_path": "/vulnerabilities/CVE-2022-36001/56694", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62007,10 +62373,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35972: Segfault in 'QuantizedBiasAdd'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4pc4-m9mj-v2r9", - "cve": "CVE-2022-35972", - "id": "pyup.io-56681", - "more_info_path": "/vulnerabilities/CVE-2022-35972/56681", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35999: 'CHECK' fail in 'Conv2DBackpropInput'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-37jf-mjv6-xfqw", + "cve": "CVE-2022-35999", + "id": "pyup.io-56696", + "more_info_path": "/vulnerabilities/CVE-2022-35999/56696", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62019,10 +62385,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36018: 'CHECK' fail in 'RaggedTensorToVariant'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m6cv-4fmf-66xf", - "cve": "CVE-2022-36018", - "id": "pyup.io-56700", - "more_info_path": "/vulnerabilities/CVE-2022-36018/56700", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35998: 'CHECK' fail in 'EmptyTensorList'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhw4-wwr7-gjc5", + "cve": "CVE-2022-35998", + "id": "pyup.io-56667", + "more_info_path": "/vulnerabilities/CVE-2022-35998/56667", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62031,10 +62397,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36016: 'CHECK'-fail in 'tensorflow::full_type::SubstituteFromAttrs'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g468-qj8g-vcjc", - "cve": "CVE-2022-36016", - "id": "pyup.io-56685", - "more_info_path": "/vulnerabilities/CVE-2022-36016/56685", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35997: 'CHECK' fail in 'tf.sparse.cross'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-p7hr-f446-x6qf", + "cve": "CVE-2022-35997", + "id": "pyup.io-56666", + "more_info_path": "/vulnerabilities/CVE-2022-35997/56666", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62043,10 +62409,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36000: 'CHECK' fail in 'Eig'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fqxc-pvf8-2w9v", - "cve": "CVE-2022-36000", - "id": "pyup.io-56670", - "more_info_path": "/vulnerabilities/CVE-2022-36000/56670", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35995: 'CHECK' fail in 'AudioSummaryV2'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g9h5-vr8m-x2h4", + "cve": "CVE-2022-35995", + "id": "pyup.io-56697", + "more_info_path": "/vulnerabilities/CVE-2022-35995/56697", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62055,10 +62421,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35974: Segfault in 'QuantizeDownAndShrinkRange'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vgvh-2pf4-jr2x", - "cve": "CVE-2022-35974", - "id": "pyup.io-56673", - "more_info_path": "/vulnerabilities/CVE-2022-35974/56673", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35994: 'CHECK' fail in 'CollectiveGather'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fhfc-2q7x-929f", + "cve": "CVE-2022-35994", + "id": "pyup.io-56692", + "more_info_path": "/vulnerabilities/CVE-2022-35994/56692", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62067,10 +62433,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35987: 'CHECK' fail in 'DenseBincount'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-w62h-8xjm-fv49", - "cve": "CVE-2022-35987", - "id": "pyup.io-56675", - "more_info_path": "/vulnerabilities/CVE-2022-35987/56675", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35993: 'CHECK' fail in 'SetSize'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-wq6q-6m32-9rv9", + "cve": "CVE-2022-35993", + "id": "pyup.io-56682", + "more_info_path": "/vulnerabilities/CVE-2022-35993/56682", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62079,10 +62445,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35966: Segfault in 'QuantizedAvgPool'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4w68-4x85-mjj9", - "cve": "CVE-2022-35966", - "id": "pyup.io-56677", - "more_info_path": "/vulnerabilities/CVE-2022-35966/56677", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35992: 'CHECK' fail in 'TensorListFromTensor'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9v8w-xmr4-wgxp", + "cve": "CVE-2022-35992", + "id": "pyup.io-56671", + "more_info_path": "/vulnerabilities/CVE-2022-35992/56671", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62091,10 +62457,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35997: 'CHECK' fail in 'tf.sparse.cross'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-p7hr-f446-x6qf", - "cve": "CVE-2022-35997", - "id": "pyup.io-56666", - "more_info_path": "/vulnerabilities/CVE-2022-35997/56666", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36026: 'CHECK' fail in 'QuantizeAndDequantizeV3'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9cr2-8pwr-fhfq", + "cve": "CVE-2022-36026", + "id": "pyup.io-56676", + "more_info_path": "/vulnerabilities/CVE-2022-36026/56676", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62103,10 +62469,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35971: 'CHECK' fail in 'FakeQuantWithMinMaxVars'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9fpg-838v-wpv7", - "cve": "CVE-2022-35971", - "id": "pyup.io-56698", - "more_info_path": "/vulnerabilities/CVE-2022-35971/56698", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35981: 'CHECK' fail in 'FractionalMaxPoolGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vxv8-r8q2-63xw", + "cve": "CVE-2022-35981", + "id": "pyup.io-56669", + "more_info_path": "/vulnerabilities/CVE-2022-35981/56669", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62115,10 +62481,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36017: Segfault in 'Requantize'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-wqmc-pm8c-2jhc", - "cve": "CVE-2022-36017", - "id": "pyup.io-56652", - "more_info_path": "/vulnerabilities/CVE-2022-36017/56652", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35973: Segfault in 'QuantizedMatMul'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-689c-r7h2-fv9v", + "cve": "CVE-2022-35973", + "id": "pyup.io-56674", + "more_info_path": "/vulnerabilities/CVE-2022-35973/56674", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62127,10 +62493,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35985: 'CHECK' fail in 'LRNGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9942-r22v-78cp", - "cve": "CVE-2022-35985", - "id": "pyup.io-56655", - "more_info_path": "/vulnerabilities/CVE-2022-35985/56655", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36018: 'CHECK' fail in 'RaggedTensorToVariant'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m6cv-4fmf-66xf", + "cve": "CVE-2022-36018", + "id": "pyup.io-56700", + "more_info_path": "/vulnerabilities/CVE-2022-36018/56700", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62139,10 +62505,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35941: 'CHECK' failure in 'AvgPoolOp'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mgmh-g2v6-mqw5", - "cve": "CVE-2022-35941", - "id": "pyup.io-56684", - "more_info_path": "/vulnerabilities/CVE-2022-35941/56684", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35990: 'CHECK' fail in 'FakeQuantWithMinMaxVarsPerChannelGradient'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h7ff-cfc9-wmmh", + "cve": "CVE-2022-35990", + "id": "pyup.io-56663", + "more_info_path": "/vulnerabilities/CVE-2022-35990/56663", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62151,10 +62517,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35993: 'CHECK' fail in 'SetSize'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-wq6q-6m32-9rv9", - "cve": "CVE-2022-35993", - "id": "pyup.io-56682", - "more_info_path": "/vulnerabilities/CVE-2022-35993/56682", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35989: 'CHECK' fail in 'MaxPool'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-j43h-pgmg-5hjq", + "cve": "CVE-2022-35989", + "id": "pyup.io-56701", + "more_info_path": "/vulnerabilities/CVE-2022-35989/56701", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62175,10 +62541,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35967: Segfault in 'QuantizedAdd'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-v6h3-348g-6h5x", - "cve": "CVE-2022-35967", - "id": "pyup.io-56689", - "more_info_path": "/vulnerabilities/CVE-2022-35967/56689", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35987: 'CHECK' fail in 'DenseBincount'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-w62h-8xjm-fv49", + "cve": "CVE-2022-35987", + "id": "pyup.io-56675", + "more_info_path": "/vulnerabilities/CVE-2022-35987/56675", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62187,10 +62553,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36014: Null-dereference in 'mlir::tfg::TFOp::nameAttr'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7j3m-8g3c-9qqq", - "cve": "CVE-2022-36014", - "id": "pyup.io-56683", - "more_info_path": "/vulnerabilities/CVE-2022-36014/56683", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35986: Segfault in 'RaggedBincount'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-wr9v-g9vf-c74v", + "cve": "CVE-2022-35986", + "id": "pyup.io-56672", + "more_info_path": "/vulnerabilities/CVE-2022-35986/56672", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62199,10 +62565,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35990: 'CHECK' fail in 'FakeQuantWithMinMaxVarsPerChannelGradient'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h7ff-cfc9-wmmh", - "cve": "CVE-2022-35990", - "id": "pyup.io-56663", - "more_info_path": "/vulnerabilities/CVE-2022-35990/56663", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35985: 'CHECK' fail in 'LRNGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9942-r22v-78cp", + "cve": "CVE-2022-35985", + "id": "pyup.io-56655", + "more_info_path": "/vulnerabilities/CVE-2022-35985/56655", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62211,10 +62577,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36013: Null-dereference in 'mlir::tfg::GraphDefImporter::ConvertNodeDef'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-828c-5j5q-vrjq", - "cve": "CVE-2022-36013", - "id": "pyup.io-56668", - "more_info_path": "/vulnerabilities/CVE-2022-36013/56668", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35984: 'CHECK' fail in 'ParameterizedTruncatedNormal'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2xf-8hgm-hpw5", + "cve": "CVE-2022-35984", + "id": "pyup.io-56661", + "more_info_path": "/vulnerabilities/CVE-2022-35984/56661", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62223,10 +62589,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35964: Segfault in 'BlockLSTMGradV2'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f7r5-q7cx-h668", - "cve": "CVE-2022-35964", - "id": "pyup.io-56665", - "more_info_path": "/vulnerabilities/CVE-2022-35964/56665", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35983: 'CHECK' fail in 'Save' and 'SaveSlices'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m6vp-8q9j-whx4", + "cve": "CVE-2022-35983", + "id": "pyup.io-56660", + "more_info_path": "/vulnerabilities/CVE-2022-35983/56660", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62235,10 +62601,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35968: 'CHECK' fail in 'AvgPoolGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-2475-53vw-vp25", - "cve": "CVE-2022-35968", - "id": "pyup.io-56690", - "more_info_path": "/vulnerabilities/CVE-2022-35968/56690", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35979: Segfault in 'QuantizedRelu' and 'QuantizedRelu6'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-v7vw-577f-vp8x", + "cve": "CVE-2022-35979", + "id": "pyup.io-56651", + "more_info_path": "/vulnerabilities/CVE-2022-35979/56651", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62247,10 +62613,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35970: Segfault in 'QuantizedInstanceNorm'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g35r-369w-3fqp", - "cve": "CVE-2022-35970", - "id": "pyup.io-56691", - "more_info_path": "/vulnerabilities/CVE-2022-35970/56691", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35974: Segfault in 'QuantizeDownAndShrinkRange'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vgvh-2pf4-jr2x", + "cve": "CVE-2022-35974", + "id": "pyup.io-56673", + "more_info_path": "/vulnerabilities/CVE-2022-35974/56673", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62259,10 +62625,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35939: OOB write in 'scatter_nd' op in TF Lite.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-ffjm-4qwc-7cmf", - "cve": "CVE-2022-35939", - "id": "pyup.io-56648", - "more_info_path": "/vulnerabilities/CVE-2022-35939/56648", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35971: 'CHECK' fail in 'FakeQuantWithMinMaxVars'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9fpg-838v-wpv7", + "cve": "CVE-2022-35971", + "id": "pyup.io-56698", + "more_info_path": "/vulnerabilities/CVE-2022-35971/56698", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62271,10 +62637,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35937: OOB read in 'Gather_nd' op in TF Lite.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pxrw-j2fv-hx3h", - "cve": "CVE-2022-35937", - "id": "pyup.io-56647", - "more_info_path": "/vulnerabilities/CVE-2022-35937/56647", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35970: Segfault in 'QuantizedInstanceNorm'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g35r-369w-3fqp", + "cve": "CVE-2022-35970", + "id": "pyup.io-56691", + "more_info_path": "/vulnerabilities/CVE-2022-35970/56691", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62283,10 +62649,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35934: 'CHECK' failure in tf.reshape via overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f4w6-h4f5-wx45", - "cve": "CVE-2022-35934", - "id": "pyup.io-56657", - "more_info_path": "/vulnerabilities/CVE-2022-35934/56657", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35968: 'CHECK' fail in 'AvgPoolGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-2475-53vw-vp25", + "cve": "CVE-2022-35968", + "id": "pyup.io-56690", + "more_info_path": "/vulnerabilities/CVE-2022-35968/56690", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62295,10 +62661,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35986: Segfault in 'RaggedBincount'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-wr9v-g9vf-c74v", - "cve": "CVE-2022-35986", - "id": "pyup.io-56672", - "more_info_path": "/vulnerabilities/CVE-2022-35986/56672", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35967: Segfault in 'QuantizedAdd'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-v6h3-348g-6h5x", + "cve": "CVE-2022-35967", + "id": "pyup.io-56689", + "more_info_path": "/vulnerabilities/CVE-2022-35967/56689", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62307,10 +62673,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35981: 'CHECK' fail in 'FractionalMaxPoolGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vxv8-r8q2-63xw", - "cve": "CVE-2022-35981", - "id": "pyup.io-56669", - "more_info_path": "/vulnerabilities/CVE-2022-35981/56669", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35965: Segfault in 'LowerBound' and 'UpperBound'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qxpx-j395-pw36", + "cve": "CVE-2022-35965", + "id": "pyup.io-56687", + "more_info_path": "/vulnerabilities/CVE-2022-35965/56687", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62319,10 +62685,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35973: Segfault in 'QuantizedMatMul'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-689c-r7h2-fv9v", - "cve": "CVE-2022-35973", - "id": "pyup.io-56674", - "more_info_path": "/vulnerabilities/CVE-2022-35973/56674", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35963: 'CHECK' failures in 'FractionalAvgPoolGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-84jm-4cf3-9jfm", + "cve": "CVE-2022-35963", + "id": "pyup.io-56695", + "more_info_path": "/vulnerabilities/CVE-2022-35963/56695", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62331,10 +62697,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36015: Integer overflow in math ops. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rh87-q4vg-m45j", - "cve": "CVE-2022-36015", - "id": "pyup.io-56653", - "more_info_path": "/vulnerabilities/CVE-2022-36015/56653", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35952: 'CHECK' failures in 'UnbatchGradOp'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h5vq-gw2c-pq47", + "cve": "CVE-2022-35952", + "id": "pyup.io-56649", + "more_info_path": "/vulnerabilities/CVE-2022-35952/56649", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62343,10 +62709,22 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36003: 'CHECK' fail in 'RandomPoissonV2'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cv2p-32v3-vhwq", - "cve": "CVE-2022-36003", - "id": "pyup.io-56654", - "more_info_path": "/vulnerabilities/CVE-2022-36003/56654", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35940: Int overflow in 'RaggedRangeOp'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x989-q2pq-4q5x", + "cve": "CVE-2022-35940", + "id": "pyup.io-56693", + "more_info_path": "/vulnerabilities/CVE-2022-35940/56693", + "specs": [ + "<2.7.4", + ">=2.8.0rc0,<2.8.3", + ">=2.9.0rc0,<2.9.2" + ], + "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" + }, + { + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35939: OOB write in 'scatter_nd' op in TF Lite.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-ffjm-4qwc-7cmf", + "cve": "CVE-2022-35939", + "id": "pyup.io-56648", + "more_info_path": "/vulnerabilities/CVE-2022-35939/56648", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62367,10 +62745,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36004: 'CHECK' fail in 'tf.random.gamma'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv8m-8x97-937q", - "cve": "CVE-2022-36004", - "id": "pyup.io-56699", - "more_info_path": "/vulnerabilities/CVE-2022-36004/56699", + "advisory": "Intel-tensorflow 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35934: 'CHECK' failure in tf.reshape via overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f4w6-h4f5-wx45", + "cve": "CVE-2022-35934", + "id": "pyup.io-56657", + "more_info_path": "/vulnerabilities/CVE-2022-35934/56657", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -62415,10 +62793,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41889: If a list of quantized tensors is assigned to an attribute, the pywrap code fails to parse the tensor and returns a 'nullptr', which is not caught. An example can be seen in 'tf.compat.v1.extract_volume_patches' by passing in quantized tensors as input 'ksizes'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xxcj-rhqg-m46g", - "cve": "CVE-2022-41889", - "id": "pyup.io-56626", - "more_info_path": "/vulnerabilities/CVE-2022-41889/56626", + "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41910: The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-frqp-wp83-qggv", + "cve": "CVE-2022-41910", + "id": "pyup.io-56641", + "more_info_path": "/vulnerabilities/CVE-2022-41910/56641", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62427,10 +62805,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41890: If 'BCast::ToShape' is given input larger than an 'int32', it will crash, despite being supposed to handle up to an 'int64'. An example can be seen in 'tf.experimental.numpy.outer' by passing in large input to the input 'b'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h246-cgh4-7475", - "cve": "CVE-2022-41890", - "id": "pyup.io-56633", - "more_info_path": "/vulnerabilities/CVE-2022-41890/56633", + "advisory": "TensorFlow is an open source platform for machine learning. The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered. We have patched the issue in GitHub commit a65411a1d69edfb16b25907ffb8f73556ce36bb7. The fix will be included in TensorFlow 2.11.0. We will also cherrypick this commit on TensorFlow 2.8.4, 2.9.3, and 2.10.1.", + "cve": "CVE-2022-41902", + "id": "pyup.io-56636", + "more_info_path": "/vulnerabilities/CVE-2022-41902/56636", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62439,10 +62817,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41907: When 'tf.raw_ops.ResizeNearestNeighborGrad' is given a large 'size' input, it overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-368v-7v32-52fx", - "cve": "CVE-2022-41907", - "id": "pyup.io-56635", - "more_info_path": "/vulnerabilities/CVE-2022-41907/56635", + "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41911: When printing a tensor, we get it's data as a 'const char*' array (since that's the underlying storage) and then we typecast it to the element type. However, conversions from 'char' to 'bool' are undefined if the 'char' is not '0' or '1', so sanitizers/fuzzers will crash.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pf36-r9c6-h97j", + "cve": "CVE-2022-41911", + "id": "pyup.io-56625", + "more_info_path": "/vulnerabilities/CVE-2022-41911/56625", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62463,10 +62841,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41898: If 'SparseFillEmptyRowsGrad' is given empty inputs, TensorFlow will crash.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-hq7g-wwwp-q46h", - "cve": "CVE-2022-41898", - "id": "pyup.io-56643", - "more_info_path": "/vulnerabilities/CVE-2022-41898/56643", + "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41908: TensorFlow is an open source platform for machine learning. An input 'token' that is not a UTF-8 bytestring will trigger a 'CHECK' fail in 'tf.raw_ops.PyFunc'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv77-9g28-cwg3", + "cve": "CVE-2022-41908", + "id": "pyup.io-56631", + "more_info_path": "/vulnerabilities/CVE-2022-41908/56631", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62475,10 +62853,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41894: The reference kernel of the 'CONV_3D_TRANSPOSE' TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of 'data_ptr += num_channels;' it should be 'data_ptr += output_num_channels;' as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6q3-vv32-2cq5", - "cve": "CVE-2022-41894", - "id": "pyup.io-56644", - "more_info_path": "/vulnerabilities/CVE-2022-41894/56644", + "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41899: TensorFlow is an open source platform for machine learning. Inputs 'dense_features' or 'example_state_data' not of rank 2 will trigger a 'CHECK' fail in 'SdcaOptimizer'.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-27rc-728f-x5w2", + "cve": "CVE-2022-41899", + "id": "pyup.io-56628", + "more_info_path": "/vulnerabilities/CVE-2022-41899/56628", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62487,10 +62865,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41896: If 'ThreadUnsafeUnigramCandidateSampler' is given input 'filterbank_channel_count' greater than the allowed max size, TensorFlow will crash.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rmg2-f698-wq35", - "cve": "CVE-2022-41896", - "id": "pyup.io-56637", - "more_info_path": "/vulnerabilities/CVE-2022-41896/56637", + "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41898: If 'SparseFillEmptyRowsGrad' is given empty inputs, TensorFlow will crash.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-hq7g-wwwp-q46h", + "cve": "CVE-2022-41898", + "id": "pyup.io-56643", + "more_info_path": "/vulnerabilities/CVE-2022-41898/56643", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62499,10 +62877,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41885: When 'tf.raw_ops.FusedResizeAndPadConv2D' is given a large tensor shape, it overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-762h-vpvw-3rcx", - "cve": "CVE-2022-41885", - "id": "pyup.io-56627", - "more_info_path": "/vulnerabilities/CVE-2022-41885/56627", + "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41897: If 'FractionMaxPoolGrad' is given outsize inputs 'row_pooling_sequence' and 'col_pooling_sequence', TensorFlow will crash.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2w8-jw48-fr7j", + "cve": "CVE-2022-41897", + "id": "pyup.io-56639", + "more_info_path": "/vulnerabilities/CVE-2022-41897/56639", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62511,10 +62889,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41893: If 'tf.raw_ops.TensorListResize' is given a nonscalar value for input 'size', it results 'CHECK' fail which can be used to trigger a denial of service attack.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-67pf-62xr-q35m", - "cve": "CVE-2022-41893", - "id": "pyup.io-56629", - "more_info_path": "/vulnerabilities/CVE-2022-41893/56629", + "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41894: The reference kernel of the 'CONV_3D_TRANSPOSE' TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of 'data_ptr += num_channels;' it should be 'data_ptr += output_num_channels;' as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6q3-vv32-2cq5", + "cve": "CVE-2022-41894", + "id": "pyup.io-56644", + "more_info_path": "/vulnerabilities/CVE-2022-41894/56644", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62523,10 +62901,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41880: When the 'BaseCandidateSamplerOp' function receives a value in 'true_classes' larger than 'range_max', a heap oob read occurs.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-8w5g-3wcv-9g2j", - "cve": "CVE-2022-41880", - "id": "pyup.io-56634", - "more_info_path": "/vulnerabilities/CVE-2022-41880/56634", + "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41893: If 'tf.raw_ops.TensorListResize' is given a nonscalar value for input 'size', it results 'CHECK' fail which can be used to trigger a denial of service attack.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-67pf-62xr-q35m", + "cve": "CVE-2022-41893", + "id": "pyup.io-56629", + "more_info_path": "/vulnerabilities/CVE-2022-41893/56629", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62535,10 +62913,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41908: TensorFlow is an open source platform for machine learning. An input 'token' that is not a UTF-8 bytestring will trigger a 'CHECK' fail in 'tf.raw_ops.PyFunc'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv77-9g28-cwg3", - "cve": "CVE-2022-41908", - "id": "pyup.io-56631", - "more_info_path": "/vulnerabilities/CVE-2022-41908/56631", + "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41890: If 'BCast::ToShape' is given input larger than an 'int32', it will crash, despite being supposed to handle up to an 'int64'. An example can be seen in 'tf.experimental.numpy.outer' by passing in large input to the input 'b'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h246-cgh4-7475", + "cve": "CVE-2022-41890", + "id": "pyup.io-56633", + "more_info_path": "/vulnerabilities/CVE-2022-41890/56633", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62547,10 +62925,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41886: When 'tf.raw_ops.ImageProjectiveTransformV2' is given a large output shape, it overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-54pp-c6pp-7fpx", - "cve": "CVE-2022-41886", - "id": "pyup.io-56640", - "more_info_path": "/vulnerabilities/CVE-2022-41886/56640", + "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41889: If a list of quantized tensors is assigned to an attribute, the pywrap code fails to parse the tensor and returns a 'nullptr', which is not caught. An example can be seen in 'tf.compat.v1.extract_volume_patches' by passing in quantized tensors as input 'ksizes'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xxcj-rhqg-m46g", + "cve": "CVE-2022-41889", + "id": "pyup.io-56626", + "more_info_path": "/vulnerabilities/CVE-2022-41889/56626", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62559,10 +62937,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41897: If 'FractionMaxPoolGrad' is given outsize inputs 'row_pooling_sequence' and 'col_pooling_sequence', TensorFlow will crash.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2w8-jw48-fr7j", - "cve": "CVE-2022-41897", - "id": "pyup.io-56639", - "more_info_path": "/vulnerabilities/CVE-2022-41897/56639", + "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41888: When running on GPU, 'tf.image.generate_bounding_box_proposals' receives a 'scores' input that must be of rank 4 but is not checked.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6x99-gv2v-q76v", + "cve": "CVE-2022-41888", + "id": "pyup.io-56642", + "more_info_path": "/vulnerabilities/CVE-2022-41888/56642", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62571,10 +62949,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41888: When running on GPU, 'tf.image.generate_bounding_box_proposals' receives a 'scores' input that must be of rank 4 but is not checked.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6x99-gv2v-q76v", - "cve": "CVE-2022-41888", - "id": "pyup.io-56642", - "more_info_path": "/vulnerabilities/CVE-2022-41888/56642", + "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41907: When 'tf.raw_ops.ResizeNearestNeighborGrad' is given a large 'size' input, it overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-368v-7v32-52fx", + "cve": "CVE-2022-41907", + "id": "pyup.io-56635", + "more_info_path": "/vulnerabilities/CVE-2022-41907/56635", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62583,10 +62961,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41911: When printing a tensor, we get it's data as a 'const char*' array (since that's the underlying storage) and then we typecast it to the element type. However, conversions from 'char' to 'bool' are undefined if the 'char' is not '0' or '1', so sanitizers/fuzzers will crash.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pf36-r9c6-h97j", - "cve": "CVE-2022-41911", - "id": "pyup.io-56625", - "more_info_path": "/vulnerabilities/CVE-2022-41911/56625", + "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41880: When the 'BaseCandidateSamplerOp' function receives a value in 'true_classes' larger than 'range_max', a heap oob read occurs.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-8w5g-3wcv-9g2j", + "cve": "CVE-2022-41880", + "id": "pyup.io-56634", + "more_info_path": "/vulnerabilities/CVE-2022-41880/56634", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62595,10 +62973,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41900: The security vulnerability results in FractionalMax(AVG)Pool with illegal pooling_ratio. Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or remote code execution.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvwp-h6jv-7472", - "cve": "CVE-2022-41900", - "id": "pyup.io-56632", - "more_info_path": "/vulnerabilities/CVE-2022-41900/56632", + "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41896: If 'ThreadUnsafeUnigramCandidateSampler' is given input 'filterbank_channel_count' greater than the allowed max size, TensorFlow will crash.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rmg2-f698-wq35", + "cve": "CVE-2022-41896", + "id": "pyup.io-56637", + "more_info_path": "/vulnerabilities/CVE-2022-41896/56637", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62607,10 +62985,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41884: If a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-jq6x-99hj-q636", - "cve": "CVE-2022-41884", - "id": "pyup.io-56638", - "more_info_path": "/vulnerabilities/CVE-2022-41884/56638", + "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41885: When 'tf.raw_ops.FusedResizeAndPadConv2D' is given a large tensor shape, it overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-762h-vpvw-3rcx", + "cve": "CVE-2022-41885", + "id": "pyup.io-56627", + "more_info_path": "/vulnerabilities/CVE-2022-41885/56627", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62619,10 +62997,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41910: The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-frqp-wp83-qggv", - "cve": "CVE-2022-41910", - "id": "pyup.io-56641", - "more_info_path": "/vulnerabilities/CVE-2022-41910/56641", + "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41886: When 'tf.raw_ops.ImageProjectiveTransformV2' is given a large output shape, it overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-54pp-c6pp-7fpx", + "cve": "CVE-2022-41886", + "id": "pyup.io-56640", + "more_info_path": "/vulnerabilities/CVE-2022-41886/56640", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62631,10 +63009,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41891: If 'tf.raw_ops.TensorListConcat' is given 'element_shape=[]', it results segmentation fault which can be used to trigger a denial of service attack.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-66vq-54fq-6jvv", - "cve": "CVE-2022-41891", - "id": "pyup.io-56624", - "more_info_path": "/vulnerabilities/CVE-2022-41891/56624", + "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41900: The security vulnerability results in FractionalMax(AVG)Pool with illegal pooling_ratio. Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or remote code execution.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvwp-h6jv-7472", + "cve": "CVE-2022-41900", + "id": "pyup.io-56632", + "more_info_path": "/vulnerabilities/CVE-2022-41900/56632", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62643,10 +63021,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41901: An input 'sparse_matrix' that is not a matrix with a shape with rank 0 will trigger a 'CHECK' fail in 'tf.raw_ops.SparseMatrixNNZ'.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g9fm-r5mm-rf9f", - "cve": "CVE-2022-41901", - "id": "pyup.io-56622", - "more_info_path": "/vulnerabilities/CVE-2022-41901/56622", + "advisory": "Intel-tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41884: If a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-jq6x-99hj-q636", + "cve": "CVE-2022-41884", + "id": "pyup.io-56638", + "more_info_path": "/vulnerabilities/CVE-2022-41884/56638", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62655,10 +63033,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41899: TensorFlow is an open source platform for machine learning. Inputs 'dense_features' or 'example_state_data' not of rank 2 will trigger a 'CHECK' fail in 'SdcaOptimizer'.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-27rc-728f-x5w2", - "cve": "CVE-2022-41899", - "id": "pyup.io-56628", - "more_info_path": "/vulnerabilities/CVE-2022-41899/56628", + "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41901: An input 'sparse_matrix' that is not a matrix with a shape with rank 0 will trigger a 'CHECK' fail in 'tf.raw_ops.SparseMatrixNNZ'.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g9fm-r5mm-rf9f", + "cve": "CVE-2022-41901", + "id": "pyup.io-56622", + "more_info_path": "/vulnerabilities/CVE-2022-41901/56622", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62667,10 +63045,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "TensorFlow is an open source platform for machine learning. The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered. We have patched the issue in GitHub commit a65411a1d69edfb16b25907ffb8f73556ce36bb7. The fix will be included in TensorFlow 2.11.0. We will also cherrypick this commit on TensorFlow 2.8.4, 2.9.3, and 2.10.1.", - "cve": "CVE-2022-41902", - "id": "pyup.io-56636", - "more_info_path": "/vulnerabilities/CVE-2022-41902/56636", + "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41891: If 'tf.raw_ops.TensorListConcat' is given 'element_shape=[]', it results segmentation fault which can be used to trigger a denial of service attack.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-66vq-54fq-6jvv", + "cve": "CVE-2022-41891", + "id": "pyup.io-56624", + "more_info_path": "/vulnerabilities/CVE-2022-41891/56624", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -62740,6 +63118,17 @@ ], "v": ">=2.0.0a0,<2.0.1,<1.15.2" }, + { + "advisory": "Intel-tensorflow versions 1.15.2 and 2.0.1 includes a fix for CVE-2020-5215: In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value. Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions. This can be easily reproduced by tf.constant(\"hello\", tf.float16), if eager execution is enabled.", + "cve": "CVE-2020-5215", + "id": "pyup.io-57071", + "more_info_path": "/vulnerabilities/CVE-2020-5215/57071", + "specs": [ + ">=2.0.0a0,<2.0.1", + "<1.15.2" + ], + "v": ">=2.0.0a0,<2.0.1,<1.15.2" + }, { "advisory": "Intel-tensorflow versions 1.15.2 and 2.0.1 update its dependency \"SQLite\" to handle CVE-2019-19646.", "cve": "CVE-2019-19646", @@ -62773,17 +63162,6 @@ ], "v": ">=2.0.0a0,<2.0.1,<1.15.2" }, - { - "advisory": "Intel-tensorflow versions 1.15.2 and 2.0.1 includes a fix for CVE-2020-5215: In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value. Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions. This can be easily reproduced by tf.constant(\"hello\", tf.float16), if eager execution is enabled.", - "cve": "CVE-2020-5215", - "id": "pyup.io-57071", - "more_info_path": "/vulnerabilities/CVE-2020-5215/57071", - "specs": [ - ">=2.0.0a0,<2.0.1", - "<1.15.2" - ], - "v": ">=2.0.0a0,<2.0.1,<1.15.2" - }, { "advisory": "Intel-tensorflow versions 1.15.2 and 2.0.1 updates its dependency \"curl\" to handle CVE-2019-5482.", "cve": "CVE-2019-5482", @@ -62820,10 +63198,10 @@ "v": ">=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow versions 2.2.1 and 2.3.1 include a fix for CVE-2020-15192: In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes a list of strings to 'dlpack.to_dlpack' there is a memory leak following an expected validation failure. The issue occurs because the 'status' argument during validation failures is not properly checked. Since each of the above methods can return an error status, the 'status' value must be checked before continuing.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fxw-76px-3rxv", - "cve": "CVE-2020-15192", - "id": "pyup.io-57055", - "more_info_path": "/vulnerabilities/CVE-2020-15192/57055", + "advisory": "Intel-tensorflow versions 2.2.1 and 2.3.1 include a fix for CVE-2020-15193: In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of \"dlpack.to_dlpack\" can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a \"reinterpret_cast\". Since the \"PyObject\" is a Python object, not a Tensorflow tensor, the cast to \"EagerTensor\" fails. The issue was patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjjg-hgv6-h69v", + "cve": "CVE-2020-15193", + "id": "pyup.io-57056", + "more_info_path": "/vulnerabilities/CVE-2020-15193/57056", "specs": [ ">=2.2.0rc0,<2.2.1", ">=2.3.0rc0,<2.3.1" @@ -62831,10 +63209,10 @@ "v": ">=2.2.0rc0,<2.2.1,>=2.3.0rc0,<2.3.1" }, { - "advisory": "Intel-tensorflow versions 2.2.1 and 2.3.1 include a fix for CVE-2020-15193: In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of \"dlpack.to_dlpack\" can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a \"reinterpret_cast\". Since the \"PyObject\" is a Python object, not a Tensorflow tensor, the cast to \"EagerTensor\" fails. The issue was patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjjg-hgv6-h69v", - "cve": "CVE-2020-15193", - "id": "pyup.io-57056", - "more_info_path": "/vulnerabilities/CVE-2020-15193/57056", + "advisory": "Intel-tensorflow versions 2.2.1 and 2.3.1 include a fix for CVE-2020-15191: In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to 'dlpack.to_dlpack' the expected validations will cause variables to bind to 'nullptr' while setting a 'status' variable to the error condition. However, this 'status' argument is not properly checked. Hence, code following these methods will bind references to null pointers. This is undefined behavior and reported as an error if compiling with '-fsanitize=null'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-q8qj-fc9q-cphr", + "cve": "CVE-2020-15191", + "id": "pyup.io-57051", + "more_info_path": "/vulnerabilities/CVE-2020-15191/57051", "specs": [ ">=2.2.0rc0,<2.2.1", ">=2.3.0rc0,<2.3.1" @@ -62853,10 +63231,10 @@ "v": ">=2.2.0rc0,<2.2.1,>=2.3.0rc0,<2.3.1" }, { - "advisory": "Intel-tensorflow versions 2.2.1 and 2.3.1 include a fix for CVE-2020-15191: In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to 'dlpack.to_dlpack' the expected validations will cause variables to bind to 'nullptr' while setting a 'status' variable to the error condition. However, this 'status' argument is not properly checked. Hence, code following these methods will bind references to null pointers. This is undefined behavior and reported as an error if compiling with '-fsanitize=null'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-q8qj-fc9q-cphr", - "cve": "CVE-2020-15191", - "id": "pyup.io-57051", - "more_info_path": "/vulnerabilities/CVE-2020-15191/57051", + "advisory": "Intel-tensorflow versions 2.2.1 and 2.3.1 includes a fix for CVE-2020-15212: In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to \"segment_ids_data\" can alter \"output_index\" and then write to outside of \"output_data\" buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue was patched in commit 204945b19e44b57906c9344c0d00120eeeae178a. A potential workaround is to add a custom \"Verifier\" to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.", + "cve": "CVE-2020-15212", + "id": "pyup.io-57053", + "more_info_path": "/vulnerabilities/CVE-2020-15212/57053", "specs": [ ">=2.2.0rc0,<2.2.1", ">=2.3.0rc0,<2.3.1" @@ -62864,10 +63242,10 @@ "v": ">=2.2.0rc0,<2.2.1,>=2.3.0rc0,<2.3.1" }, { - "advisory": "Intel-tensorflow versions 2.2.1 and 2.3.1 includes a fix for CVE-2020-15212: In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to \"segment_ids_data\" can alter \"output_index\" and then write to outside of \"output_data\" buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue was patched in commit 204945b19e44b57906c9344c0d00120eeeae178a. A potential workaround is to add a custom \"Verifier\" to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.", - "cve": "CVE-2020-15212", - "id": "pyup.io-57053", - "more_info_path": "/vulnerabilities/CVE-2020-15212/57053", + "advisory": "Intel-tensorflow versions 2.2.1 and 2.3.1 include a fix for CVE-2020-15192: In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes a list of strings to 'dlpack.to_dlpack' there is a memory leak following an expected validation failure. The issue occurs because the 'status' argument during validation failures is not properly checked. Since each of the above methods can return an error status, the 'status' value must be checked before continuing.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fxw-76px-3rxv", + "cve": "CVE-2020-15192", + "id": "pyup.io-57055", + "more_info_path": "/vulnerabilities/CVE-2020-15192/57055", "specs": [ ">=2.2.0rc0,<2.2.1", ">=2.3.0rc0,<2.3.1" @@ -62885,16 +63263,6 @@ ], "v": ">=2.2.0rc0,<2.2.1,>=2.3.0rc0,<2.3.1" }, - { - "advisory": "Intel-tensorflow version 2.3.1 includes a fix for CVE-2020-15196: In Tensorflow version 2.3.0, the \"SparseCountSparseOutput\" and \"RaggedCountSparseOutput\" implementations don't validate that the \"weights\" tensor has the same shape as the data. The check exists for \"DenseCountSparseOutput\", where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue was patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pg59-2f92-5cph", - "cve": "CVE-2020-15196", - "id": "pyup.io-57047", - "more_info_path": "/vulnerabilities/CVE-2020-15196/57047", - "specs": [ - ">=2.3.0rc0,<2.3.1" - ], - "v": ">=2.3.0rc0,<2.3.1" - }, { "advisory": "Intel-tensorflow 2.3.1 includes a fix for CVE-2020-15198: In Tensorflow before version 2.3.1, the \"SparseCountSparseOutput\" implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the \"indices\" tensor has the same shape as the \"values\" one. The values in these tensors are always accessed in parallel. Thus, a shape mismatch can result in accesses outside the bounds of heap allocated buffers. The issue was patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-jc87-6vpp-7ff3", "cve": "CVE-2020-15198", @@ -62905,16 +63273,6 @@ ], "v": ">=2.3.0rc0,<2.3.1" }, - { - "advisory": "Intel-tensorflow 2.3.1 includes a fix for CVE-2020-15200: In Tensorflow before version 2.3.1, the \"RaggedCountSparseOutput\" implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the \"splits\" tensor generate a valid partitioning of the \"values\" tensor. Thus, the code sets up conditions to cause a heap buffer overflow. A \"BatchedMap\" is equivalent to a vector where each element is a hashmap. However, if the first element of \"splits_values\" is not 0, \"batch_idx\" will never be 1, hence there will be no hashmap at index 0 in \"per_batch_counts\". Trying to access that in the user code results in a segmentation fault. The issue was patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x7rp-74x2-mjf3", - "cve": "CVE-2020-15200", - "id": "pyup.io-57048", - "more_info_path": "/vulnerabilities/CVE-2020-15200/57048", - "specs": [ - ">=2.3.0rc0,<2.3.1" - ], - "v": ">=2.3.0rc0,<2.3.1" - }, { "advisory": "Intel-tensorflow 2.3.1 includes a fix for CVE-2020-15199: In Tensorflow before version 2.3.1, the \"RaggedCountSparseOutput\" does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the \"splits\" tensor has the minimum required number of elements. Code uses this quantity to initialize a different data structure. Since \"BatchedMap\" is equivalent to a vector, it needs to have at least one element to not be \"nullptr\". If user passes a \"splits\" tensor that is empty or has exactly one element, we get a \"SIGABRT\" signal raised by the operating system. The issue was patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x5cp-9pcf-pp3h", "cve": "CVE-2020-15199", @@ -62926,10 +63284,10 @@ "v": ">=2.3.0rc0,<2.3.1" }, { - "advisory": "Intel-tensorflow 2.3.1 includes a fix for CVE-2020-15201: In Tensorflow before version 2.3.1, the \"RaggedCountSparseOutput\" implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the \"splits\" tensor generate a valid partitioning of the \"values\" tensor. Hence, the code is prone to heap buffer overflow. If \"split_values\" does not end with a value at least \"num_values\" then the \"while\" loop condition will trigger a read outside of the bounds of \"split_values\" once \"batch_idx\" grows too large. The issue was patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4", - "cve": "CVE-2020-15201", - "id": "pyup.io-57050", - "more_info_path": "/vulnerabilities/CVE-2020-15201/57050", + "advisory": "Intel-tensorflow 2.3.1 includes a fix for CVE-2020-15200: In Tensorflow before version 2.3.1, the \"RaggedCountSparseOutput\" implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the \"splits\" tensor generate a valid partitioning of the \"values\" tensor. Thus, the code sets up conditions to cause a heap buffer overflow. A \"BatchedMap\" is equivalent to a vector where each element is a hashmap. However, if the first element of \"splits_values\" is not 0, \"batch_idx\" will never be 1, hence there will be no hashmap at index 0 in \"per_batch_counts\". Trying to access that in the user code results in a segmentation fault. The issue was patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x7rp-74x2-mjf3", + "cve": "CVE-2020-15200", + "id": "pyup.io-57048", + "more_info_path": "/vulnerabilities/CVE-2020-15200/57048", "specs": [ ">=2.3.0rc0,<2.3.1" ], @@ -62946,36 +63304,30 @@ "v": ">=2.3.0rc0,<2.3.1" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37680: In affected versions the implementation of fully connected layers in TFLite is vulnerable to a division by zero error (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/fully_connected.cc#L226). The Tensorflow team has patched the issue in GitHub commit 718721986aa137691ee23f03638867151f74935f.", - "cve": "CVE-2021-37680", - "id": "pyup.io-57006", - "more_info_path": "/vulnerabilities/CVE-2021-37680/57006", + "advisory": "Intel-tensorflow version 2.3.1 includes a fix for CVE-2020-15196: In Tensorflow version 2.3.0, the \"SparseCountSparseOutput\" and \"RaggedCountSparseOutput\" implementations don't validate that the \"weights\" tensor has the same shape as the data. The check exists for \"DenseCountSparseOutput\", where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue was patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pg59-2f92-5cph", + "cve": "CVE-2020-15196", + "id": "pyup.io-57047", + "more_info_path": "/vulnerabilities/CVE-2020-15196/57047", "specs": [ - ">=2.3.0rc0,<2.3.4", - ">=2.4.0rc0,<2.4.3", - ">=2.5.0rc0,<2.5.1", - ">=2.6.0rc0,<2.6.0" + ">=2.3.0rc0,<2.3.1" ], - "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" + "v": ">=2.3.0rc0,<2.3.1" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37683: In affected versions the implementation of division in TFLite is vulnerable to a division by 0 error (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/div.cc). There is no check that the divisor tensor does not contain zero elements. The Tensorflow team has patched the issue in GitHub commit 1e206baedf8bef0334cca3eb92bab134ef525a28.", - "cve": "CVE-2021-37683", - "id": "pyup.io-57004", - "more_info_path": "/vulnerabilities/CVE-2021-37683/57004", + "advisory": "Intel-tensorflow 2.3.1 includes a fix for CVE-2020-15201: In Tensorflow before version 2.3.1, the \"RaggedCountSparseOutput\" implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the \"splits\" tensor generate a valid partitioning of the \"values\" tensor. Hence, the code is prone to heap buffer overflow. If \"split_values\" does not end with a value at least \"num_values\" then the \"while\" loop condition will trigger a read outside of the bounds of \"split_values\" once \"batch_idx\" grows too large. The issue was patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4", + "cve": "CVE-2020-15201", + "id": "pyup.io-57050", + "more_info_path": "/vulnerabilities/CVE-2020-15201/57050", "specs": [ - ">=2.3.0rc0,<2.3.4", - ">=2.4.0rc0,<2.4.3", - ">=2.5.0rc0,<2.5.1", - ">=2.6.0rc0,<2.6.0" + ">=2.3.0rc0,<2.3.1" ], - "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" + "v": ">=2.3.0rc0,<2.3.1" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37637: It is possible to trigger a null pointer dereference in TensorFlow by passing an invalid input to `tf.raw_ops.CompressElement`. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/data/compression_utils.cc#L34) was accessing the size of a buffer obtained from the return of a separate function call before validating that said buffer is valid. The Tensorflow team has patched the issue in GitHub commit 5dc7f6981fdaf74c8c5be41f393df705841fb7c5.", - "cve": "CVE-2021-37637", - "id": "pyup.io-57005", - "more_info_path": "/vulnerabilities/CVE-2021-37637/57005", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37683: In affected versions the implementation of division in TFLite is vulnerable to a division by 0 error (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/div.cc). There is no check that the divisor tensor does not contain zero elements. The Tensorflow team has patched the issue in GitHub commit 1e206baedf8bef0334cca3eb92bab134ef525a28.", + "cve": "CVE-2021-37683", + "id": "pyup.io-57004", + "more_info_path": "/vulnerabilities/CVE-2021-37683/57004", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.4.0rc0,<2.4.3", @@ -62985,10 +63337,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37667: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in 'tf.raw_ops.UnicodeEncode'. The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/unicode_ops.cc#L533-L539) reads the first dimension of the 'input_splits' tensor before validating that this tensor is not empty. The Tensorflow team has patched the issue in GitHub commit 2e0ee46f1a47675152d3d865797a18358881d7a6.", - "cve": "CVE-2021-37667", - "id": "pyup.io-57003", - "more_info_path": "/vulnerabilities/CVE-2021-37667/57003", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37648: In affected versions the code for 'tf.raw_ops.SaveV2' does not properly validate the inputs and an attacker can trigger a null pointer dereference. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/save_restore_v2_ops.cc) uses 'ValidateInputs' to check that the input arguments are valid. This validation would have caught the illegal state represented by the reproducer above. However, the validation uses 'OP_REQUIRES' which translates to setting the 'Status' object of the current 'OpKernelContext' to an error status, followed by an empty 'return' statement which just terminates the execution of the function it is present in. However, this does not mean that the kernel execution is finalized: instead, execution continues from the nQext line in 'Compute' that follows the call to 'ValidateInputs'. This is equivalent to lacking the validation. The Tensorflow team has patched the issue in GitHub commit 9728c60e136912a12d99ca56e106b7cce7af5986.", + "cve": "CVE-2021-37648", + "id": "pyup.io-57001", + "more_info_path": "/vulnerabilities/CVE-2021-37648/57001", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.4.0rc0,<2.4.3", @@ -63011,10 +63363,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37642: In affected versions the implementation of 'tf.raw_ops.ResourceScatterDiv' is vulnerable to a division by 0 error. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/resource_variable_ops.cc#L865) uses a common class for all binary operations but fails to treat the division by 0 case separately. The Tensorflow team has patched the issue in GitHub commit 4aacb30888638da75023e6601149415b39763d76.", - "cve": "CVE-2021-37642", - "id": "pyup.io-57008", - "more_info_path": "/vulnerabilities/CVE-2021-37642/57008", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37689: In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. This is caused by the MLIR optimization of 'L2NormalizeReduceAxis' operator. The implementation (https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements. The Tensorflow team has patched the issue in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955.", + "cve": "CVE-2021-37689", + "id": "pyup.io-57000", + "more_info_path": "/vulnerabilities/CVE-2021-37689/57000", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.4.0rc0,<2.4.3", @@ -63024,10 +63376,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37689: In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. This is caused by the MLIR optimization of 'L2NormalizeReduceAxis' operator. The implementation (https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements. The Tensorflow team has patched the issue in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955.", - "cve": "CVE-2021-37689", - "id": "pyup.io-57000", - "more_info_path": "/vulnerabilities/CVE-2021-37689/57000", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37680: In affected versions the implementation of fully connected layers in TFLite is vulnerable to a division by zero error (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/fully_connected.cc#L226). The Tensorflow team has patched the issue in GitHub commit 718721986aa137691ee23f03638867151f74935f.", + "cve": "CVE-2021-37680", + "id": "pyup.io-57006", + "more_info_path": "/vulnerabilities/CVE-2021-37680/57006", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.4.0rc0,<2.4.3", @@ -63050,10 +63402,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37648: In affected versions the code for 'tf.raw_ops.SaveV2' does not properly validate the inputs and an attacker can trigger a null pointer dereference. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/save_restore_v2_ops.cc) uses 'ValidateInputs' to check that the input arguments are valid. This validation would have caught the illegal state represented by the reproducer above. However, the validation uses 'OP_REQUIRES' which translates to setting the 'Status' object of the current 'OpKernelContext' to an error status, followed by an empty 'return' statement which just terminates the execution of the function it is present in. However, this does not mean that the kernel execution is finalized: instead, execution continues from the nQext line in 'Compute' that follows the call to 'ValidateInputs'. This is equivalent to lacking the validation. The Tensorflow team has patched the issue in GitHub commit 9728c60e136912a12d99ca56e106b7cce7af5986.", - "cve": "CVE-2021-37648", - "id": "pyup.io-57001", - "more_info_path": "/vulnerabilities/CVE-2021-37648/57001", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37667: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in 'tf.raw_ops.UnicodeEncode'. The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/unicode_ops.cc#L533-L539) reads the first dimension of the 'input_splits' tensor before validating that this tensor is not empty. The Tensorflow team has patched the issue in GitHub commit 2e0ee46f1a47675152d3d865797a18358881d7a6.", + "cve": "CVE-2021-37667", + "id": "pyup.io-57003", + "more_info_path": "/vulnerabilities/CVE-2021-37667/57003", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.4.0rc0,<2.4.3", @@ -63076,23 +63428,36 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37651: In affected versions the implementation for 'tf.raw_ops.FractionalAvgPoolGrad' can be tricked into accessing data outside of bounds of heap allocated buffers. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty 'EigenDoubleMatrixMap' and then accesses this buffer with indices that are outside of the empty area. The Tensorflow team has patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30.", - "cve": "CVE-2021-37651", - "id": "pyup.io-56991", - "more_info_path": "/vulnerabilities/CVE-2021-37651/56991", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37637: It is possible to trigger a null pointer dereference in TensorFlow by passing an invalid input to `tf.raw_ops.CompressElement`. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/data/compression_utils.cc#L34) was accessing the size of a buffer obtained from the return of a separate function call before validating that said buffer is valid. The Tensorflow team has patched the issue in GitHub commit 5dc7f6981fdaf74c8c5be41f393df705841fb7c5.", + "cve": "CVE-2021-37637", + "id": "pyup.io-57005", + "more_info_path": "/vulnerabilities/CVE-2021-37637/57005", "specs": [ ">=2.3.0rc0,<2.3.4", + ">=2.4.0rc0,<2.4.3", ">=2.5.0rc0,<2.5.1", + ">=2.6.0rc0,<2.6.0" + ], + "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" + }, + { + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37642: In affected versions the implementation of 'tf.raw_ops.ResourceScatterDiv' is vulnerable to a division by 0 error. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/resource_variable_ops.cc#L865) uses a common class for all binary operations but fails to treat the division by 0 case separately. The Tensorflow team has patched the issue in GitHub commit 4aacb30888638da75023e6601149415b39763d76.", + "cve": "CVE-2021-37642", + "id": "pyup.io-57008", + "more_info_path": "/vulnerabilities/CVE-2021-37642/57008", + "specs": [ + ">=2.3.0rc0,<2.3.4", ">=2.4.0rc0,<2.4.3", + ">=2.5.0rc0,<2.5.1", ">=2.6.0rc0,<2.6.0" ], - "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" + "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37654: In affected versions an attacker can trigger a crash via a 'CHECK'-fail in debug builds of TensorFlow using 'tf.raw_ops.ResourceGather' or a read from outside the bounds of heap allocated data in the same API in a release build. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L660-L668) does not check that the 'batch_dims' value that the user supplies is less than the rank of the input tensor. Since the implementation uses several for loops over the dimensions of 'tensor', this results in reading data from outside the bounds of heap allocated buffer backing the tensor. The Tensorflow team has patched the issue in GitHub commit bc9c546ce7015c57c2f15c168b3d9201de679a1d.", - "cve": "CVE-2021-37654", - "id": "pyup.io-56990", - "more_info_path": "/vulnerabilities/CVE-2021-37654/56990", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37659: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/cwise_ops_common.h#L264) assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr. We have patched the issue in GitHub commit 93f428fd1768df147171ed674fee1fc5ab8309ec.", + "cve": "CVE-2021-37659", + "id": "pyup.io-56996", + "more_info_path": "/vulnerabilities/CVE-2021-37659/56996", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.5.0rc0,<2.5.1", @@ -63102,10 +63467,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37652: In affected versions the implementation for 'tf.raw_ops.BoostedTreesCreateEnsemble' can result in a use after free error if an attacker supplies specially crafted arguments. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/boosted_trees/resource_ops.cc#L55) uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent 'free'-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed. The Tensorflow team has patched the issue in GitHub commit 5ecec9c6fbdbc6be03295685190a45e7eee726ab.", - "cve": "CVE-2021-37652", - "id": "pyup.io-56988", - "more_info_path": "/vulnerabilities/CVE-2021-37652/56988", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37647: When a user does not supply arguments that determine a valid sparse tensor, 'tf.raw_ops.SparseTensorSliceDataset' implementation can be made to dereference a null pointer. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L240-L251) has some argument validation but fails to consider the case when either 'indices' or 'values' are provided for an empty sparse tensor when the other is not. If 'indices' is empty, then code that performs validation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L260-L261) (i.e., checking that the indices are monotonically increasing) results in a null pointer dereference. If 'indices' as provided by the user is empty, then 'indices' in the C++ code above is backed by an empty 'std::vector', hence calling 'indices->dim_size(0)' results in null pointer dereferencing (same as calling 'std::vector::at()' on an empty vector). The Tensorflow team has patched the issue in GitHub commit 02cc160e29d20631de3859c6653184e3f876b9d7.", + "cve": "CVE-2021-37647", + "id": "pyup.io-56998", + "more_info_path": "/vulnerabilities/CVE-2021-37647/56998", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.5.0rc0,<2.5.1", @@ -63115,10 +63480,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37638: Sending invalid argument for 'row_partition_types' of 'tf.raw_ops.RaggedTensorToTensor' API results in a null pointer dereference and undefined behavior. The implementation (https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L328) accesses the first element of a user supplied list of values without validating that the provided list is not empty. The Tensorflow team has patched the issue in GitHub commit 301ae88b331d37a2a16159b65b255f4f9eb39314.", - "cve": "CVE-2021-37638", - "id": "pyup.io-56989", - "more_info_path": "/vulnerabilities/CVE-2021-37638/56989", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37651: In affected versions the implementation for 'tf.raw_ops.FractionalAvgPoolGrad' can be tricked into accessing data outside of bounds of heap allocated buffers. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty 'EigenDoubleMatrixMap' and then accesses this buffer with indices that are outside of the empty area. The Tensorflow team has patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30.", + "cve": "CVE-2021-37651", + "id": "pyup.io-56991", + "more_info_path": "/vulnerabilities/CVE-2021-37651/56991", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.5.0rc0,<2.5.1", @@ -63128,10 +63493,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37664: In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `BoostedTreesSparseCalculateBestFeatureSplit`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) needs to validate that each value in `stats_summary_indices` is in range. The Tensorflow team has patched the issue in GitHub commit e84c975313e8e8e38bb2ea118196369c45c51378.", - "cve": "CVE-2021-37664", - "id": "pyup.io-56997", - "more_info_path": "/vulnerabilities/CVE-2021-37664/56997", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37654: In affected versions an attacker can trigger a crash via a 'CHECK'-fail in debug builds of TensorFlow using 'tf.raw_ops.ResourceGather' or a read from outside the bounds of heap allocated data in the same API in a release build. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L660-L668) does not check that the 'batch_dims' value that the user supplies is less than the rank of the input tensor. Since the implementation uses several for loops over the dimensions of 'tensor', this results in reading data from outside the bounds of heap allocated buffer backing the tensor. The Tensorflow team has patched the issue in GitHub commit bc9c546ce7015c57c2f15c168b3d9201de679a1d.", + "cve": "CVE-2021-37654", + "id": "pyup.io-56990", + "more_info_path": "/vulnerabilities/CVE-2021-37654/56990", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.5.0rc0,<2.5.1", @@ -63167,10 +63532,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37659: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/cwise_ops_common.h#L264) assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr. We have patched the issue in GitHub commit 93f428fd1768df147171ed674fee1fc5ab8309ec.", - "cve": "CVE-2021-37659", - "id": "pyup.io-56996", - "more_info_path": "/vulnerabilities/CVE-2021-37659/56996", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37639: When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer. Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration. The implementation (https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the 'tensor_name' user controlled input and immediately retrieves the tensor at the restoration index (controlled via 'preferred_shard' argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements and if the restoration index is outside the bounds, this results in heap OOB read. The Tensorflow team has patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622.", + "cve": "CVE-2021-37639", + "id": "pyup.io-56993", + "more_info_path": "/vulnerabilities/CVE-2021-37639/56993", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.5.0rc0,<2.5.1", @@ -63180,10 +63545,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37639: When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer. Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration. The implementation (https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the 'tensor_name' user controlled input and immediately retrieves the tensor at the restoration index (controlled via 'preferred_shard' argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements and if the restoration index is outside the bounds, this results in heap OOB read. The Tensorflow team has patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622.", - "cve": "CVE-2021-37639", - "id": "pyup.io-56993", - "more_info_path": "/vulnerabilities/CVE-2021-37639/56993", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37664: In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `BoostedTreesSparseCalculateBestFeatureSplit`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) needs to validate that each value in `stats_summary_indices` is in range. The Tensorflow team has patched the issue in GitHub commit e84c975313e8e8e38bb2ea118196369c45c51378.", + "cve": "CVE-2021-37664", + "id": "pyup.io-56997", + "more_info_path": "/vulnerabilities/CVE-2021-37664/56997", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.5.0rc0,<2.5.1", @@ -63206,10 +63571,23 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37647: When a user does not supply arguments that determine a valid sparse tensor, 'tf.raw_ops.SparseTensorSliceDataset' implementation can be made to dereference a null pointer. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L240-L251) has some argument validation but fails to consider the case when either 'indices' or 'values' are provided for an empty sparse tensor when the other is not. If 'indices' is empty, then code that performs validation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L260-L261) (i.e., checking that the indices are monotonically increasing) results in a null pointer dereference. If 'indices' as provided by the user is empty, then 'indices' in the C++ code above is backed by an empty 'std::vector', hence calling 'indices->dim_size(0)' results in null pointer dereferencing (same as calling 'std::vector::at()' on an empty vector). The Tensorflow team has patched the issue in GitHub commit 02cc160e29d20631de3859c6653184e3f876b9d7.", - "cve": "CVE-2021-37647", - "id": "pyup.io-56998", - "more_info_path": "/vulnerabilities/CVE-2021-37647/56998", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37652: In affected versions the implementation for 'tf.raw_ops.BoostedTreesCreateEnsemble' can result in a use after free error if an attacker supplies specially crafted arguments. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/boosted_trees/resource_ops.cc#L55) uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent 'free'-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed. The Tensorflow team has patched the issue in GitHub commit 5ecec9c6fbdbc6be03295685190a45e7eee726ab.", + "cve": "CVE-2021-37652", + "id": "pyup.io-56988", + "more_info_path": "/vulnerabilities/CVE-2021-37652/56988", + "specs": [ + ">=2.3.0rc0,<2.3.4", + ">=2.5.0rc0,<2.5.1", + ">=2.4.0rc0,<2.4.3", + ">=2.6.0rc0,<2.6.0" + ], + "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" + }, + { + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37638: Sending invalid argument for 'row_partition_types' of 'tf.raw_ops.RaggedTensorToTensor' API results in a null pointer dereference and undefined behavior. The implementation (https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L328) accesses the first element of a user supplied list of values without validating that the provided list is not empty. The Tensorflow team has patched the issue in GitHub commit 301ae88b331d37a2a16159b65b255f4f9eb39314.", + "cve": "CVE-2021-37638", + "id": "pyup.io-56989", + "more_info_path": "/vulnerabilities/CVE-2021-37638/56989", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.5.0rc0,<2.5.1", @@ -63266,10 +63644,10 @@ "v": ">=2.5.0,<2.13.0" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29539: TensorFlow is an end-to-end open source platform for machine learning. Calling `tf.raw_ops.ImmutableConst`(https://www.tensorflow.org/api_docs/python/tf/raw_ops/ImmutableConst) with a `dtype` of `tf.resource` or `tf.variant` results in a segfault in the implementation as code assumes that the tensor contents are pure scalars. We have patched the issue in 4f663d4b8f0bec1b48da6fa091a7d29609980fa4 and will release TensorFlow 2.5.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. If using `tf.raw_ops.ImmutableConst` in code, you can prevent the segfault by inserting a filter for the `dtype` argument.", - "cve": "CVE-2021-29539", - "id": "pyup.io-56985", - "more_info_path": "/vulnerabilities/CVE-2021-29539/56985", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29549: An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero. Since `vector_num_elements` is determined based on input shapes (https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0.", + "cve": "CVE-2021-29549", + "id": "pyup.io-56982", + "more_info_path": "/vulnerabilities/CVE-2021-29549/56982", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63294,10 +63672,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29552: An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar. Since the tensor is empty the `CHECK` involved in `.scalar()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination.", - "cve": "CVE-2021-29552", - "id": "pyup.io-56973", - "more_info_path": "/vulnerabilities/CVE-2021-29552/56973", + "advisory": "Intel-tensorflow 2.5.0, 2.4.2, 2.3.3, 2.2.3 and 2.1.4 include a fix for CVE-2021-29548: An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc) does not validate all constraints specified in the op's contract (https://www.tensorflow.org/api_docs/python/tf/raw_ops/QuantizedBatchNormWithGlobalNormalization).", + "cve": "CVE-2021-29548", + "id": "pyup.io-56974", + "more_info_path": "/vulnerabilities/CVE-2021-29548/56974", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63308,10 +63686,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.5.0, 2.4.2, 2.3.3, 2.2.3 and 2.1.4 include a fix for CVE-2021-29548: An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc) does not validate all constraints specified in the op's contract (https://www.tensorflow.org/api_docs/python/tf/raw_ops/QuantizedBatchNormWithGlobalNormalization).", - "cve": "CVE-2021-29548", - "id": "pyup.io-56974", - "more_info_path": "/vulnerabilities/CVE-2021-29548/56974", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29539: TensorFlow is an end-to-end open source platform for machine learning. Calling `tf.raw_ops.ImmutableConst`(https://www.tensorflow.org/api_docs/python/tf/raw_ops/ImmutableConst) with a `dtype` of `tf.resource` or `tf.variant` results in a segfault in the implementation as code assumes that the tensor contents are pure scalars. We have patched the issue in 4f663d4b8f0bec1b48da6fa091a7d29609980fa4 and will release TensorFlow 2.5.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. If using `tf.raw_ops.ImmutableConst` in code, you can prevent the segfault by inserting a filter for the `dtype` argument.", + "cve": "CVE-2021-29539", + "id": "pyup.io-56985", + "more_info_path": "/vulnerabilities/CVE-2021-29539/56985", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63322,10 +63700,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29549: An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero. Since `vector_num_elements` is determined based on input shapes (https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0.", - "cve": "CVE-2021-29549", - "id": "pyup.io-56982", - "more_info_path": "/vulnerabilities/CVE-2021-29549/56982", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29537: An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly.", + "cve": "CVE-2021-29537", + "id": "pyup.io-56983", + "more_info_path": "/vulnerabilities/CVE-2021-29537/56983", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63336,10 +63714,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29532: An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to `tf.raw_ops.RaggedCross`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/efea03b38fb8d3b81762237dc85e579cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments. Each of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads.", - "cve": "CVE-2021-29532", - "id": "pyup.io-56968", - "more_info_path": "/vulnerabilities/CVE-2021-29532/56968", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail in 'tf.raw_ops.EncodePng'. See CVE-2021-29531.", + "cve": "CVE-2021-29531", + "id": "pyup.io-56979", + "more_info_path": "/vulnerabilities/CVE-2021-29531/56979", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63350,10 +63728,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a division by 0 in 'Conv2DBackpropFilter'. See CVE-2021-29524.", - "cve": "CVE-2021-29524", - "id": "pyup.io-56969", - "more_info_path": "/vulnerabilities/CVE-2021-29524/56969", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by invalid validation in 'SparseMatrixSparseCholesky'. See CVE-2021-29530.", + "cve": "CVE-2021-29530", + "id": "pyup.io-56964", + "more_info_path": "/vulnerabilities/CVE-2021-29530/56964", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63364,10 +63742,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix vulnerabilities where session operations in eager mode lead to null pointer dereferences. See CVE-2021-29518.", - "cve": "CVE-2021-29518", - "id": "pyup.io-56970", - "more_info_path": "/vulnerabilities/CVE-2021-29518/56970", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a division by 0 in 'Conv2DBackpropInput'. See CVE-2021-29525.", + "cve": "CVE-2021-29525", + "id": "pyup.io-56965", + "more_info_path": "/vulnerabilities/CVE-2021-29525/56965", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63378,10 +63756,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a 'CHECK'-fail in 'AddManySparseToTensorsMap'. See CVE-2021-29523.", - "cve": "CVE-2021-29523", - "id": "pyup.io-56971", - "more_info_path": "/vulnerabilities/CVE-2021-29523/56971", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a 'CHECK'-fail in 'SparseCross' caused by type confusion. See CVE-2021-29519.", + "cve": "CVE-2021-29519", + "id": "pyup.io-56966", + "more_info_path": "/vulnerabilities/CVE-2021-29519/56966", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63392,10 +63770,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix fixes a heap buffer overflow caused by rounding. See CVE-2021-29529.", - "cve": "CVE-2021-29529", - "id": "pyup.io-56972", - "more_info_path": "/vulnerabilities/CVE-2021-29529/56972", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29513: Calling TF operations with tensors of non-numeric types when the operations expect numeric tensors result in null pointer dereferences. The conversion from Python array to C++ array (https://github.com/tensorflow/tensorflow/blob/ff70c47a396ef1e3cb73c90513da4f5cb71bebba/tensorflow/python/lib/core/ndarray_tensor.cc#L113-L169) is vulnerable to a type confusion.", + "cve": "CVE-2021-29513", + "id": "pyup.io-56978", + "more_info_path": "/vulnerabilities/CVE-2021-29513/56978", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63406,10 +63784,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a segfault in 'SparseCountSparseOutput'. See CVE-2021-29521.", - "cve": "CVE-2021-29521", - "id": "pyup.io-56980", - "more_info_path": "/vulnerabilities/CVE-2021-29521/56980", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a division by 0 in 'Conv2DBackpropFilter'. See CVE-2021-29524.", + "cve": "CVE-2021-29524", + "id": "pyup.io-56969", + "more_info_path": "/vulnerabilities/CVE-2021-29524/56969", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63420,10 +63798,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail in 'tf.raw_ops.EncodePng'. See CVE-2021-29531.", - "cve": "CVE-2021-29531", - "id": "pyup.io-56979", - "more_info_path": "/vulnerabilities/CVE-2021-29531/56979", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix vulnerabilities where session operations in eager mode lead to null pointer dereferences. See CVE-2021-29518.", + "cve": "CVE-2021-29518", + "id": "pyup.io-56970", + "more_info_path": "/vulnerabilities/CVE-2021-29518/56970", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63434,10 +63812,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a heap buffer overflow in 'Conv3DBackprop*'. See CVE-2021-29520.", - "cve": "CVE-2021-29520", - "id": "pyup.io-56976", - "more_info_path": "/vulnerabilities/CVE-2021-29520/56976", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a 'CHECK'-fail in 'AddManySparseToTensorsMap'. See CVE-2021-29523.", + "cve": "CVE-2021-29523", + "id": "pyup.io-56971", + "more_info_path": "/vulnerabilities/CVE-2021-29523/56971", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63448,10 +63826,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29513: Calling TF operations with tensors of non-numeric types when the operations expect numeric tensors result in null pointer dereferences. The conversion from Python array to C++ array (https://github.com/tensorflow/tensorflow/blob/ff70c47a396ef1e3cb73c90513da4f5cb71bebba/tensorflow/python/lib/core/ndarray_tensor.cc#L113-L169) is vulnerable to a type confusion.", - "cve": "CVE-2021-29513", - "id": "pyup.io-56978", - "more_info_path": "/vulnerabilities/CVE-2021-29513/56978", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix fixes a heap buffer overflow caused by rounding. See CVE-2021-29529.", + "cve": "CVE-2021-29529", + "id": "pyup.io-56972", + "more_info_path": "/vulnerabilities/CVE-2021-29529/56972", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63462,10 +63840,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29537: An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly.", - "cve": "CVE-2021-29537", - "id": "pyup.io-56983", - "more_info_path": "/vulnerabilities/CVE-2021-29537/56983", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a segfault in 'SparseCountSparseOutput'. See CVE-2021-29521.", + "cve": "CVE-2021-29521", + "id": "pyup.io-56980", + "more_info_path": "/vulnerabilities/CVE-2021-29521/56980", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63476,10 +63854,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by invalid validation in 'SparseMatrixSparseCholesky'. See CVE-2021-29530.", - "cve": "CVE-2021-29530", - "id": "pyup.io-56964", - "more_info_path": "/vulnerabilities/CVE-2021-29530/56964", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29552: An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar. Since the tensor is empty the `CHECK` involved in `.scalar()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination.", + "cve": "CVE-2021-29552", + "id": "pyup.io-56973", + "more_info_path": "/vulnerabilities/CVE-2021-29552/56973", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63490,10 +63868,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a division by 0 in 'Conv2DBackpropInput'. See CVE-2021-29525.", - "cve": "CVE-2021-29525", - "id": "pyup.io-56965", - "more_info_path": "/vulnerabilities/CVE-2021-29525/56965", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29534: An attacker can trigger a denial of service via a 'CHECK'-fail in 'tf.raw_ops.SparseConcat'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/b432a38fe0e1b4b904a6c222cbce794c39703e87/tensorflow/core/kernels/sparse_concat_op.cc#L76) takes the values specified in 'shapes[0]' as dimensions for the output shape. The 'TensorShape' constructor (https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a 'CHECK' operation which triggers when 'InitDims' (https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use 'BuildTensorShapeBase' or 'AddDimWithStatus' to prevent 'CHECK'-failures in the presence of overflows.", + "cve": "CVE-2021-29534", + "id": "pyup.io-56977", + "more_info_path": "/vulnerabilities/CVE-2021-29534/56977", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63504,10 +63882,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a division by 0 in 'Conv3DBackprop*'. See CVE-2021-29522.", - "cve": "CVE-2021-29522", - "id": "pyup.io-56967", - "more_info_path": "/vulnerabilities/CVE-2021-29522/56967", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a heap buffer overflow in 'Conv3DBackprop*'. See CVE-2021-29520.", + "cve": "CVE-2021-29520", + "id": "pyup.io-56976", + "more_info_path": "/vulnerabilities/CVE-2021-29520/56976", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63518,10 +63896,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a 'CHECK'-fail in 'SparseCross' caused by type confusion. See CVE-2021-29519.", - "cve": "CVE-2021-29519", - "id": "pyup.io-56966", - "more_info_path": "/vulnerabilities/CVE-2021-29519/56966", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a division by 0 in 'Conv3DBackprop*'. See CVE-2021-29522.", + "cve": "CVE-2021-29522", + "id": "pyup.io-56967", + "more_info_path": "/vulnerabilities/CVE-2021-29522/56967", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63546,10 +63924,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29534: An attacker can trigger a denial of service via a 'CHECK'-fail in 'tf.raw_ops.SparseConcat'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/b432a38fe0e1b4b904a6c222cbce794c39703e87/tensorflow/core/kernels/sparse_concat_op.cc#L76) takes the values specified in 'shapes[0]' as dimensions for the output shape. The 'TensorShape' constructor (https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a 'CHECK' operation which triggers when 'InitDims' (https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use 'BuildTensorShapeBase' or 'AddDimWithStatus' to prevent 'CHECK'-failures in the presence of overflows.", - "cve": "CVE-2021-29534", - "id": "pyup.io-56977", - "more_info_path": "/vulnerabilities/CVE-2021-29534/56977", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29532: An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to `tf.raw_ops.RaggedCross`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/efea03b38fb8d3b81762237dc85e579cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments. Each of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads.", + "cve": "CVE-2021-29532", + "id": "pyup.io-56968", + "more_info_path": "/vulnerabilities/CVE-2021-29532/56968", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63602,10 +63980,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'StringNGrams'. See CVE-2021-29542.", - "cve": "CVE-2021-29542", - "id": "pyup.io-56961", - "more_info_path": "/vulnerabilities/CVE-2021-29542/56961", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29536: An attacker can cause a heap buffer overflow in 'QuantizedReshape' by passing in invalid thresholds for the quantization. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then '.flat()' is an empty buffer and accessing the element at position 0 results in overflow.", + "cve": "CVE-2021-29536", + "id": "pyup.io-56960", + "more_info_path": "/vulnerabilities/CVE-2021-29536/56960", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63616,10 +63994,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2,>=2.2.0rc0,<2.2.3" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29536: An attacker can cause a heap buffer overflow in 'QuantizedReshape' by passing in invalid thresholds for the quantization. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then '.flat()' is an empty buffer and accessing the element at position 0 results in overflow.", - "cve": "CVE-2021-29536", - "id": "pyup.io-56960", - "more_info_path": "/vulnerabilities/CVE-2021-29536/56960", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'StringNGrams'. See CVE-2021-29542.", + "cve": "CVE-2021-29542", + "id": "pyup.io-56961", + "more_info_path": "/vulnerabilities/CVE-2021-29542/56961", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63630,10 +64008,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2,>=2.2.0rc0,<2.2.3" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29543: An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.CTCGreedyDecoder`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/1615440b17b364b875eb06f43d087381f1460a65/tensorflow/core/kernels/ctc_decoder_ops.cc#L37-L50) has a `CHECK_LT` inserted to validate some invariants. When this condition is false, the program aborts, instead of returning a valid error to the user. This abnormal termination can be weaponized in denial of service attacks.", - "cve": "CVE-2021-29543", - "id": "pyup.io-56958", - "more_info_path": "/vulnerabilities/CVE-2021-29543/56958", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29544: An attacker can trigger a denial of service via a 'CHECK'-fail in 'tf.raw_ops.QuantizeAndDequantizeV4Grad'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163) does not validate the rank of the 'input_*' tensors. In turn, this results in the tensors being passes as they are to 'QuantizeAndDequantizePerChannelGradientImpl' (https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306). However, the 'vec' method, requires the rank to 1 and triggers a 'CHECK' failure otherwise.", + "cve": "CVE-2021-29544", + "id": "pyup.io-56959", + "more_info_path": "/vulnerabilities/CVE-2021-29544/56959", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63644,10 +64022,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.4.0rc0,<2.4.2,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29544: An attacker can trigger a denial of service via a 'CHECK'-fail in 'tf.raw_ops.QuantizeAndDequantizeV4Grad'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163) does not validate the rank of the 'input_*' tensors. In turn, this results in the tensors being passes as they are to 'QuantizeAndDequantizePerChannelGradientImpl' (https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306). However, the 'vec' method, requires the rank to 1 and triggers a 'CHECK' failure otherwise.", - "cve": "CVE-2021-29544", - "id": "pyup.io-56959", - "more_info_path": "/vulnerabilities/CVE-2021-29544/56959", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29543: An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.CTCGreedyDecoder`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/1615440b17b364b875eb06f43d087381f1460a65/tensorflow/core/kernels/ctc_decoder_ops.cc#L37-L50) has a `CHECK_LT` inserted to validate some invariants. When this condition is false, the program aborts, instead of returning a valid error to the user. This abnormal termination can be weaponized in denial of service attacks.", + "cve": "CVE-2021-29543", + "id": "pyup.io-56958", + "more_info_path": "/vulnerabilities/CVE-2021-29543/56958", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -63686,10 +64064,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix fixes a null pointer dereference via invalid Ragged Tensors. See CVE-2021-29516.", - "cve": "CVE-2021-29516", - "id": "pyup.io-56954", - "more_info_path": "/vulnerabilities/CVE-2021-29516/56954", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in 'Conv3D'. See CVE-2021-29517.", + "cve": "CVE-2021-29517", + "id": "pyup.io-56955", + "more_info_path": "/vulnerabilities/CVE-2021-29517/56955", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.2.0rc0,<2.2.3", @@ -63700,10 +64078,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in 'Conv3D'. See CVE-2021-29517.", - "cve": "CVE-2021-29517", - "id": "pyup.io-56955", - "more_info_path": "/vulnerabilities/CVE-2021-29517/56955", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix fixes a null pointer dereference via invalid Ragged Tensors. See CVE-2021-29516.", + "cve": "CVE-2021-29516", + "id": "pyup.io-56954", + "more_info_path": "/vulnerabilities/CVE-2021-29516/56954", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.2.0rc0,<2.2.3", @@ -63770,10 +64148,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29545: An attacker can trigger a denial of service via a 'CHECK'-fail in converting sparse tensors to CSR Sparse matrices. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/800346f2c03a27e182dd4fba48295f65e7790739/tensorflow/core/kernels/sparse/kernels.cc#L66) does a double redirection to access an element of an array allocated on the heap. If the value at 'indices(i, 0)' is such that 'indices(i, 0) + 1' is outside the bounds of 'csr_row_ptr', this results in writing outside of bounds of heap allocated data.", - "cve": "CVE-2021-29545", - "id": "pyup.io-56949", - "more_info_path": "/vulnerabilities/CVE-2021-29545/56949", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29551: The implementation of 'MatrixTriangularSolve' (https://github.com/tensorflow/tensorflow/blob/8cae746d8449c7dda5298327353d68613f16e798/tensorflow/core/kernels/linalg/matrix_triangular_solve_op_impl.h#L160-L240) fails to terminate kernel execution if one validation condition fails.", + "cve": "CVE-2021-29551", + "id": "pyup.io-56948", + "more_info_path": "/vulnerabilities/CVE-2021-29551/56948", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.3.0rc0,<2.3.3", @@ -63784,10 +64162,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.4.0rc0,<2.4.2,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29551: The implementation of 'MatrixTriangularSolve' (https://github.com/tensorflow/tensorflow/blob/8cae746d8449c7dda5298327353d68613f16e798/tensorflow/core/kernels/linalg/matrix_triangular_solve_op_impl.h#L160-L240) fails to terminate kernel execution if one validation condition fails.", - "cve": "CVE-2021-29551", - "id": "pyup.io-56948", - "more_info_path": "/vulnerabilities/CVE-2021-29551/56948", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29545: An attacker can trigger a denial of service via a 'CHECK'-fail in converting sparse tensors to CSR Sparse matrices. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/800346f2c03a27e182dd4fba48295f65e7790739/tensorflow/core/kernels/sparse/kernels.cc#L66) does a double redirection to access an element of an array allocated on the heap. If the value at 'indices(i, 0)' is such that 'indices(i, 0) + 1' is outside the bounds of 'csr_row_ptr', this results in writing outside of bounds of heap allocated data.", + "cve": "CVE-2021-29545", + "id": "pyup.io-56949", + "more_info_path": "/vulnerabilities/CVE-2021-29545/56949", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.3.0rc0,<2.3.3", @@ -63868,10 +64246,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by incomplete validation in 'tf.raw_ops.CTCLoss'. See CVE-2021-29613.", - "cve": "CVE-2021-29613", - "id": "pyup.io-56942", - "more_info_path": "/vulnerabilities/CVE-2021-29613/56942", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29556: An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.Reverse`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/36229ea9e9451dac14a8b1f4711c435a1d84a594/tensorflow/core/kernels/reverse_op.cc#L75-L76) performs a division based on the first dimension of the tensor argument.", + "cve": "CVE-2021-29556", + "id": "pyup.io-56941", + "more_info_path": "/vulnerabilities/CVE-2021-29556/56941", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -63882,10 +64260,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29556: An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.Reverse`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/36229ea9e9451dac14a8b1f4711c435a1d84a594/tensorflow/core/kernels/reverse_op.cc#L75-L76) performs a division based on the first dimension of the tensor argument.", - "cve": "CVE-2021-29556", - "id": "pyup.io-56941", - "more_info_path": "/vulnerabilities/CVE-2021-29556/56941", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by incomplete validation in 'tf.raw_ops.CTCLoss'. See CVE-2021-29613.", + "cve": "CVE-2021-29613", + "id": "pyup.io-56942", + "more_info_path": "/vulnerabilities/CVE-2021-29613/56942", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -63910,10 +64288,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.1.0rc0,<2.1.4,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail in 'LoadAndRemapMatrix'. See CVE-2021-29561.", - "cve": "CVE-2021-29561", - "id": "pyup.io-56937", - "more_info_path": "/vulnerabilities/CVE-2021-29561/56937", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'QuantizedConv2D'. See CVE-2021-29527.", + "cve": "CVE-2021-29527", + "id": "pyup.io-56938", + "more_info_path": "/vulnerabilities/CVE-2021-29527/56938", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -63924,10 +64302,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4,>=2.3.0rc0,<2.3.3" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'QuantizedConv2D'. See CVE-2021-29527.", - "cve": "CVE-2021-29527", - "id": "pyup.io-56938", - "more_info_path": "/vulnerabilities/CVE-2021-29527/56938", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail in 'LoadAndRemapMatrix'. See CVE-2021-29561.", + "cve": "CVE-2021-29561", + "id": "pyup.io-56937", + "more_info_path": "/vulnerabilities/CVE-2021-29561/56937", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -63966,10 +64344,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'TransposeConv'. See CVE-2021-29588.", - "cve": "CVE-2021-29588", - "id": "pyup.io-56928", - "more_info_path": "/vulnerabilities/CVE-2021-29588/56928", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'BandedTriangularSolve'. See CVE-2021-29612.", + "cve": "CVE-2021-29612", + "id": "pyup.io-56930", + "more_info_path": "/vulnerabilities/CVE-2021-29612/56930", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -63980,10 +64358,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'BandedTriangularSolve'. See CVE-2021-29612.", - "cve": "CVE-2021-29612", - "id": "pyup.io-56930", - "more_info_path": "/vulnerabilities/CVE-2021-29612/56930", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'TransposeConv'. See CVE-2021-29588.", + "cve": "CVE-2021-29588", + "id": "pyup.io-56928", + "more_info_path": "/vulnerabilities/CVE-2021-29588/56928", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64022,10 +64400,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'FusedBatchNorm'. See CVE-2021-29555.", - "cve": "CVE-2021-29555", - "id": "pyup.io-56934", - "more_info_path": "/vulnerabilities/CVE-2021-29555/56934", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29589: The reference implementation of the `GatherNd` TFLite operator is vulnerable to a division by zero error (https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/reference_ops.h#L966). An attacker can craft a model such that `params` input would be an empty tensor. In turn, `params_shape.Dims(.)` would be zero, in at least one dimension.", + "cve": "CVE-2021-29589", + "id": "pyup.io-56932", + "more_info_path": "/vulnerabilities/CVE-2021-29589/56932", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64036,10 +64414,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29589: The reference implementation of the `GatherNd` TFLite operator is vulnerable to a division by zero error (https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/reference_ops.h#L966). An attacker can craft a model such that `params` input would be an empty tensor. In turn, `params_shape.Dims(.)` would be zero, in at least one dimension.", - "cve": "CVE-2021-29589", - "id": "pyup.io-56932", - "more_info_path": "/vulnerabilities/CVE-2021-29589/56932", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB access in unicode ops. See CVE-2021-29559.", + "cve": "CVE-2021-29559", + "id": "pyup.io-56933", + "more_info_path": "/vulnerabilities/CVE-2021-29559/56933", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64050,10 +64428,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB access in unicode ops. See CVE-2021-29559.", - "cve": "CVE-2021-29559", - "id": "pyup.io-56933", - "more_info_path": "/vulnerabilities/CVE-2021-29559/56933", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'FusedBatchNorm'. See CVE-2021-29555.", + "cve": "CVE-2021-29555", + "id": "pyup.io-56934", + "more_info_path": "/vulnerabilities/CVE-2021-29555/56934", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64078,10 +64456,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a null pointer dereference in 'SparseFillEmptyRows'. See CVE-2021-29565.", - "cve": "CVE-2021-29565", - "id": "pyup.io-56883", - "more_info_path": "/vulnerabilities/CVE-2021-29565/56883", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of hashtable lookup. See CVE-2021-29604.", + "cve": "CVE-2021-29604", + "id": "pyup.io-56905", + "more_info_path": "/vulnerabilities/CVE-2021-29604/56905", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64092,10 +64470,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29547: An attacker can cause a segfault and denial of service via accessing data outside of bounds in 'tf.raw_ops.QuantizedBatchNormWithGlobalNormalization'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, '.flat()' is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds.", - "cve": "CVE-2021-29547", - "id": "pyup.io-56903", - "more_info_path": "/vulnerabilities/CVE-2021-29547/56903", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a integer overflow in TFLite concatentation. See CVE-2021-29601.", + "cve": "CVE-2021-29601", + "id": "pyup.io-56913", + "more_info_path": "/vulnerabilities/CVE-2021-29601/56913", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64106,10 +64484,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of hashtable lookup. See CVE-2021-29604.", - "cve": "CVE-2021-29604", - "id": "pyup.io-56905", - "more_info_path": "/vulnerabilities/CVE-2021-29604/56905", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a segfault in 'tf.raw_ops.SparseCountSparseOutput'. See CVE-2021-29619.", + "cve": "CVE-2021-29619", + "id": "pyup.io-56888", + "more_info_path": "/vulnerabilities/CVE-2021-29619/56888", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64120,10 +64498,80 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 update 'curl' to '7.76.0' to handle CVE-2020-8177.", - "cve": "CVE-2020-8177", - "id": "pyup.io-56892", - "more_info_path": "/vulnerabilities/CVE-2020-8177/56892", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29616: The implementation of TrySimplify (https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc#L390-L401) has undefined behavior due to dereferencing a null pointer in corner cases that result in optimizing a node with no inputs.", + "cve": "CVE-2021-29616", + "id": "pyup.io-56884", + "more_info_path": "/vulnerabilities/CVE-2021-29616/56884", + "specs": [ + ">=2.5.0rc0,<2.5.0", + ">=2.4.0rc0,<2.4.2", + ">=2.3.0rc0,<2.3.3", + ">=2.2.0rc0,<2.2.3", + ">=2.1.0rc0,<2.1.4" + ], + "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" + }, + { + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by incomplete validation in 'SparseReshape'. See CVE-2021-29611.", + "cve": "CVE-2021-29611", + "id": "pyup.io-56921", + "more_info_path": "/vulnerabilities/CVE-2021-29611/56921", + "specs": [ + ">=2.5.0rc0,<2.5.0", + ">=2.4.0rc0,<2.4.2", + ">=2.3.0rc0,<2.3.3", + ">=2.2.0rc0,<2.2.3", + ">=2.1.0rc0,<2.1.4" + ], + "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" + }, + { + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'DenseCountSparseOutput'. See CVE-2021-29554.", + "cve": "CVE-2021-29554", + "id": "pyup.io-56895", + "more_info_path": "/vulnerabilities/CVE-2021-29554/56895", + "specs": [ + ">=2.5.0rc0,<2.5.0", + ">=2.4.0rc0,<2.4.2", + ">=2.3.0rc0,<2.3.3", + ">=2.2.0rc0,<2.2.3", + ">=2.1.0rc0,<2.1.4" + ], + "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" + }, + { + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'Split'. See CVE-2021-29599.", + "cve": "CVE-2021-29599", + "id": "pyup.io-56881", + "more_info_path": "/vulnerabilities/CVE-2021-29599/56881", + "specs": [ + ">=2.5.0rc0,<2.5.0", + ">=2.4.0rc0,<2.4.2", + ">=2.3.0rc0,<2.3.3", + ">=2.2.0rc0,<2.2.3", + ">=2.1.0rc0,<2.1.4" + ], + "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" + }, + { + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'FractionalAvgPoolGrad'. See CVE-2021-29578.", + "cve": "CVE-2021-29578", + "id": "pyup.io-56889", + "more_info_path": "/vulnerabilities/CVE-2021-29578/56889", + "specs": [ + ">=2.5.0rc0,<2.5.0", + ">=2.4.0rc0,<2.4.2", + ">=2.3.0rc0,<2.3.3", + ">=2.2.0rc0,<2.2.3", + ">=2.1.0rc0,<2.1.4" + ], + "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" + }, + { + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'DepthwiseConv'. See CVE-2021-29602.", + "cve": "CVE-2021-29602", + "id": "pyup.io-56890", + "more_info_path": "/vulnerabilities/CVE-2021-29602/56890", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64147,6 +64595,20 @@ ], "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, + { + "advisory": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in 'tf.raw_ops.ParameterizedTruncatedNormal'. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of 'shape'. If 'shape' argument is empty, then 'shape_tensor.flat()' is an empty array. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.", + "cve": "CVE-2021-29568", + "id": "pyup.io-56908", + "more_info_path": "/vulnerabilities/CVE-2021-29568/56908", + "specs": [ + ">=2.5.0rc0,<2.5.0", + ">=2.4.0rc0,<2.4.2", + ">=2.3.0rc0,<2.3.3", + ">=2.2.0rc0,<2.2.3", + ">=2.1.0rc0,<2.1.4" + ], + "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" + }, { "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29610: The validation in 'tf.raw_ops.QuantizeAndDequantizeV2' allows invalid values for 'axis' argument:. The validation (https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74-L77) uses '||' to mix two different conditions. If 'axis_ < -1' the condition in 'OP_REQUIRES' will still be true, but this value of 'axis_' results in heap underflow. This allows attackers to read/write to other data on the heap.", "cve": "CVE-2021-29610", @@ -64162,10 +64624,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'SVDF'. See CVE-2021-29598.", - "cve": "CVE-2021-29598", - "id": "pyup.io-56917", - "more_info_path": "/vulnerabilities/CVE-2021-29598/56917", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29608: TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in 'tf.raw_ops.RaggedTensorToTensor', an attacker can exploit an undefined behavior if input arguments are empty. The implementation (https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones. There are multiple 'DCHECK' validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything.", + "cve": "CVE-2021-29608", + "id": "pyup.io-56886", + "more_info_path": "/vulnerabilities/CVE-2021-29608/56886", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64176,10 +64638,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29571: The implementation of 'tf.raw_ops.MaxPoolGradWithArgmax' can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation (https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of 'boxes' input is 4, as required by the op (https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in 'boxes' is less than 4, accesses similar to 'tboxes(b, bb, 3)' will access data outside of bounds. Further during code execution there are also writes to these indices.", - "cve": "CVE-2021-29571", - "id": "pyup.io-56909", - "more_info_path": "/vulnerabilities/CVE-2021-29571/56909", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB read in 'tf.raw_ops.Dequantize'. See CVE-2021-29582.", + "cve": "CVE-2021-29582", + "id": "pyup.io-56877", + "more_info_path": "/vulnerabilities/CVE-2021-29582/56877", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64190,10 +64652,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix an undefined behavior and a 'CHECK'-fail in 'FractionalMaxPoolGrad'. See CVE-2021-29580.", - "cve": "CVE-2021-29580", - "id": "pyup.io-56922", - "more_info_path": "/vulnerabilities/CVE-2021-29580/56922", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by incomplete validation in 'SparseSparseMinimum'. See CVE-2021-29607.", + "cve": "CVE-2021-29607", + "id": "pyup.io-56882", + "more_info_path": "/vulnerabilities/CVE-2021-29607/56882", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64204,10 +64666,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29563: An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from the implementation of `tf.raw_ops.RFFT`. Eigen code operating on an empty matrix can trigger on an assertion and will cause program termination.", - "cve": "CVE-2021-29563", - "id": "pyup.io-56918", - "more_info_path": "/vulnerabilities/CVE-2021-29563/56918", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix fixes a heap OOB read in TFLite. See CVE-2021-29606.", + "cve": "CVE-2021-29606", + "id": "pyup.io-56878", + "more_info_path": "/vulnerabilities/CVE-2021-29606/56878", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64218,10 +64680,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'QuantizedMul'. See CVE-2021-29528.", - "cve": "CVE-2021-29528", - "id": "pyup.io-56919", - "more_info_path": "/vulnerabilities/CVE-2021-29528/56919", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'SVDF'. See CVE-2021-29598.", + "cve": "CVE-2021-29598", + "id": "pyup.io-56917", + "more_info_path": "/vulnerabilities/CVE-2021-29598/56917", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64232,10 +64694,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by incomplete validation in 'SparseReshape'. See CVE-2021-29611.", - "cve": "CVE-2021-29611", - "id": "pyup.io-56921", - "more_info_path": "/vulnerabilities/CVE-2021-29611/56921", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29587: TensorFlow is an end-to-end open source platform for machine learning. The `Prepare` step of the `SpaceToDepth` TFLite operator does not check for 0 before division (https://github.com/tensorflow/tensorflow/blob/5f7975d09eac0f10ed8a17dbb6f5964977725adc/tensorflow/lite/kernels/space_to_depth.cc#L63-L67). An attacker can craft a model such that `params->block_size` would be zero.", + "cve": "CVE-2021-29587", + "id": "pyup.io-56907", + "more_info_path": "/vulnerabilities/CVE-2021-29587/56907", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64246,10 +64708,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB in 'QuantizeAndDequantizeV3'. See CVE-2021-29553.", - "cve": "CVE-2021-29553", - "id": "pyup.io-56920", - "more_info_path": "/vulnerabilities/CVE-2021-29553/56920", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail due to integer overflow. See CVE-2021-29584.", + "cve": "CVE-2021-29584", + "id": "pyup.io-56874", + "more_info_path": "/vulnerabilities/CVE-2021-29584/56874", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64260,10 +64722,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow versions 2.5.0, 2.4.2, 2.3.3, 2.2.3 and 2.1.4 updates its dependency \"curl\" to a secure version (7.76.0).", - "cve": "CVE-2020-8285", - "id": "pyup.io-56912", - "more_info_path": "/vulnerabilities/CVE-2020-8285/56912", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29550: An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments (https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to `GeneratePoolingSequence`(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since `output_length` can be 0, this results in runtime crashing.", + "cve": "CVE-2021-29550", + "id": "pyup.io-56923", + "more_info_path": "/vulnerabilities/CVE-2021-29550/56923", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64274,10 +64736,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29583: The implementation of 'tf.raw_ops.FusedBatchNorm' is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that 'scale', 'offset', 'mean' and 'variance' (the last two only when required) all have the same number of elements as the number of channels of 'x'. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior.", - "cve": "CVE-2021-29583", - "id": "pyup.io-56885", - "more_info_path": "/vulnerabilities/CVE-2021-29583/56885", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix an undefined behavior and a 'CHECK'-fail in 'FractionalMaxPoolGrad'. See CVE-2021-29580.", + "cve": "CVE-2021-29580", + "id": "pyup.io-56922", + "more_info_path": "/vulnerabilities/CVE-2021-29580/56922", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64288,10 +64750,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'SpaceToBatchNd'. See CVE-2021-29597.", - "cve": "CVE-2021-29597", - "id": "pyup.io-56880", - "more_info_path": "/vulnerabilities/CVE-2021-29597/56880", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'MaxPool3DGradGrad'. See CVE-2021-29576.", + "cve": "CVE-2021-29576", + "id": "pyup.io-56900", + "more_info_path": "/vulnerabilities/CVE-2021-29576/56900", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64302,10 +64764,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail in 'tf.raw_ops.IRFFT'. See CVE-2021-29562.", - "cve": "CVE-2021-29562", - "id": "pyup.io-56879", - "more_info_path": "/vulnerabilities/CVE-2021-29562/56879", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap out of bounds read in 'RequantizationRange'. See CVE-2021-29569.", + "cve": "CVE-2021-29569", + "id": "pyup.io-56910", + "more_info_path": "/vulnerabilities/CVE-2021-29569/56910", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64316,10 +64778,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix fixes a heap OOB read in TFLite. See CVE-2021-29606.", - "cve": "CVE-2021-29606", - "id": "pyup.io-56878", - "more_info_path": "/vulnerabilities/CVE-2021-29606/56878", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29566: An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to 'tf.raw_ops.Dilation2DBackpropInput'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array. The values for 'h_out' and 'w_out' are guaranteed to be in range for 'out_backprop' (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating 'h_in_max'/'w_in_max' and 'in_backprop'.", + "cve": "CVE-2021-29566", + "id": "pyup.io-56906", + "more_info_path": "/vulnerabilities/CVE-2021-29566/56906", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64330,10 +64792,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'Split'. See CVE-2021-29599.", - "cve": "CVE-2021-29599", - "id": "pyup.io-56881", - "more_info_path": "/vulnerabilities/CVE-2021-29599/56881", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a null pointer dereference in 'SparseFillEmptyRows'. See CVE-2021-29565.", + "cve": "CVE-2021-29565", + "id": "pyup.io-56883", + "more_info_path": "/vulnerabilities/CVE-2021-29565/56883", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64344,10 +64806,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by incomplete validation in 'SparseSparseMinimum'. See CVE-2021-29607.", - "cve": "CVE-2021-29607", - "id": "pyup.io-56882", - "more_info_path": "/vulnerabilities/CVE-2021-29607/56882", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29563: An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from the implementation of `tf.raw_ops.RFFT`. Eigen code operating on an empty matrix can trigger on an assertion and will cause program termination.", + "cve": "CVE-2021-29563", + "id": "pyup.io-56918", + "more_info_path": "/vulnerabilities/CVE-2021-29563/56918", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64358,10 +64820,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'AvgPool3DGrad'. See CVE-2021-29577.", - "cve": "CVE-2021-29577", - "id": "pyup.io-56896", - "more_info_path": "/vulnerabilities/CVE-2021-29577/56896", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a null pointer dereference in 'EditDistance'. See CVE-2021-29564.", + "cve": "CVE-2021-29564", + "id": "pyup.io-56902", + "more_info_path": "/vulnerabilities/CVE-2021-29564/56902", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64372,10 +64834,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'DenseCountSparseOutput'. See CVE-2021-29554.", - "cve": "CVE-2021-29554", - "id": "pyup.io-56895", - "more_info_path": "/vulnerabilities/CVE-2021-29554/56895", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29547: An attacker can cause a segfault and denial of service via accessing data outside of bounds in 'tf.raw_ops.QuantizedBatchNormWithGlobalNormalization'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, '.flat()' is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds.", + "cve": "CVE-2021-29547", + "id": "pyup.io-56903", + "more_info_path": "/vulnerabilities/CVE-2021-29547/56903", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64386,10 +64848,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a integer overflow in TFLite memory allocation. See CVE-2021-29605.", - "cve": "CVE-2021-29605", - "id": "pyup.io-56899", - "more_info_path": "/vulnerabilities/CVE-2021-29605/56899", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29546: TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger an integer division by zero undefined behavior in `tf.raw_ops.QuantizedBiasAdd`. This is because the implementation of the Eigen kernel (https://github.com/tensorflow/tensorflow/blob/61bca8bd5ba8a68b2d97435ddfafcdf2b85672cd/tensorflow/core/kernels/quantization_utils.h#L812-L849) does a division by the number of elements of the smaller input (based on shape) without checking that this is not zero.", + "cve": "CVE-2021-29546", + "id": "pyup.io-56924", + "more_info_path": "/vulnerabilities/CVE-2021-29546/56924", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64400,10 +64862,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a null pointer dereference in 'EditDistance'. See CVE-2021-29564.", - "cve": "CVE-2021-29564", - "id": "pyup.io-56902", - "more_info_path": "/vulnerabilities/CVE-2021-29564/56902", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB in 'QuantizeAndDequantizeV3'. See CVE-2021-29553.", + "cve": "CVE-2021-29553", + "id": "pyup.io-56920", + "more_info_path": "/vulnerabilities/CVE-2021-29553/56920", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64414,10 +64876,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29587: TensorFlow is an end-to-end open source platform for machine learning. The `Prepare` step of the `SpaceToDepth` TFLite operator does not check for 0 before division (https://github.com/tensorflow/tensorflow/blob/5f7975d09eac0f10ed8a17dbb6f5964977725adc/tensorflow/lite/kernels/space_to_depth.cc#L63-L67). An attacker can craft a model such that `params->block_size` would be zero.", - "cve": "CVE-2021-29587", - "id": "pyup.io-56907", - "more_info_path": "/vulnerabilities/CVE-2021-29587/56907", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29583: The implementation of 'tf.raw_ops.FusedBatchNorm' is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that 'scale', 'offset', 'mean' and 'variance' (the last two only when required) all have the same number of elements as the number of channels of 'x'. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior.", + "cve": "CVE-2021-29583", + "id": "pyup.io-56885", + "more_info_path": "/vulnerabilities/CVE-2021-29583/56885", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64428,10 +64890,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB write in TFLite. See CVE-2021-29603.", - "cve": "CVE-2021-29603", - "id": "pyup.io-56904", - "more_info_path": "/vulnerabilities/CVE-2021-29603/56904", + "advisory": "Intel-tensorflow versions 2.5.0, 2.4.2, 2.3.3, 2.2.3 and 2.1.4 updates its dependency \"curl\" to a secure version (7.76.0).", + "cve": "CVE-2020-8285", + "id": "pyup.io-56912", + "more_info_path": "/vulnerabilities/CVE-2020-8285/56912", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64442,10 +64904,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap out of bounds read in 'RequantizationRange'. See CVE-2021-29569.", - "cve": "CVE-2021-29569", - "id": "pyup.io-56910", - "more_info_path": "/vulnerabilities/CVE-2021-29569/56910", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'AvgPool3DGrad'. See CVE-2021-29577.", + "cve": "CVE-2021-29577", + "id": "pyup.io-56896", + "more_info_path": "/vulnerabilities/CVE-2021-29577/56896", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64456,10 +64918,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.5.0, 2.4.2, 2.3.3, 2.2.3, and 2.1.4 include a fix for CVE-2021-29572: The implementation of `tf.raw_ops.SdcaOptimizer` triggers undefined behavior due to dereferencing a null pointer. The implementation (https://github.com/tensorflow/tensorflow/blob/60a45c8b6192a4699f2e2709a2645a751d435cc3/tensorflow/core/kernels/sdca_internal.cc) does not validate that the user supplied arguments satisfy all constraints expected by the op(https://www.tensorflow.org/api_docs/python/tf/raw_ops/SdcaOptimizer).", - "cve": "CVE-2021-29572", - "id": "pyup.io-56911", - "more_info_path": "/vulnerabilities/CVE-2021-29572/56911", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a integer overflow in TFLite memory allocation. See CVE-2021-29605.", + "cve": "CVE-2021-29605", + "id": "pyup.io-56899", + "more_info_path": "/vulnerabilities/CVE-2021-29605/56899", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64470,10 +64932,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in optimized pooling implementations in TFLite. See CVE-2021-29586.", - "cve": "CVE-2021-29586", - "id": "pyup.io-56915", - "more_info_path": "/vulnerabilities/CVE-2021-29586/56915", + "advisory": "Intel-tensorflow 2.5.0, 2.4.2, 2.3.3, 2.2.3, and 2.1.4 include a fix for CVE-2021-29572: The implementation of `tf.raw_ops.SdcaOptimizer` triggers undefined behavior due to dereferencing a null pointer. The implementation (https://github.com/tensorflow/tensorflow/blob/60a45c8b6192a4699f2e2709a2645a751d435cc3/tensorflow/core/kernels/sdca_internal.cc) does not validate that the user supplied arguments satisfy all constraints expected by the op(https://www.tensorflow.org/api_docs/python/tf/raw_ops/SdcaOptimizer).", + "cve": "CVE-2021-29572", + "id": "pyup.io-56911", + "more_info_path": "/vulnerabilities/CVE-2021-29572/56911", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64498,10 +64960,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a integer overflow in TFLite concatentation. See CVE-2021-29601.", - "cve": "CVE-2021-29601", - "id": "pyup.io-56913", - "more_info_path": "/vulnerabilities/CVE-2021-29601/56913", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in optimized pooling implementations in TFLite. See CVE-2021-29586.", + "cve": "CVE-2021-29586", + "id": "pyup.io-56915", + "more_info_path": "/vulnerabilities/CVE-2021-29586/56915", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64526,66 +64988,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29618: Passing a complex argument to `tf.transpose` at the same time as passing 'conjugate=True' argument results in a crash.", - "cve": "CVE-2021-29618", - "id": "pyup.io-56925", - "more_info_path": "/vulnerabilities/CVE-2021-29618/56925", - "specs": [ - ">=2.5.0rc0,<2.5.0", - ">=2.4.0rc0,<2.4.2", - ">=2.3.0rc0,<2.3.3", - ">=2.2.0rc0,<2.2.3", - ">=2.1.0rc0,<2.1.4" - ], - "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" - }, - { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29560: An attacker can cause a heap buffer overflow in `tf.raw_ops.RaggedTensorToTensor`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) uses the same index to access two arrays in parallel. Since the user controls the shape of the input arguments, an attacker could trigger a heap OOB access when 'parent_output_index' is shorter than 'row_split'.", - "cve": "CVE-2021-29560", - "id": "pyup.io-56926", - "more_info_path": "/vulnerabilities/CVE-2021-29560/56926", - "specs": [ - ">=2.5.0rc0,<2.5.0", - ">=2.4.0rc0,<2.4.2", - ">=2.3.0rc0,<2.3.3", - ">=2.2.0rc0,<2.2.3", - ">=2.1.0rc0,<2.1.4" - ], - "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" - }, - { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a stack overflow due to looping TFLite subgraph. See CVE-2021-29591.", - "cve": "CVE-2021-29591", - "id": "pyup.io-56875", - "more_info_path": "/vulnerabilities/CVE-2021-29591/56875", - "specs": [ - ">=2.5.0rc0,<2.5.0", - ">=2.4.0rc0,<2.4.2", - ">=2.3.0rc0,<2.3.3", - ">=2.2.0rc0,<2.2.3", - ">=2.1.0rc0,<2.1.4" - ], - "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" - }, - { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail due to integer overflow. See CVE-2021-29584.", - "cve": "CVE-2021-29584", - "id": "pyup.io-56874", - "more_info_path": "/vulnerabilities/CVE-2021-29584/56874", - "specs": [ - ">=2.5.0rc0,<2.5.0", - ">=2.4.0rc0,<2.4.2", - ">=2.3.0rc0,<2.3.3", - ">=2.2.0rc0,<2.2.3", - ">=2.1.0rc0,<2.1.4" - ], - "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" - }, - { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix an overflow and a denial of service in 'tf.raw_ops.ReverseSequence'. See CVE-2021-29575.", - "cve": "CVE-2021-29575", - "id": "pyup.io-56876", - "more_info_path": "/vulnerabilities/CVE-2021-29575/56876", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'SpaceToBatchNd'. See CVE-2021-29597.", + "cve": "CVE-2021-29597", + "id": "pyup.io-56880", + "more_info_path": "/vulnerabilities/CVE-2021-29597/56880", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64596,10 +65002,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB read in 'tf.raw_ops.Dequantize'. See CVE-2021-29582.", - "cve": "CVE-2021-29582", - "id": "pyup.io-56877", - "more_info_path": "/vulnerabilities/CVE-2021-29582/56877", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB write in TFLite. See CVE-2021-29603.", + "cve": "CVE-2021-29603", + "id": "pyup.io-56904", + "more_info_path": "/vulnerabilities/CVE-2021-29603/56904", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64610,10 +65016,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a segfault in 'tf.raw_ops.SparseCountSparseOutput'. See CVE-2021-29619.", - "cve": "CVE-2021-29619", - "id": "pyup.io-56888", - "more_info_path": "/vulnerabilities/CVE-2021-29619/56888", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29560: An attacker can cause a heap buffer overflow in `tf.raw_ops.RaggedTensorToTensor`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) uses the same index to access two arrays in parallel. Since the user controls the shape of the input arguments, an attacker could trigger a heap OOB access when 'parent_output_index' is shorter than 'row_split'.", + "cve": "CVE-2021-29560", + "id": "pyup.io-56926", + "more_info_path": "/vulnerabilities/CVE-2021-29560/56926", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64624,10 +65030,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'DepthwiseConv'. See CVE-2021-29602.", - "cve": "CVE-2021-29602", - "id": "pyup.io-56890", - "more_info_path": "/vulnerabilities/CVE-2021-29602/56890", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a stack overflow due to looping TFLite subgraph. See CVE-2021-29591.", + "cve": "CVE-2021-29591", + "id": "pyup.io-56875", + "more_info_path": "/vulnerabilities/CVE-2021-29591/56875", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64652,10 +65058,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29616: The implementation of TrySimplify (https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc#L390-L401) has undefined behavior due to dereferencing a null pointer in corner cases that result in optimizing a node with no inputs.", - "cve": "CVE-2021-29616", - "id": "pyup.io-56884", - "more_info_path": "/vulnerabilities/CVE-2021-29616/56884", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29618: Passing a complex argument to `tf.transpose` at the same time as passing 'conjugate=True' argument results in a crash.", + "cve": "CVE-2021-29618", + "id": "pyup.io-56925", + "more_info_path": "/vulnerabilities/CVE-2021-29618/56925", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64666,10 +65072,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'MaxPool3DGradGrad'. See CVE-2021-29576.", - "cve": "CVE-2021-29576", - "id": "pyup.io-56900", - "more_info_path": "/vulnerabilities/CVE-2021-29576/56900", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix an overflow and a denial of service in 'tf.raw_ops.ReverseSequence'. See CVE-2021-29575.", + "cve": "CVE-2021-29575", + "id": "pyup.io-56876", + "more_info_path": "/vulnerabilities/CVE-2021-29575/56876", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64694,24 +65100,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29608: TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in 'tf.raw_ops.RaggedTensorToTensor', an attacker can exploit an undefined behavior if input arguments are empty. The implementation (https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones. There are multiple 'DCHECK' validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything.", - "cve": "CVE-2021-29608", - "id": "pyup.io-56886", - "more_info_path": "/vulnerabilities/CVE-2021-29608/56886", - "specs": [ - ">=2.5.0rc0,<2.5.0", - ">=2.4.0rc0,<2.4.2", - ">=2.3.0rc0,<2.3.3", - ">=2.2.0rc0,<2.2.3", - ">=2.1.0rc0,<2.1.4" - ], - "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" - }, - { - "advisory": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in 'tf.raw_ops.ParameterizedTruncatedNormal'. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of 'shape'. If 'shape' argument is empty, then 'shape_tensor.flat()' is an empty array. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.", - "cve": "CVE-2021-29568", - "id": "pyup.io-56908", - "more_info_path": "/vulnerabilities/CVE-2021-29568/56908", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 update 'curl' to '7.76.0' to handle CVE-2020-8177.", + "cve": "CVE-2020-8177", + "id": "pyup.io-56892", + "more_info_path": "/vulnerabilities/CVE-2020-8177/56892", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64736,24 +65128,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow versions 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 update its dependency \"curl\" to handle CVE-2020-8284.", - "cve": "CVE-2020-8284", - "id": "pyup.io-56897", - "more_info_path": "/vulnerabilities/CVE-2020-8284/56897", - "specs": [ - ">=2.5.0rc0,<2.5.0", - ">=2.4.0rc0,<2.4.2", - ">=2.3.0rc0,<2.3.3", - ">=2.2.0rc0,<2.2.3", - ">=2.1.0rc0,<2.1.4" - ], - "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" - }, - { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29550: An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments (https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to `GeneratePoolingSequence`(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since `output_length` can be 0, this results in runtime crashing.", - "cve": "CVE-2021-29550", - "id": "pyup.io-56923", - "more_info_path": "/vulnerabilities/CVE-2021-29550/56923", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29571: The implementation of 'tf.raw_ops.MaxPoolGradWithArgmax' can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation (https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of 'boxes' input is 4, as required by the op (https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in 'boxes' is less than 4, accesses similar to 'tboxes(b, bb, 3)' will access data outside of bounds. Further during code execution there are also writes to these indices.", + "cve": "CVE-2021-29571", + "id": "pyup.io-56909", + "more_info_path": "/vulnerabilities/CVE-2021-29571/56909", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64764,10 +65142,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29546: TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger an integer division by zero undefined behavior in `tf.raw_ops.QuantizedBiasAdd`. This is because the implementation of the Eigen kernel (https://github.com/tensorflow/tensorflow/blob/61bca8bd5ba8a68b2d97435ddfafcdf2b85672cd/tensorflow/core/kernels/quantization_utils.h#L812-L849) does a division by the number of elements of the smaller input (based on shape) without checking that this is not zero.", - "cve": "CVE-2021-29546", - "id": "pyup.io-56924", - "more_info_path": "/vulnerabilities/CVE-2021-29546/56924", + "advisory": "Intel-tensorflow versions 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 update its dependency \"curl\" to handle CVE-2020-8284.", + "cve": "CVE-2020-8284", + "id": "pyup.io-56897", + "more_info_path": "/vulnerabilities/CVE-2020-8284/56897", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64778,10 +65156,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'FractionalAvgPoolGrad'. See CVE-2021-29578.", - "cve": "CVE-2021-29578", - "id": "pyup.io-56889", - "more_info_path": "/vulnerabilities/CVE-2021-29578/56889", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'OneHot'. See CVE-2021-29600.", + "cve": "CVE-2021-29600", + "id": "pyup.io-56901", + "more_info_path": "/vulnerabilities/CVE-2021-29600/56901", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64806,10 +65184,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'OneHot'. See CVE-2021-29600.", - "cve": "CVE-2021-29600", - "id": "pyup.io-56901", - "more_info_path": "/vulnerabilities/CVE-2021-29600/56901", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'QuantizedMul'. See CVE-2021-29528.", + "cve": "CVE-2021-29528", + "id": "pyup.io-56919", + "more_info_path": "/vulnerabilities/CVE-2021-29528/56919", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64820,10 +65198,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29566: An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to 'tf.raw_ops.Dilation2DBackpropInput'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array. The values for 'h_out' and 'w_out' are guaranteed to be in range for 'out_backprop' (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating 'h_in_max'/'w_in_max' and 'in_backprop'.", - "cve": "CVE-2021-29566", - "id": "pyup.io-56906", - "more_info_path": "/vulnerabilities/CVE-2021-29566/56906", + "advisory": "Intel-tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail in 'tf.raw_ops.IRFFT'. See CVE-2021-29562.", + "cve": "CVE-2021-29562", + "id": "pyup.io-56879", + "more_info_path": "/vulnerabilities/CVE-2021-29562/56879", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -64834,10 +65212,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37691: In affected versions an attacker can craft a TFLite model that would trigger a division by zero error in LSH [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/lsh_projection.cc#L118). The Tensorflow team has patched the issue in GitHub commit 0575b640091680cfb70f4dd93e70658de43b94f9.", - "cve": "CVE-2021-37691", - "id": "pyup.io-56873", - "more_info_path": "/vulnerabilities/CVE-2021-37691/56873", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37645: In affected versions the implementation of `tf.raw_ops.QuantizeAndDequantizeV4Grad` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L126) uses the `axis` value as the size argument to `absl::InlinedVector` constructor. But, the constructor uses an unsigned type for the argument, so the implicit conversion transforms the negative value to a large integer. The Tensorflow team has patched the issue in GitHub commit 96f364a1ca3009f98980021c4b32be5fdcca33a1.", + "cve": "CVE-2021-37645", + "id": "pyup.io-56872", + "more_info_path": "/vulnerabilities/CVE-2021-37645/56872", "specs": [ ">=2.5.0rc0,<2.5.1", ">=2.4.0rc0,<2.4.3", @@ -64847,10 +65225,10 @@ "v": ">=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37688: In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer. The Tensorflow team has patched the issue in GitHub commit 15691e456c7dc9bd6be203b09765b063bf4a380c.", - "cve": "CVE-2021-37688", - "id": "pyup.io-56871", - "more_info_path": "/vulnerabilities/CVE-2021-37688/56871", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37691: In affected versions an attacker can craft a TFLite model that would trigger a division by zero error in LSH [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/lsh_projection.cc#L118). The Tensorflow team has patched the issue in GitHub commit 0575b640091680cfb70f4dd93e70658de43b94f9.", + "cve": "CVE-2021-37691", + "id": "pyup.io-56873", + "more_info_path": "/vulnerabilities/CVE-2021-37691/56873", "specs": [ ">=2.5.0rc0,<2.5.1", ">=2.4.0rc0,<2.4.3", @@ -64860,10 +65238,10 @@ "v": ">=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37645: In affected versions the implementation of `tf.raw_ops.QuantizeAndDequantizeV4Grad` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L126) uses the `axis` value as the size argument to `absl::InlinedVector` constructor. But, the constructor uses an unsigned type for the argument, so the implicit conversion transforms the negative value to a large integer. The Tensorflow team has patched the issue in GitHub commit 96f364a1ca3009f98980021c4b32be5fdcca33a1.", - "cve": "CVE-2021-37645", - "id": "pyup.io-56872", - "more_info_path": "/vulnerabilities/CVE-2021-37645/56872", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37688: In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer. The Tensorflow team has patched the issue in GitHub commit 15691e456c7dc9bd6be203b09765b063bf4a380c.", + "cve": "CVE-2021-37688", + "id": "pyup.io-56871", + "more_info_path": "/vulnerabilities/CVE-2021-37688/56871", "specs": [ ">=2.5.0rc0,<2.5.1", ">=2.4.0rc0,<2.4.3", @@ -64907,6 +65285,19 @@ ], "v": ">=2.6.0a1,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, + { + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37685: In affected versions TFLite's 'expand_dims.cc' (https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/expand_dims.cc#L36-L50) contains a vulnerability which allows reading one element outside of bounds of heap allocated data. If 'axis' is a large negative value (e.g., '-100000'), then after the first 'if' it would still be negative. The check following the 'if' statement will pass and the 'for' loop would read one element before the start of 'input_dims.data' (when 'i = 0'). The Tensorflow team has patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257.", + "cve": "CVE-2021-37685", + "id": "pyup.io-56860", + "more_info_path": "/vulnerabilities/CVE-2021-37685/56860", + "specs": [ + ">=2.6.0rc0,<2.6.0", + ">=2.3.0rc0,<2.3.4", + ">=2.4.0rc0,<2.4.3", + ">=2.5.0rc0,<2.5.1" + ], + "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" + }, { "advisory": "Intel-tensorflow version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37668:\nIn affected versions, an attacker can cause denial of service in applications serving models using \"tf.raw_ops.UnravelIndex\" by triggering a division by 0. The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/unravel_index_op.cc#L36) does not check that the tensor subsumed by \"dims\" is not empty. Hence, if one element of \"dims\" is 0, the implementation does a division by 0. The Tensorflow team has patched the issue in GitHub commit a776040a5e7ebf76eeb7eb923bf1ae417dd4d233.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-2wmv-37vq-52g5\nhttps://github.com/tensorflow/tensorflow/commit/a776040a5e7ebf76eeb7eb923bf1ae417dd4d233", "cve": "CVE-2021-37668", @@ -64946,19 +65337,6 @@ ], "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, - { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37653: In affected versions an attacker can trigger a crash via a floating point exception in 'tf.raw_ops.ResourceGather'. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L725-L731) computes the value of a value, 'batch_size', and then divides by it without checking that this value is not 0. The Tensorflow team has patched the issue in GitHub commit ac117ee8a8ea57b73d34665cdf00ef3303bc0b11.", - "cve": "CVE-2021-37653", - "id": "pyup.io-56866", - "more_info_path": "/vulnerabilities/CVE-2021-37653/56866", - "specs": [ - ">=2.6.0rc0,<2.6.0", - ">=2.3.0rc0,<2.3.4", - ">=2.4.0rc0,<2.4.3", - ">=2.5.0rc0,<2.5.1" - ], - "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" - }, { "advisory": "Intel-tensorflow version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37663:\nIn affected versions, due to incomplete validation in \"tf.raw_ops.QuantizeV2\", an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that \"min_range\" and \"max_range\" both have the same non-zero number of elements. If \"axis\" is provided (i.e., not \"-1\"), then validation should check that it is a value in range for the rank of \"input\" tensor and then the lengths of \"min_range\" and \"max_range\" inputs match the \"axis\" dimension of the \"input\" tensor. The Tensorflow team has patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g25h-jr74-qp5j\nhttps://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708", "cve": "CVE-2021-37663", @@ -64985,19 +65363,6 @@ ], "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, - { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37676: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in 'tf.raw_ops.SparseFillEmptyRows'. The shape inference implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/sparse_ops.cc#L608-L634) does not validate that the input arguments are not empty tensors. The Tensorflow team has patched the issue in GitHub commit 578e634b4f1c1c684d4b4294f9e5281b2133b3ed.", - "cve": "CVE-2021-37676", - "id": "pyup.io-56865", - "more_info_path": "/vulnerabilities/CVE-2021-37676/56865", - "specs": [ - ">=2.6.0rc0,<2.6.0", - ">=2.3.0rc0,<2.3.4", - ">=2.4.0rc0,<2.4.3", - ">=2.5.0rc0,<2.5.1" - ], - "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" - }, { "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37657: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all operations of type 'tf.raw_ops.MatrixDiagV*'. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/linalg/matrix_diag_op.cc) has incomplete validation that the value of 'k' is a valid tensor. The Tensorflow team has checked that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong. The Tensorflow team has patched the issue in GitHub commit f2a673bd34f0d64b8e40a551ac78989d16daad09.", "cve": "CVE-2021-37657", @@ -65012,10 +65377,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37685: In affected versions TFLite's 'expand_dims.cc' (https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/expand_dims.cc#L36-L50) contains a vulnerability which allows reading one element outside of bounds of heap allocated data. If 'axis' is a large negative value (e.g., '-100000'), then after the first 'if' it would still be negative. The check following the 'if' statement will pass and the 'for' loop would read one element before the start of 'input_dims.data' (when 'i = 0'). The Tensorflow team has patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257.", - "cve": "CVE-2021-37685", - "id": "pyup.io-56860", - "more_info_path": "/vulnerabilities/CVE-2021-37685/56860", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37661: In affected versions an attacker can cause a denial of service in 'boosted_trees_create_quantile_stream_resource' by using negative arguments. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantile_ops.cc#L96) does not validate that 'num_streams' only contains non-negative numbers. In turn, this results in using this value to allocate memory (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantiles/quantile_stream_resource.h#L31-L40). However, 'reserve' receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library. The Tensorflow team has patched the issue in GitHub commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992.", + "cve": "CVE-2021-37661", + "id": "pyup.io-56861", + "more_info_path": "/vulnerabilities/CVE-2021-37661/56861", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -65038,10 +65403,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37661: In affected versions an attacker can cause a denial of service in 'boosted_trees_create_quantile_stream_resource' by using negative arguments. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantile_ops.cc#L96) does not validate that 'num_streams' only contains non-negative numbers. In turn, this results in using this value to allocate memory (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantiles/quantile_stream_resource.h#L31-L40). However, 'reserve' receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library. The Tensorflow team has patched the issue in GitHub commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992.", - "cve": "CVE-2021-37661", - "id": "pyup.io-56861", - "more_info_path": "/vulnerabilities/CVE-2021-37661/56861", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37676: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in 'tf.raw_ops.SparseFillEmptyRows'. The shape inference implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/sparse_ops.cc#L608-L634) does not validate that the input arguments are not empty tensors. The Tensorflow team has patched the issue in GitHub commit 578e634b4f1c1c684d4b4294f9e5281b2133b3ed.", + "cve": "CVE-2021-37676", + "id": "pyup.io-56865", + "more_info_path": "/vulnerabilities/CVE-2021-37676/56865", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -65051,17 +65416,17 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37662: In affected versions an attacker can generate undefined behavior via a reference binding to nullptr in 'BoostedTreesCalculateBestGainsPerFeature' and similar attack can occur in 'BoostedTreesCalculateBestFeatureSplitV2'. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) does not validate the input values. The Tensorflow team has patched the issue in GitHub commit 9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad and in commit 429f009d2b2c09028647dd4bb7b3f6f414bbaad7.", - "cve": "CVE-2021-37662", - "id": "pyup.io-56850", - "more_info_path": "/vulnerabilities/CVE-2021-37662/56850", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37653: In affected versions an attacker can trigger a crash via a floating point exception in 'tf.raw_ops.ResourceGather'. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L725-L731) computes the value of a value, 'batch_size', and then divides by it without checking that this value is not 0. The Tensorflow team has patched the issue in GitHub commit ac117ee8a8ea57b73d34665cdf00ef3303bc0b11.", + "cve": "CVE-2021-37653", + "id": "pyup.io-56866", + "more_info_path": "/vulnerabilities/CVE-2021-37653/56866", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", - ">=2.5.0rc0,<2.5.1", - ">=2.4.0rc0,<2.4.3" + ">=2.4.0rc0,<2.4.3", + ">=2.5.0rc0,<2.5.1" ], - "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3" + "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, { "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37641: In affected versions if the arguments to 'tf.raw_ops.RaggedGather' don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by 'params_nested_splits' is not an empty list of tensors. The Tensorflow team has patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373.", @@ -65090,10 +65455,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37643: If a user does not provide a valid padding value to 'tf.raw_ops.MatrixDiagPartOp', then the code triggers a null pointer dereference (if input is empty) or produces invalid behavior, ignoring all values after the first. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L89) reads the first value from a tensor buffer without first checking that the tensor has values to read from. The Tensorflow team has patched the issue in GitHub commit 482da92095c4d48f8784b1f00dda4f81c28d2988.", - "cve": "CVE-2021-37643", - "id": "pyup.io-56851", - "more_info_path": "/vulnerabilities/CVE-2021-37643/56851", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37644: In affected versions providing a negative element to 'num_elements' list argument of 'tf.raw_ops.TensorListReserve' causes the runtime to abort the process due to reallocating a 'std::vector' to have a negative number of elements. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/list_kernels.cc#L312) calls 'std::vector.resize()' with the new size controlled by input given by the user, without checking that this input is valid. The Tensorflow team has patched the issue in GitHub commit 8a6e874437670045e6c7dc6154c7412b4a2135e2.", + "cve": "CVE-2021-37644", + "id": "pyup.io-56854", + "more_info_path": "/vulnerabilities/CVE-2021-37644/56854", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -65116,10 +65481,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37646: In affected versions the implementation of 'tf.raw_ops.StringNGrams' is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/string_ngrams_op.cc#L184) calls 'reserve' on a 'tstring' with a value that sometimes can be negative if user supplies negative 'ngram_widths'. The 'reserve' method calls 'TF_TString_Reserve' which has an 'unsigned long' argument for the size of the buffer. Hence, the implicit conversion transforms the negative value to a large integer. The Tensorflow team has patched the issue in GitHub commit c283e542a3f422420cfdb332414543b62fc4e4a5.", - "cve": "CVE-2021-37646", - "id": "pyup.io-56855", - "more_info_path": "/vulnerabilities/CVE-2021-37646/56855", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37662: In affected versions an attacker can generate undefined behavior via a reference binding to nullptr in 'BoostedTreesCalculateBestGainsPerFeature' and similar attack can occur in 'BoostedTreesCalculateBestFeatureSplitV2'. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) does not validate the input values. The Tensorflow team has patched the issue in GitHub commit 9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad and in commit 429f009d2b2c09028647dd4bb7b3f6f414bbaad7.", + "cve": "CVE-2021-37662", + "id": "pyup.io-56850", + "more_info_path": "/vulnerabilities/CVE-2021-37662/56850", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -65142,10 +65507,23 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37644: In affected versions providing a negative element to 'num_elements' list argument of 'tf.raw_ops.TensorListReserve' causes the runtime to abort the process due to reallocating a 'std::vector' to have a negative number of elements. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/list_kernels.cc#L312) calls 'std::vector.resize()' with the new size controlled by input given by the user, without checking that this input is valid. The Tensorflow team has patched the issue in GitHub commit 8a6e874437670045e6c7dc6154c7412b4a2135e2.", - "cve": "CVE-2021-37644", - "id": "pyup.io-56854", - "more_info_path": "/vulnerabilities/CVE-2021-37644/56854", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37646: In affected versions the implementation of 'tf.raw_ops.StringNGrams' is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/string_ngrams_op.cc#L184) calls 'reserve' on a 'tstring' with a value that sometimes can be negative if user supplies negative 'ngram_widths'. The 'reserve' method calls 'TF_TString_Reserve' which has an 'unsigned long' argument for the size of the buffer. Hence, the implicit conversion transforms the negative value to a large integer. The Tensorflow team has patched the issue in GitHub commit c283e542a3f422420cfdb332414543b62fc4e4a5.", + "cve": "CVE-2021-37646", + "id": "pyup.io-56855", + "more_info_path": "/vulnerabilities/CVE-2021-37646/56855", + "specs": [ + ">=2.6.0rc0,<2.6.0", + ">=2.3.0rc0,<2.3.4", + ">=2.5.0rc0,<2.5.1", + ">=2.4.0rc0,<2.4.3" + ], + "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3" + }, + { + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37643: If a user does not provide a valid padding value to 'tf.raw_ops.MatrixDiagPartOp', then the code triggers a null pointer dereference (if input is empty) or produces invalid behavior, ignoring all values after the first. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L89) reads the first value from a tensor buffer without first checking that the tensor has values to read from. The Tensorflow team has patched the issue in GitHub commit 482da92095c4d48f8784b1f00dda4f81c28d2988.", + "cve": "CVE-2021-37643", + "id": "pyup.io-56851", + "more_info_path": "/vulnerabilities/CVE-2021-37643/56851", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -65180,6 +65558,19 @@ ], "v": ">=2.6.0rc0,<2.6.0,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1" }, + { + "advisory": "Intel-tensorflow version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37670:\nIn affected versions, an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to \"tf.raw_ops.UpperBound\". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/searchsorted_op.cc#L85-L104) does not validate the rank of \"sorted_input\" argument. A similar issue occurs in \"tf.raw_ops.LowerBound\". The Tensorflow team has patched the issue in GitHub commit 42459e4273c2e47a3232cc16c4f4fff3b3a35c38.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9697-98pf-4rw7\nhttps://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38", + "cve": "CVE-2021-37670", + "id": "pyup.io-56836", + "more_info_path": "/vulnerabilities/CVE-2021-37670/56836", + "specs": [ + ">=2.6.0rc0,<2.6.0", + ">=2.5.0rc0,<2.5.1", + ">=2.4.0rc0,<2.4.3", + ">=2.3.0rc0,<2.3.4" + ], + "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" + }, { "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1, and 2.6.0 updates its dependency 'curl' to v7.77.0 to include security fixes.", "cve": "CVE-2021-22901", @@ -65194,10 +65585,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for 'tf.raw_ops.Dequantize' has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses 'axis' to select between two different values for 'minmax_rank' which is then used to retrieve tensor dimensions. However, code assumes that 'axis' can be either '-1' or a value greater than '-1', with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.", - "cve": "CVE-2021-37677", - "id": "pyup.io-56845", - "more_info_path": "/vulnerabilities/CVE-2021-37677/56845", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37655: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to 'tf.raw_ops.ResourceScatterUpdate'. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L919-L923) has an incomplete validation of the relationship between the shapes of 'indices' and 'updates': instead of checking that the shape of 'indices' is a prefix of the shape of 'updates' (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship. The Tensorflow team has patched the issue in GitHub commit 01cff3f986259d661103412a20745928c727326f.", + "cve": "CVE-2021-37655", + "id": "pyup.io-56834", + "more_info_path": "/vulnerabilities/CVE-2021-37655/56834", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -65233,23 +65624,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37655: TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to 'tf.raw_ops.ResourceScatterUpdate'. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L919-L923) has an incomplete validation of the relationship between the shapes of 'indices' and 'updates': instead of checking that the shape of 'indices' is a prefix of the shape of 'updates' (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship. The Tensorflow team has patched the issue in GitHub commit 01cff3f986259d661103412a20745928c727326f.", - "cve": "CVE-2021-37655", - "id": "pyup.io-56834", - "more_info_path": "/vulnerabilities/CVE-2021-37655/56834", - "specs": [ - ">=2.6.0rc0,<2.6.0", - ">=2.5.0rc0,<2.5.1", - ">=2.4.0rc0,<2.4.3", - ">=2.3.0rc0,<2.3.4" - ], - "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" - }, - { - "advisory": "Intel-tensorflow version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37670:\nIn affected versions, an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to \"tf.raw_ops.UpperBound\". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/searchsorted_op.cc#L85-L104) does not validate the rank of \"sorted_input\" argument. A similar issue occurs in \"tf.raw_ops.LowerBound\". The Tensorflow team has patched the issue in GitHub commit 42459e4273c2e47a3232cc16c4f4fff3b3a35c38.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9697-98pf-4rw7\nhttps://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38", - "cve": "CVE-2021-37670", - "id": "pyup.io-56836", - "more_info_path": "/vulnerabilities/CVE-2021-37670/56836", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37650: In affected versions the implementation for `tf.raw_ops.ExperimentalDatasetToTFRecord` and `tf.raw_ops.DatasetToTFRecord` can trigger heap buffer overflow and segmentation fault. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc#L93-L102) assumes that all records in the dataset are of string type. However, there is no check for that, and the example given above uses numeric types. The Tensorflow team has patched the issue in GitHub commit e0b6e58c328059829c3eb968136f17aa72b6c876.", + "cve": "CVE-2021-37650", + "id": "pyup.io-56842", + "more_info_path": "/vulnerabilities/CVE-2021-37650/56842", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -65259,10 +65637,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a denial of service via a segmentation fault in 'tf.raw_ops.MaxPoolGrad' caused by missing validation. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/maxpooling_op.cc) misses some validation for the 'orig_input' and 'orig_output' tensors. The fixes for CVE-2021-29579 were incomplete. We have patched the issue in GitHub commit 136b51f10903e044308cf77117c0ed9871350475. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.", - "cve": "CVE-2021-37674", - "id": "pyup.io-56843", - "more_info_path": "/vulnerabilities/CVE-2021-37674/56843", + "advisory": "Intel-tensorflow version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37669:\nIn affected versions, an attacker can cause denial of service in applications serving models using \"tf.raw_ops.NonMaxSuppressionV5\" by triggering a division by 0. The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a \"std::vector\". However, as \"std::vector::resize\" takes the size argument as a \"size_t\" and \"output_size\" is an \"int\", there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in \"CombinedNonMaxSuppression\". The Tensorflow team has patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vmjw-c2vp-p33c\nhttps://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d\nhttps://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58", + "cve": "CVE-2021-37669", + "id": "pyup.io-56844", + "more_info_path": "/vulnerabilities/CVE-2021-37669/56844", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -65272,10 +65650,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37650: In affected versions the implementation for `tf.raw_ops.ExperimentalDatasetToTFRecord` and `tf.raw_ops.DatasetToTFRecord` can trigger heap buffer overflow and segmentation fault. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc#L93-L102) assumes that all records in the dataset are of string type. However, there is no check for that, and the example given above uses numeric types. The Tensorflow team has patched the issue in GitHub commit e0b6e58c328059829c3eb968136f17aa72b6c876.", - "cve": "CVE-2021-37650", - "id": "pyup.io-56842", - "more_info_path": "/vulnerabilities/CVE-2021-37650/56842", + "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1, and 2.6.0 updates its dependency 'curl' to v7.77.0 to include security fixes.", + "cve": "CVE-2021-22876", + "id": "pyup.io-56840", + "more_info_path": "/vulnerabilities/CVE-2021-22876/56840", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -65286,9 +65664,9 @@ }, { "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1, and 2.6.0 updates its dependency 'curl' to v7.77.0 to include security fixes.", - "cve": "CVE-2021-22876", - "id": "pyup.io-56840", - "more_info_path": "/vulnerabilities/CVE-2021-22876/56840", + "cve": "CVE-2021-22897", + "id": "pyup.io-56839", + "more_info_path": "/vulnerabilities/CVE-2021-22897/56839", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -65298,10 +65676,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "Intel-tensorflow version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37679:\nIn affected versions it is possible to nest a \"tf.map_fn\" within another \"tf.map_fn\" call. However, if the input tensor is a \"RaggedTensor\" and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The \"t\" and \"z\" outputs should be identical, however this is not the case. The last row of \"t\" contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a \"Variant\" tensor to a \"RaggedTensor\". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. The Tensorflow team has patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g8wg-cjwc-xhhp\nhttps://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12", - "cve": "CVE-2021-37679", - "id": "pyup.io-56837", - "more_info_path": "/vulnerabilities/CVE-2021-37679/56837", + "advisory": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for 'tf.raw_ops.Dequantize' has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses 'axis' to select between two different values for 'minmax_rank' which is then used to retrieve tensor dimensions. However, code assumes that 'axis' can be either '-1' or a value greater than '-1', with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.", + "cve": "CVE-2021-37677", + "id": "pyup.io-56845", + "more_info_path": "/vulnerabilities/CVE-2021-37677/56845", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -65311,10 +65689,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "Intel-tensorflow version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37669:\nIn affected versions, an attacker can cause denial of service in applications serving models using \"tf.raw_ops.NonMaxSuppressionV5\" by triggering a division by 0. The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a \"std::vector\". However, as \"std::vector::resize\" takes the size argument as a \"size_t\" and \"output_size\" is an \"int\", there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in \"CombinedNonMaxSuppression\". The Tensorflow team has patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vmjw-c2vp-p33c\nhttps://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d\nhttps://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58", - "cve": "CVE-2021-37669", - "id": "pyup.io-56844", - "more_info_path": "/vulnerabilities/CVE-2021-37669/56844", + "advisory": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a denial of service via a segmentation fault in 'tf.raw_ops.MaxPoolGrad' caused by missing validation. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/maxpooling_op.cc) misses some validation for the 'orig_input' and 'orig_output' tensors. The fixes for CVE-2021-29579 were incomplete. We have patched the issue in GitHub commit 136b51f10903e044308cf77117c0ed9871350475. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.", + "cve": "CVE-2021-37674", + "id": "pyup.io-56843", + "more_info_path": "/vulnerabilities/CVE-2021-37674/56843", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -65324,10 +65702,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "Intel-tensorflow 2.3.4, 2.4.3, 2.5.1, and 2.6.0 updates its dependency 'curl' to v7.77.0 to include security fixes.", - "cve": "CVE-2021-22897", - "id": "pyup.io-56839", - "more_info_path": "/vulnerabilities/CVE-2021-22897/56839", + "advisory": "Intel-tensorflow version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37679:\nIn affected versions it is possible to nest a \"tf.map_fn\" within another \"tf.map_fn\" call. However, if the input tensor is a \"RaggedTensor\" and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The \"t\" and \"z\" outputs should be identical, however this is not the case. The last row of \"t\" contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a \"Variant\" tensor to a \"RaggedTensor\". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. The Tensorflow team has patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g8wg-cjwc-xhhp\nhttps://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12", + "cve": "CVE-2021-37679", + "id": "pyup.io-56837", + "more_info_path": "/vulnerabilities/CVE-2021-37679/56837", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -65351,20 +65729,20 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4,>=2.7.0rc0,<2.7.0" }, { - "advisory": "Intel-tensorflow version 2.6.1 includes a fix for CVE-2021-41220: In affected versions, the async implementation of 'CollectiveReduceV2' suffers from a memory leak and a use after free. This occurs due to the asynchronous computation and the fact that objects that have been 'std::move()'d are still accessed. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gpfh-jvf9-7wg5\nhttps://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75", - "cve": "CVE-2021-41220", - "id": "pyup.io-56832", - "more_info_path": "/vulnerabilities/CVE-2021-41220/56832", + "advisory": "Intel-tensorflow version 2.6.1 includes a fix for CVE-2021-41211: In affected versions, the shape inference code for 'QuantizeV2' can trigger a read outside of bounds of heap allocated array. This occurs whenever 'axis' is a negative value less than '-1'. In this case, we are accessing data before the start of a heap buffer. The code allows 'axis' to be an optional argument ('s' would contain an 'error::NOT_FOUND' error code). Otherwise, it assumes that 'axis' is a valid index into the dimensions of the 'input' tensor. If 'axis' is less than '-1' then this results in a heap OOB read. The fix is included in TensorFlow 2.7.0. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvgx-3v3q-m36c\nhttps://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244", + "cve": "CVE-2021-41211", + "id": "pyup.io-56833", + "more_info_path": "/vulnerabilities/CVE-2021-41211/56833", "specs": [ ">=2.6.0rc0,<2.6.1" ], "v": ">=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow version 2.6.1 includes a fix for CVE-2021-41211: In affected versions, the shape inference code for 'QuantizeV2' can trigger a read outside of bounds of heap allocated array. This occurs whenever 'axis' is a negative value less than '-1'. In this case, we are accessing data before the start of a heap buffer. The code allows 'axis' to be an optional argument ('s' would contain an 'error::NOT_FOUND' error code). Otherwise, it assumes that 'axis' is a valid index into the dimensions of the 'input' tensor. If 'axis' is less than '-1' then this results in a heap OOB read. The fix is included in TensorFlow 2.7.0. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvgx-3v3q-m36c\nhttps://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244", - "cve": "CVE-2021-41211", - "id": "pyup.io-56833", - "more_info_path": "/vulnerabilities/CVE-2021-41211/56833", + "advisory": "Intel-tensorflow version 2.6.1 includes a fix for CVE-2021-41220: In affected versions, the async implementation of 'CollectiveReduceV2' suffers from a memory leak and a use after free. This occurs due to the asynchronous computation and the fact that objects that have been 'std::move()'d are still accessed. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gpfh-jvf9-7wg5\nhttps://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75", + "cve": "CVE-2021-41220", + "id": "pyup.io-56832", + "more_info_path": "/vulnerabilities/CVE-2021-41220/56832", "specs": [ ">=2.6.0rc0,<2.6.1" ], @@ -65426,10 +65804,10 @@ ], "intel-tensorflow-avx512": [ { - "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15195: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of 'SparseFillEmptyRowsGrad' uses a double indexing pattern. It is possible for 'reverse_index_map(i)' to be an index outside of bounds of 'grad_values', thus resulting in a heap buffer overflow.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-63xm-rx5p-xvqr", - "cve": "CVE-2020-15195", - "id": "pyup.io-57503", - "more_info_path": "/vulnerabilities/CVE-2020-15195/57503", + "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15208: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a \"DCHECK\" which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue was patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d", + "cve": "CVE-2020-15208", + "id": "pyup.io-57506", + "more_info_path": "/vulnerabilities/CVE-2020-15208/57506", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -65440,10 +65818,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15205: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the 'data_splits' argument of 'tf.raw_ops.StringNGrams' lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after 'ee ff' are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g7p5-5759-qv46", - "cve": "CVE-2020-15205", - "id": "pyup.io-57510", - "more_info_path": "/vulnerabilities/CVE-2020-15205/57510", + "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15202: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the 'Shard' API in TensorFlow expects the last argument to be a function taking two 'int64' (i.e., 'long long') arguments. However, there are several places in TensorFlow where a lambda taking 'int' or 'int32' arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6fg-mjxg-hqq4", + "cve": "CVE-2020-15202", + "id": "pyup.io-57508", + "more_info_path": "/vulnerabilities/CVE-2020-15202/57508", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -65454,10 +65832,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15208: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a \"DCHECK\" which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue was patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d", - "cve": "CVE-2020-15208", - "id": "pyup.io-57506", - "more_info_path": "/vulnerabilities/CVE-2020-15208/57506", + "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15206: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's \"SavedModel\" protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using \"tensorflow-serving\" or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d. However, this was not enough, as #41097 reported a different failure mode. The issue was finally patched in commit df095206f25471e864a8e63a0f1caef53a0e3a6", + "cve": "CVE-2020-15206", + "id": "pyup.io-57504", + "more_info_path": "/vulnerabilities/CVE-2020-15206/57504", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -65468,10 +65846,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15204: In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling \"tf.raw_ops.GetSessionHandle\" or \"tf.raw_ops.GetSessionHandleV2\" results in a null pointer dereference In linked snippet, in eager mode, \"ctx->session_state()\" returns \"nullptr\". Since code immediately dereferences this, we get a segmentation fault. The issue was patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1", - "cve": "CVE-2020-15204", - "id": "pyup.io-57502", - "more_info_path": "/vulnerabilities/CVE-2020-15204/57502", + "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15211: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative \"-1\" value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the \"-1\" index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue was patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83). A potential workaround would be to add a custom \"Verifier\" to the model loading code to ensure that only operators which accept optional inputs use the \"-1\" special value and only for the tensors that they expect to be optional. Since this allow-list type approach is error-prone, it's advised upgrading to the patched code.", + "cve": "CVE-2020-15211", + "id": "pyup.io-57505", + "more_info_path": "/vulnerabilities/CVE-2020-15211/57505", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -65496,10 +65874,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15190: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the \"tf.raw_ops.Switch\" operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. However, the eager runtime traverses all tensors in the output. Since only one of the tensors is defined, the other one is \"nullptr\", hence we are binding a reference to \"nullptr\". This is undefined behavior and reported as an error if compiling with \"-fsanitize=null\". In this case, this results in a segmentation fault The issue was patched in commit da8558533d925694483d2c136a9220d6d49d843c\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4g9f-63rx-5cw4", - "cve": "CVE-2020-15190", - "id": "pyup.io-57507", - "more_info_path": "/vulnerabilities/CVE-2020-15190/57507", + "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15207: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses 'ResolveAxis' to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the 'DCHECK' does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-q4qf-3fc6-8x34", + "cve": "CVE-2020-15207", + "id": "pyup.io-57512", + "more_info_path": "/vulnerabilities/CVE-2020-15207/57512", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -65510,10 +65888,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15206: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's \"SavedModel\" protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using \"tensorflow-serving\" or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d. However, this was not enough, as #41097 reported a different failure mode. The issue was finally patched in commit df095206f25471e864a8e63a0f1caef53a0e3a6", - "cve": "CVE-2020-15206", - "id": "pyup.io-57504", - "more_info_path": "/vulnerabilities/CVE-2020-15206/57504", + "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15205: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the 'data_splits' argument of 'tf.raw_ops.StringNGrams' lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after 'ee ff' are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g7p5-5759-qv46", + "cve": "CVE-2020-15205", + "id": "pyup.io-57510", + "more_info_path": "/vulnerabilities/CVE-2020-15205/57510", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -65524,10 +65902,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15202: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the 'Shard' API in TensorFlow expects the last argument to be a function taking two 'int64' (i.e., 'long long') arguments. However, there are several places in TensorFlow where a lambda taking 'int' or 'int32' arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6fg-mjxg-hqq4", - "cve": "CVE-2020-15202", - "id": "pyup.io-57508", - "more_info_path": "/vulnerabilities/CVE-2020-15202/57508", + "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15204: In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling \"tf.raw_ops.GetSessionHandle\" or \"tf.raw_ops.GetSessionHandleV2\" results in a null pointer dereference In linked snippet, in eager mode, \"ctx->session_state()\" returns \"nullptr\". Since code immediately dereferences this, we get a segmentation fault. The issue was patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1", + "cve": "CVE-2020-15204", + "id": "pyup.io-57502", + "more_info_path": "/vulnerabilities/CVE-2020-15204/57502", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -65538,10 +65916,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15203: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the 'fill' argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a 'printf' call is constructed. This may result in segmentation fault.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79", - "cve": "CVE-2020-15203", - "id": "pyup.io-57511", - "more_info_path": "/vulnerabilities/CVE-2020-15203/57511", + "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15195: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of 'SparseFillEmptyRowsGrad' uses a double indexing pattern. It is possible for 'reverse_index_map(i)' to be an index outside of bounds of 'grad_values', thus resulting in a heap buffer overflow.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-63xm-rx5p-xvqr", + "cve": "CVE-2020-15195", + "id": "pyup.io-57503", + "more_info_path": "/vulnerabilities/CVE-2020-15195/57503", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -65552,10 +65930,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 include a fix for CVE-2020-15211: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative \"-1\" value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the \"-1\" index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue was patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83). A potential workaround would be to add a custom \"Verifier\" to the model loading code to ensure that only operators which accept optional inputs use the \"-1\" special value and only for the tensors that they expect to be optional. Since this allow-list type approach is error-prone, it's advised upgrading to the patched code.", - "cve": "CVE-2020-15211", - "id": "pyup.io-57505", - "more_info_path": "/vulnerabilities/CVE-2020-15211/57505", + "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15203: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the 'fill' argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a 'printf' call is constructed. This may result in segmentation fault.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79", + "cve": "CVE-2020-15203", + "id": "pyup.io-57511", + "more_info_path": "/vulnerabilities/CVE-2020-15203/57511", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -65566,10 +65944,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0a0,<2.1.2,>=2.2.0a0,<2.2.1,>=2.3.0a0,<2.3.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15207: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses 'ResolveAxis' to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the 'DCHECK' does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-q4qf-3fc6-8x34", - "cve": "CVE-2020-15207", - "id": "pyup.io-57512", - "more_info_path": "/vulnerabilities/CVE-2020-15207/57512", + "advisory": "Intel-tensorflow-avx512 versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 include a fix for CVE-2020-15190: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the \"tf.raw_ops.Switch\" operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. However, the eager runtime traverses all tensors in the output. Since only one of the tensors is defined, the other one is \"nullptr\", hence we are binding a reference to \"nullptr\". This is undefined behavior and reported as an error if compiling with \"-fsanitize=null\". In this case, this results in a segmentation fault The issue was patched in commit da8558533d925694483d2c136a9220d6d49d843c\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4g9f-63rx-5cw4", + "cve": "CVE-2020-15190", + "id": "pyup.io-57507", + "more_info_path": "/vulnerabilities/CVE-2020-15190/57507", "specs": [ "<1.15.4", ">=2.0.0a0,<2.0.3", @@ -65608,10 +65986,10 @@ "v": "<1.15.4,>=2.0.0a0,<2.0.3,>=2.1.0rc0,<2.1.2,>=2.2.0rc0,<2.2.1,>=2.3.0rc0,<2.3.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 update its dependency \"PCRE\" to fix CVE-2019-20838.", - "cve": "CVE-2019-20838", - "id": "pyup.io-57486", - "more_info_path": "/vulnerabilities/CVE-2019-20838/57486", + "advisory": "Intel-tensorflow-avx512 versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 update its dependency 'Junit4' to v4.13.1 to include a security fix.", + "cve": "CVE-2020-15250", + "id": "pyup.io-57487", + "more_info_path": "/vulnerabilities/CVE-2020-15250/57487", "specs": [ "<1.15.5", ">=2.0.0a0,<2.0.4", @@ -65622,10 +66000,10 @@ "v": "<1.15.5,>=2.0.0a0,<2.0.4,>=2.1.0rc0,<2.1.3,>=2.2.0rc0,<2.2.2,>=2.3.0rc0,<2.3.2" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 update its dependency 'Junit4' to v4.13.1 to include a security fix.", - "cve": "CVE-2020-15250", - "id": "pyup.io-57487", - "more_info_path": "/vulnerabilities/CVE-2020-15250/57487", + "advisory": "Intel-tensorflow-avx512 versions 2.3.2, 2.2.2, 2.1.3, 2.0.4 and 1.15.5 update its dependency \"PCRE\" to fix CVE-2019-20838.", + "cve": "CVE-2019-20838", + "id": "pyup.io-57486", + "more_info_path": "/vulnerabilities/CVE-2019-20838/57486", "specs": [ "<1.15.5", ">=2.0.0a0,<2.0.4", @@ -65664,10 +66042,10 @@ "v": "<1.15.5,>=2.0.0a0,<2.0.4,>=2.1.0rc0,<2.1.3,>=2.2.0rc0,<2.2.2,>=2.3.0rc0,<2.3.2" }, { - "advisory": "Intel-tensorflow-avx512 versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26268: In affected versions, the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden.", - "cve": "CVE-2020-26268", - "id": "pyup.io-57493", - "more_info_path": "/vulnerabilities/CVE-2020-26268/57493", + "advisory": "Intel-tensorflow-avx512 versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26267: In affected versions, the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes.", + "cve": "CVE-2020-26267", + "id": "pyup.io-57491", + "more_info_path": "/vulnerabilities/CVE-2020-26267/57491", "specs": [ "<1.15.5", ">=2.0.0a0,<2.0.4", @@ -65679,10 +66057,10 @@ "v": "<1.15.5,>=2.0.0a0,<2.0.4,>=2.1.0rc0,<2.1.3,>=2.2.0rc0,<2.2.2,>=2.3.0rc0,<2.3.2,>=2.4.0rc0,<2.4.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26267: In affected versions, the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes.", - "cve": "CVE-2020-26267", - "id": "pyup.io-57491", - "more_info_path": "/vulnerabilities/CVE-2020-26267/57491", + "advisory": "Intel-tensorflow-avx512 versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26270: In affected versions, running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer.", + "cve": "CVE-2020-26270", + "id": "pyup.io-57494", + "more_info_path": "/vulnerabilities/CVE-2020-26270/57494", "specs": [ "<1.15.5", ">=2.0.0a0,<2.0.4", @@ -65694,10 +66072,10 @@ "v": "<1.15.5,>=2.0.0a0,<2.0.4,>=2.1.0rc0,<2.1.3,>=2.2.0rc0,<2.2.2,>=2.3.0rc0,<2.3.2,>=2.4.0rc0,<2.4.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0 includes a fix for CVE-2020-26266: In affected versions and under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen.", - "cve": "CVE-2020-26266", - "id": "pyup.io-57492", - "more_info_path": "/vulnerabilities/CVE-2020-26266/57492", + "advisory": "Intel-tensorflow-avx512 versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26268: In affected versions, the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden.", + "cve": "CVE-2020-26268", + "id": "pyup.io-57493", + "more_info_path": "/vulnerabilities/CVE-2020-26268/57493", "specs": [ "<1.15.5", ">=2.0.0a0,<2.0.4", @@ -65709,10 +66087,10 @@ "v": "<1.15.5,>=2.0.0a0,<2.0.4,>=2.1.0rc0,<2.1.3,>=2.2.0rc0,<2.2.2,>=2.3.0rc0,<2.3.2,>=2.4.0rc0,<2.4.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26271: In affected versions, under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library.", - "cve": "CVE-2020-26271", - "id": "pyup.io-57490", - "more_info_path": "/vulnerabilities/CVE-2020-26271/57490", + "advisory": "Intel-tensorflow-avx512 versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0 includes a fix for CVE-2020-26266: In affected versions and under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen.", + "cve": "CVE-2020-26266", + "id": "pyup.io-57492", + "more_info_path": "/vulnerabilities/CVE-2020-26266/57492", "specs": [ "<1.15.5", ">=2.0.0a0,<2.0.4", @@ -65724,10 +66102,10 @@ "v": "<1.15.5,>=2.0.0a0,<2.0.4,>=2.1.0rc0,<2.1.3,>=2.2.0rc0,<2.2.2,>=2.3.0rc0,<2.3.2,>=2.4.0rc0,<2.4.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26270: In affected versions, running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer.", - "cve": "CVE-2020-26270", - "id": "pyup.io-57494", - "more_info_path": "/vulnerabilities/CVE-2020-26270/57494", + "advisory": "Intel-tensorflow-avx512 versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 and 2.4.0 includes a fix for CVE-2020-26271: In affected versions, under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library.", + "cve": "CVE-2020-26271", + "id": "pyup.io-57490", + "more_info_path": "/vulnerabilities/CVE-2020-26271/57490", "specs": [ "<1.15.5", ">=2.0.0a0,<2.0.4", @@ -65750,10 +66128,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25668: Attackers using Tensorflow prior to 2.12.0 or 2.11.1 can access heap memory which is not in the control of user, leading to a crash or remote code execution.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gw97-ff7c-9v96", - "cve": "CVE-2023-25668", - "id": "pyup.io-57090", - "more_info_path": "/vulnerabilities/CVE-2023-25668/57090", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25661: In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the 'Convolution3DTranspose' function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a 'Convolution3DTranspose' call.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq", + "cve": "CVE-2023-25661", + "id": "pyup.io-57082", + "more_info_path": "/vulnerabilities/CVE-2023-25661/57082", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65761,10 +66139,21 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25663: Prior to versions 2.12.0 and 2.11.1, when 'ctx->step_containter()' is a null ptr, the Lookup function will be executed with a null pointer.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-64jg-wjww-7c5w", - "cve": "CVE-2023-25663", - "id": "pyup.io-57092", - "more_info_path": "/vulnerabilities/CVE-2023-25663/57092", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25801: Prior to versions 2.12.0 and 2.11.1, 'nn_ops.fractional_avg_pool_v2' and 'nn_ops.fractional_max_pool_v2' require the first and fourth elements of their parameter 'pooling_ratio' to be equal to 1.0, as pooling on batch and channel dimensions is not supported.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f49c-87jh-g47q", + "cve": "CVE-2023-25801", + "id": "pyup.io-57083", + "more_info_path": "/vulnerabilities/CVE-2023-25801/57083", + "specs": [ + "<2.11.1", + ">=2.12.0rc0,<2.12.0" + ], + "v": "<2.11.1,>=2.12.0rc0,<2.12.0" + }, + { + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25675: When running versions prior to 2.12.0 and 2.11.1 with XLA, 'tf.raw_ops.Bincount' segfaults when given a parameter 'weights' that is neither the same shape as parameter 'arr' nor a length-0 tensor.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7x4v-9gxg-9hwj", + "cve": "CVE-2023-25675", + "id": "pyup.io-57084", + "more_info_path": "/vulnerabilities/CVE-2023-25675/57084", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65783,10 +66172,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25801: Prior to versions 2.12.0 and 2.11.1, 'nn_ops.fractional_avg_pool_v2' and 'nn_ops.fractional_max_pool_v2' require the first and fourth elements of their parameter 'pooling_ratio' to be equal to 1.0, as pooling on batch and channel dimensions is not supported.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f49c-87jh-g47q", - "cve": "CVE-2023-25801", - "id": "pyup.io-57083", - "more_info_path": "/vulnerabilities/CVE-2023-25801/57083", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25672: The function 'tf.raw_ops.LookupTableImportV2' cannot handle scalars in the 'values' parameter and gives an NPE. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.", + "cve": "CVE-2023-25672", + "id": "pyup.io-57077", + "more_info_path": "/vulnerabilities/CVE-2023-25672/57077", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65794,10 +66183,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25673: Versions prior to 2.12.0 and 2.11.1 have a Floating Point Exception in TensorListSplit with XLA. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.", - "cve": "CVE-2023-25673", - "id": "pyup.io-57076", - "more_info_path": "/vulnerabilities/CVE-2023-25673/57076", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25668: Attackers using Tensorflow prior to 2.12.0 or 2.11.1 can access heap memory which is not in the control of user, leading to a crash or remote code execution.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gw97-ff7c-9v96", + "cve": "CVE-2023-25668", + "id": "pyup.io-57090", + "more_info_path": "/vulnerabilities/CVE-2023-25668/57090", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65805,10 +66194,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25670: Versions prior to 2.12.0 and 2.11.1 have a null point error in QuantizedMatMulWithBiasAndDequantize with MKL enabled.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rq-hwc3-x77w", - "cve": "CVE-2023-25670", - "id": "pyup.io-57088", - "more_info_path": "/vulnerabilities/CVE-2023-25670/57088", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25667: Prior to versions 2.12.0 and 2.11.1, integer overflow occurs when '2^31 <= num_frames * height * width * channels < 2^32', for example Full HD screencast of at least 346 frames.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fqm2-gh8w-gr68", + "cve": "CVE-2023-25667", + "id": "pyup.io-57080", + "more_info_path": "/vulnerabilities/CVE-2023-25667/57080", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65816,10 +66205,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25665: Prior to versions 2.12.0 and 2.11.1, when 'SparseSparseMaximum' is given invalid sparse tensors as inputs, it can give a null pointer error.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-558h-mq8x-7q9g", - "cve": "CVE-2023-25665", - "id": "pyup.io-57085", - "more_info_path": "/vulnerabilities/CVE-2023-25665/57085", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25666: Prior to versions 2.12.0 and 2.11.1, there is a floating point exception in AudioSpectrogram. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f637-vh3r-vfh2", + "cve": "CVE-2023-25666", + "id": "pyup.io-57079", + "more_info_path": "/vulnerabilities/CVE-2023-25666/57079", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65827,10 +66216,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25671: There is out-of-bounds access due to mismatched integer type sizes.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-j5w9-hmfh-4cr6", - "cve": "CVE-2023-25671", - "id": "pyup.io-57087", - "more_info_path": "/vulnerabilities/CVE-2023-25671/57087", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25663: Prior to versions 2.12.0 and 2.11.1, when 'ctx->step_containter()' is a null ptr, the Lookup function will be executed with a null pointer.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-64jg-wjww-7c5w", + "cve": "CVE-2023-25663", + "id": "pyup.io-57092", + "more_info_path": "/vulnerabilities/CVE-2023-25663/57092", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65838,10 +66227,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25661: In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the 'Convolution3DTranspose' function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a 'Convolution3DTranspose' call.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq", - "cve": "CVE-2023-25661", - "id": "pyup.io-57082", - "more_info_path": "/vulnerabilities/CVE-2023-25661/57082", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25664: Prior to versions 2.12.0 and 2.11.1, there is a heap buffer overflow in TAvgPoolGrad.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6hg6-5c2q-7rcr", + "cve": "CVE-2023-25664", + "id": "pyup.io-57091", + "more_info_path": "/vulnerabilities/CVE-2023-25664/57091", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65849,10 +66238,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25675: When running versions prior to 2.12.0 and 2.11.1 with XLA, 'tf.raw_ops.Bincount' segfaults when given a parameter 'weights' that is neither the same shape as parameter 'arr' nor a length-0 tensor.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7x4v-9gxg-9hwj", - "cve": "CVE-2023-25675", - "id": "pyup.io-57084", - "more_info_path": "/vulnerabilities/CVE-2023-25675/57084", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25662: Versions prior to 2.12.0 and 2.11.1 are vulnerable to integer overflow in EditDistance.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7jvm-xxmr-v5cw", + "cve": "CVE-2023-25662", + "id": "pyup.io-57093", + "more_info_path": "/vulnerabilities/CVE-2023-25662/57093", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65860,10 +66249,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-27579: Constructing a tflite model with a paramater 'filter_input_channel' of less than 1 gives a FPE.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5w96-866f-6rm8", - "cve": "CVE-2023-27579", - "id": "pyup.io-57086", - "more_info_path": "/vulnerabilities/CVE-2023-27579/57086", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25659: Prior to versions 2.12.0 and 2.11.1, if the parameter 'indices' for 'DynamicStitch' does not match the shape of the parameter 'data', it can trigger an stack OOB read.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-93vr-9q9m-pj8p", + "cve": "CVE-2023-25659", + "id": "pyup.io-57095", + "more_info_path": "/vulnerabilities/CVE-2023-25659/57095", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65871,10 +66260,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25672: The function 'tf.raw_ops.LookupTableImportV2' cannot handle scalars in the 'values' parameter and gives an NPE. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.", - "cve": "CVE-2023-25672", - "id": "pyup.io-57077", - "more_info_path": "/vulnerabilities/CVE-2023-25672/57077", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25673: Versions prior to 2.12.0 and 2.11.1 have a Floating Point Exception in TensorListSplit with XLA. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.", + "cve": "CVE-2023-25673", + "id": "pyup.io-57076", + "more_info_path": "/vulnerabilities/CVE-2023-25673/57076", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65882,10 +66271,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25669: Prior to versions 2.12.0 and 2.11.1, if the stride and window size are not positive for 'tf.raw_ops.AvgPoolGrad', it can give a floating point exception.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rcf8-g8jv-vg6p", - "cve": "CVE-2023-25669", - "id": "pyup.io-57089", - "more_info_path": "/vulnerabilities/CVE-2023-25669/57089", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25670: Versions prior to 2.12.0 and 2.11.1 have a null point error in QuantizedMatMulWithBiasAndDequantize with MKL enabled.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rq-hwc3-x77w", + "cve": "CVE-2023-25670", + "id": "pyup.io-57088", + "more_info_path": "/vulnerabilities/CVE-2023-25670/57088", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65893,10 +66282,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25667: Prior to versions 2.12.0 and 2.11.1, integer overflow occurs when '2^31 <= num_frames * height * width * channels < 2^32', for example Full HD screencast of at least 346 frames.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fqm2-gh8w-gr68", - "cve": "CVE-2023-25667", - "id": "pyup.io-57080", - "more_info_path": "/vulnerabilities/CVE-2023-25667/57080", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25665: Prior to versions 2.12.0 and 2.11.1, when 'SparseSparseMaximum' is given invalid sparse tensors as inputs, it can give a null pointer error.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-558h-mq8x-7q9g", + "cve": "CVE-2023-25665", + "id": "pyup.io-57085", + "more_info_path": "/vulnerabilities/CVE-2023-25665/57085", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65904,10 +66293,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25664: Prior to versions 2.12.0 and 2.11.1, there is a heap buffer overflow in TAvgPoolGrad.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6hg6-5c2q-7rcr", - "cve": "CVE-2023-25664", - "id": "pyup.io-57091", - "more_info_path": "/vulnerabilities/CVE-2023-25664/57091", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25671: There is out-of-bounds access due to mismatched integer type sizes.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-j5w9-hmfh-4cr6", + "cve": "CVE-2023-25671", + "id": "pyup.io-57087", + "more_info_path": "/vulnerabilities/CVE-2023-25671/57087", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65915,10 +66304,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25662: Versions prior to 2.12.0 and 2.11.1 are vulnerable to integer overflow in EditDistance.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7jvm-xxmr-v5cw", - "cve": "CVE-2023-25662", - "id": "pyup.io-57093", - "more_info_path": "/vulnerabilities/CVE-2023-25662/57093", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-27579: Constructing a tflite model with a paramater 'filter_input_channel' of less than 1 gives a FPE.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5w96-866f-6rm8", + "cve": "CVE-2023-27579", + "id": "pyup.io-57086", + "more_info_path": "/vulnerabilities/CVE-2023-27579/57086", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65926,10 +66315,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25660: Prior to versions 2.12.0 and 2.11.1, when the parameter 'summarize' of 'tf.raw_ops.Print' is zero, the new method 'SummarizeArray' will reference to a nullptr, leading to a seg fault.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qjqc-vqcf-5qvj", - "cve": "CVE-2023-25660", - "id": "pyup.io-57094", - "more_info_path": "/vulnerabilities/CVE-2023-25660/57094", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25669: Prior to versions 2.12.0 and 2.11.1, if the stride and window size are not positive for 'tf.raw_ops.AvgPoolGrad', it can give a floating point exception.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rcf8-g8jv-vg6p", + "cve": "CVE-2023-25669", + "id": "pyup.io-57089", + "more_info_path": "/vulnerabilities/CVE-2023-25669/57089", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65937,10 +66326,10 @@ "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25659: Prior to versions 2.12.0 and 2.11.1, if the parameter 'indices' for 'DynamicStitch' does not match the shape of the parameter 'data', it can trigger an stack OOB read.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-93vr-9q9m-pj8p", - "cve": "CVE-2023-25659", - "id": "pyup.io-57095", - "more_info_path": "/vulnerabilities/CVE-2023-25659/57095", + "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25660: Prior to versions 2.12.0 and 2.11.1, when the parameter 'summarize' of 'tf.raw_ops.Print' is zero, the new method 'SummarizeArray' will reference to a nullptr, leading to a seg fault.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qjqc-vqcf-5qvj", + "cve": "CVE-2023-25660", + "id": "pyup.io-57094", + "more_info_path": "/vulnerabilities/CVE-2023-25660/57094", "specs": [ "<2.11.1", ">=2.12.0rc0,<2.12.0" @@ -65958,17 +66347,6 @@ ], "v": "<2.11.1,>=2.12.0rc0,<2.12.0" }, - { - "advisory": "Intel-tensorflow-avx512 2.11.1 and 2.12.0 include a fix for CVE-2023-25666: Prior to versions 2.12.0 and 2.11.1, there is a floating point exception in AudioSpectrogram. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f637-vh3r-vfh2", - "cve": "CVE-2023-25666", - "id": "pyup.io-57079", - "more_info_path": "/vulnerabilities/CVE-2023-25666/57079", - "specs": [ - "<2.11.1", - ">=2.12.0rc0,<2.12.0" - ], - "v": "<2.11.1,>=2.12.0rc0,<2.12.0" - }, { "advisory": "Intel-tensorflow-avx512 2.12 includes a fix for an Authenticated Local Privilege Escalation vulnerability.\r\nhttps://github.com/advisories/GHSA-m2f8-v8q4-3m59", "cve": "CVE-2023-27506", @@ -65980,30 +66358,30 @@ "v": "<2.12" }, { - "advisory": "Intel-tensorflow-avx512 updates its curl dependency from version 8.2.1 to 8.4.0 to address CVE-2023-38546.", - "cve": "CVE-2023-38546", - "id": "pyup.io-73088", - "more_info_path": "/vulnerabilities/CVE-2023-38546/73088", + "advisory": "Intel-tensorflow-avx512 2.14.0 updates its curl dependency from version 8.2.1 to 8.4.0 to address CVE-2023-38545.", + "cve": "CVE-2023-38545", + "id": "pyup.io-73087", + "more_info_path": "/vulnerabilities/CVE-2023-38545/73087", "specs": [ "<2.14.0" ], "v": "<2.14.0" }, { - "advisory": "Intel-tensorflow-avx512 2.14.0 updates its curl dependency from version 8.2.1 to 8.4.0 to address CVE-2023-38545.", - "cve": "CVE-2023-38545", - "id": "pyup.io-73087", - "more_info_path": "/vulnerabilities/CVE-2023-38545/73087", + "advisory": "Intel-tensorflow-avx512 updates its curl dependency from version 8.2.1 to 8.4.0 to address CVE-2023-38546.", + "cve": "CVE-2023-38546", + "id": "pyup.io-73088", + "more_info_path": "/vulnerabilities/CVE-2023-38546/73088", "specs": [ "<2.14.0" ], "v": "<2.14.0" }, { - "advisory": "Intel-tensorflow-avx512 2.4.0 includes a fix for CVE-2020-15266: In Tensorflow before version 2.4.0, when the 'boxes' argument of 'tf.image.crop_and_resize' has a very large value, the CPU kernel implementation receives it as a C++ 'nan' floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault.\nhttps://github.com/tensorflow/tensorflow/issues/42129\nhttps://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xwhf-g6j5-j5gc", - "cve": "CVE-2020-15266", - "id": "pyup.io-57496", - "more_info_path": "/vulnerabilities/CVE-2020-15266/57496", + "advisory": "Intel-tensorflow-avx512 2.4.0 includes a fix for CVE-2020-15265: In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.", + "cve": "CVE-2020-15265", + "id": "pyup.io-57498", + "more_info_path": "/vulnerabilities/CVE-2020-15265/57498", "specs": [ "<2.4.0" ], @@ -66012,8 +66390,8 @@ { "advisory": "Intel-tensorflow-avx512 2.4.0 includes a fix for CVE-2020-15265: In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.", "cve": "CVE-2020-15265", - "id": "pyup.io-57498", - "more_info_path": "/vulnerabilities/CVE-2020-15265/57498", + "id": "pyup.io-57495", + "more_info_path": "/vulnerabilities/CVE-2020-15265/57495", "specs": [ "<2.4.0" ], @@ -66030,20 +66408,20 @@ "v": "<2.4.0" }, { - "advisory": "Intel-tensorflow-avx512 2.4.0 includes a fix for CVE-2020-15265: In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.", - "cve": "CVE-2020-15265", - "id": "pyup.io-57495", - "more_info_path": "/vulnerabilities/CVE-2020-15265/57495", + "advisory": "Intel-tensorflow-avx512 2.4.0 includes a fix for CVE-2020-15266: In Tensorflow before version 2.4.0, when the 'boxes' argument of 'tf.image.crop_and_resize' has a very large value, the CPU kernel implementation receives it as a C++ 'nan' floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault.\nhttps://github.com/tensorflow/tensorflow/issues/42129\nhttps://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xwhf-g6j5-j5gc", + "cve": "CVE-2020-15266", + "id": "pyup.io-57496", + "more_info_path": "/vulnerabilities/CVE-2020-15266/57496", "specs": [ "<2.4.0" ], "v": "<2.4.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22923.", - "cve": "CVE-2021-22923", - "id": "pyup.io-57293", - "more_info_path": "/vulnerabilities/CVE-2021-22923/57293", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41204: In affected versions, during TensorFlow's Grappler optimizer phase, constant folding might attempt to deep copy a resource tensor. This results in a segfault, as these tensors are supposed to not change. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-786j-5qwq-r36x\nhttps://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659", + "cve": "CVE-2021-41204", + "id": "pyup.io-57297", + "more_info_path": "/vulnerabilities/CVE-2021-41204/57297", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66064,22 +66442,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22925.", - "cve": "CVE-2021-22925", - "id": "pyup.io-57275", - "more_info_path": "/vulnerabilities/CVE-2021-22925/57275", - "specs": [ - "<2.4.4", - ">=2.5.0rc0,<2.5.2", - ">=2.6.0rc0,<2.6.1" - ], - "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" - }, - { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41203: In affected versions, an attacker can trigger undefined behavior, integer overflows, segfaults and 'CHECK'-fail crashes if they can change saved checkpoints from outside of TensorFlow. This is because the checkpoints loading infrastructure is missing validation for invalid file formats.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7pxj-m4jf-r6h2", - "cve": "CVE-2021-41203", - "id": "pyup.io-57300", - "more_info_path": "/vulnerabilities/CVE-2021-41203/57300", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22923.", + "cve": "CVE-2021-22923", + "id": "pyup.io-57293", + "more_info_path": "/vulnerabilities/CVE-2021-22923/57293", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66088,10 +66454,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41225: In affected versions, TensorFlow's Grappler optimizer has a use of unitialized variable. If the 'train_nodes' vector (obtained from the saved model that gets optimized) does not contain a 'Dequeue' node, then 'dequeue_node' is left unitialized. The fix is also included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7r94-xv9v-63jw\nhttps://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3", - "cve": "CVE-2021-41225", - "id": "pyup.io-57306", - "more_info_path": "/vulnerabilities/CVE-2021-41225/57306", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22925.", + "cve": "CVE-2021-22925", + "id": "pyup.io-57275", + "more_info_path": "/vulnerabilities/CVE-2021-22925/57275", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66100,10 +66466,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41198: In affected versions, if 'tf.tile' is called with a large input argument, then the TensorFlow process will crash due to a 'CHECK'-failure caused by an overflow. The number of elements in the output tensor is too much for the 'int64_t' type and the overflow is detected via a 'CHECK' statement. This aborts the process. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-2p25-55c9-h58q", - "cve": "CVE-2021-41198", - "id": "pyup.io-57307", - "more_info_path": "/vulnerabilities/CVE-2021-41198/57307", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41222: In affected versions, the implementation of 'SplitV' can trigger a segfault if an attacker supplies negative arguments. This occurs whenever 'size_splits' contains more than one value and at least one value is negative. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cpf4-wx82-gxp6\nhttps://github.com/tensorflow/tensorflow/commit/25d622ffc432acc736b14ca3904177579e733cc6", + "cve": "CVE-2021-41222", + "id": "pyup.io-57302", + "more_info_path": "/vulnerabilities/CVE-2021-41222/57302", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66112,10 +66478,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41219: In affected versions, the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to 'nullptr'. This occurs whenever the dimensions of 'a' or 'b' are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, it should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4f99-p9c2-3j8x\nhttps://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae", - "cve": "CVE-2021-41219", - "id": "pyup.io-57291", - "more_info_path": "/vulnerabilities/CVE-2021-41219/57291", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41200: In affected versions, if 'tf.summary.create_file_writer' is called with non-scalar arguments, code crashes due to a 'CHECK'-fail. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gh8h-7j2j-qv4f", + "cve": "CVE-2021-41200", + "id": "pyup.io-57303", + "more_info_path": "/vulnerabilities/CVE-2021-41200/57303", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66124,10 +66490,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41213: In affected versions, the code behind 'tf.function' API can be made to deadlock when two 'tf.function' decorated Python functions are mutually recursive. This occurs due to using a non-reentrant 'Lock' Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive 'tf.function', although this is not a frequent scenario.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf\nhttps://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7", - "cve": "CVE-2021-41213", - "id": "pyup.io-57301", - "more_info_path": "/vulnerabilities/CVE-2021-41213/57301", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41203: In affected versions, an attacker can trigger undefined behavior, integer overflows, segfaults and 'CHECK'-fail crashes if they can change saved checkpoints from outside of TensorFlow. This is because the checkpoints loading infrastructure is missing validation for invalid file formats.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7pxj-m4jf-r6h2", + "cve": "CVE-2021-41203", + "id": "pyup.io-57300", + "more_info_path": "/vulnerabilities/CVE-2021-41203/57300", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66136,10 +66502,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41218: In affected versions, the shape inference code for 'AllToAll' can be made to execute a division by 0. This occurs whenever the 'split_count' argument is 0. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9crf-c6qr-r273\nhttps://github.com/tensorflow/tensorflow/commit/a8ad3e5e79c75f36edb81e0ba3f3c0c5442aeddc", - "cve": "CVE-2021-41218", - "id": "pyup.io-57278", - "more_info_path": "/vulnerabilities/CVE-2021-41218/57278", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41225: In affected versions, TensorFlow's Grappler optimizer has a use of unitialized variable. If the 'train_nodes' vector (obtained from the saved model that gets optimized) does not contain a 'Dequeue' node, then 'dequeue_node' is left unitialized. The fix is also included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7r94-xv9v-63jw\nhttps://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3", + "cve": "CVE-2021-41225", + "id": "pyup.io-57306", + "more_info_path": "/vulnerabilities/CVE-2021-41225/57306", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66148,10 +66514,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41201: In affected versions, during execution, 'EinsumHelper::ParseEquation()' is supposed to set the flags in 'input_has_ellipsis' vector and '*output_has_ellipsis' boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to 'true' and never assigns 'false'. This results in unitialized variable access if callers assume that 'EinsumHelper::ParseEquation()' always sets these flags. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-j86v-p27c-73fm\nhttps://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6", - "cve": "CVE-2021-41201", - "id": "pyup.io-57280", - "more_info_path": "/vulnerabilities/CVE-2021-41201/57280", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41219: In affected versions, the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to 'nullptr'. This occurs whenever the dimensions of 'a' or 'b' are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, it should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4f99-p9c2-3j8x\nhttps://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae", + "cve": "CVE-2021-41219", + "id": "pyup.io-57291", + "more_info_path": "/vulnerabilities/CVE-2021-41219/57291", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66171,18 +66537,6 @@ ], "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, - { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41205: In affected versions, the shape inference functions for the 'QuantizeAndDequantizeV*' operations can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rx-x2rw-pc6f\nhttps://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d", - "cve": "CVE-2021-41205", - "id": "pyup.io-57289", - "more_info_path": "/vulnerabilities/CVE-2021-41205/57289", - "specs": [ - "<2.4.4", - ">=2.5.0rc0,<2.5.2", - ">=2.6.0rc0,<2.6.1" - ], - "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" - }, { "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41214: In affected versions, the shape inference code for 'tf.ragged.cross' has an undefined behavior due to binding a reference to 'nullptr'. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vwhq-49r4-gj9v\nhttps://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8", "cve": "CVE-2021-41214", @@ -66196,10 +66550,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41204: In affected versions, during TensorFlow's Grappler optimizer phase, constant folding might attempt to deep copy a resource tensor. This results in a segfault, as these tensors are supposed to not change. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-786j-5qwq-r36x\nhttps://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659", - "cve": "CVE-2021-41204", - "id": "pyup.io-57297", - "more_info_path": "/vulnerabilities/CVE-2021-41204/57297", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41212: In affected versions, the shape inference code for 'tf.ragged.cross' can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fr77-rrx3-cp7g\nhttps://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8", + "cve": "CVE-2021-41212", + "id": "pyup.io-57305", + "more_info_path": "/vulnerabilities/CVE-2021-41212/57305", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66208,10 +66562,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41226: In affected versions, the implementation of 'SparseBinCount' is vulnerable to a heap OOB access. This is because of missing validation between the elements of the 'values' argument and the shape of the sparse output. The fix is also included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-374m-jm66-3vj8\nhttps://github.com/tensorflow/tensorflow/commit/f410212e373eb2aec4c9e60bf3702eba99a38aba", - "cve": "CVE-2021-41226", - "id": "pyup.io-57298", - "more_info_path": "/vulnerabilities/CVE-2021-41226/57298", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22926.", + "cve": "CVE-2021-22926", + "id": "pyup.io-57284", + "more_info_path": "/vulnerabilities/CVE-2021-22926/57284", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66220,10 +66574,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41222: In affected versions, the implementation of 'SplitV' can trigger a segfault if an attacker supplies negative arguments. This occurs whenever 'size_splits' contains more than one value and at least one value is negative. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cpf4-wx82-gxp6\nhttps://github.com/tensorflow/tensorflow/commit/25d622ffc432acc736b14ca3904177579e733cc6", - "cve": "CVE-2021-41222", - "id": "pyup.io-57302", - "more_info_path": "/vulnerabilities/CVE-2021-41222/57302", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41196: In affected versions, the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m539-j985-hcr8", + "cve": "CVE-2021-41196", + "id": "pyup.io-57279", + "more_info_path": "/vulnerabilities/CVE-2021-41196/57279", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66232,10 +66586,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22922.", - "cve": "CVE-2021-22922", - "id": "pyup.io-57276", - "more_info_path": "/vulnerabilities/CVE-2021-22922/57276", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41195: In affected versions, the implementation of 'tf.math.segment_*' operations results in a 'CHECK'-fail related abort (and denial of service) if a segment id in 'segment_ids' is large. This is similar to CVE-2021-29584 (and similar to other reported vulnerabilities in TensorFlow localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using 'AddDim'. However, if the number of elements in the tensor overflows an 'int64_t' value, 'AddDim' results in a 'CHECK' failure which provokes a 'std::abort'. Instead, code should use 'AddDimWithStatus'. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cq76-mxrc-vchh", + "cve": "CVE-2021-41195", + "id": "pyup.io-57282", + "more_info_path": "/vulnerabilities/CVE-2021-41195/57282", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66244,10 +66598,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41200: In affected versions, if 'tf.summary.create_file_writer' is called with non-scalar arguments, code crashes due to a 'CHECK'-fail. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gh8h-7j2j-qv4f", - "cve": "CVE-2021-41200", - "id": "pyup.io-57303", - "more_info_path": "/vulnerabilities/CVE-2021-41200/57303", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41215: In affected versions, the shape inference code for 'DeserializeSparse' can trigger a null pointer dereference. This is because the shape inference function assumes that the 'serialize_sparse' tensor is a tensor with positive rank (and having '3' as the last dimension). The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x3v8-c8qx-3j3r\nhttps://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850", + "cve": "CVE-2021-41215", + "id": "pyup.io-57285", + "more_info_path": "/vulnerabilities/CVE-2021-41215/57285", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66256,10 +66610,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41217: In affected versions, the process of building the control flow graph for a TensorFlow model is vulnerable to a null pointer exception when nodes that should be paired are not. This occurs because the code assumes that the first node in the pairing (e.g., an 'Enter' node) always exists when encountering the second node (e.g., an 'Exit' node). When this is not the case, 'parent' is 'nullptr' so dereferencing it causes a crash. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5crj-c72x-m7gq\nhttps://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff", - "cve": "CVE-2021-41217", - "id": "pyup.io-57288", - "more_info_path": "/vulnerabilities/CVE-2021-41217/57288", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41210: In affected versions, the shape inference functions for 'SparseCountSparseOutput' can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m342-ff57-4jcc\r\nhttps://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2", + "cve": "CVE-2021-41210", + "id": "pyup.io-57308", + "more_info_path": "/vulnerabilities/CVE-2021-41210/57308", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66268,10 +66622,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41216: In affected versions, the shape inference function for 'Transpose' is vulnerable to a heap buffer overflow. This occurs whenever 'perm' contains negative elements. The shape inference function does not validate that the indices in 'perm' are all valid. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-3ff2-r28g-w7h9\nhttps://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14", - "cve": "CVE-2021-41216", - "id": "pyup.io-57294", - "more_info_path": "/vulnerabilities/CVE-2021-41216/57294", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41228: In affected versions, TensorFlow's 'saved_model_cli' tool is vulnerable to a code injection as it calls 'eval' on user supplied strings. This can be used by attackers to run arbitrary code on the plaform where the CLI tool runs. However, given that the tool is always run manually, the impact of this is not severe. The issue has been patched by adding a 'safe' flag which defaults to 'True' and an explicit warning for users.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-3rcw-9p9x-582v\nhttps://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7", + "cve": "CVE-2021-41228", + "id": "pyup.io-57283", + "more_info_path": "/vulnerabilities/CVE-2021-41228/57283", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66280,10 +66634,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41202: In affected versions, while calculating the size of the output within the 'tf.range' kernel, there is a conditional statement of type 'int64 = condition ? int64 : double'. Due to C++ implicit conversion rules, both branches of the condition will be cast to 'double' and the result would be truncated before the assignment. This result in overflows. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xrqm-fpgr-6hhx", - "cve": "CVE-2021-41202", - "id": "pyup.io-57296", - "more_info_path": "/vulnerabilities/CVE-2021-41202/57296", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41227: In affected versions, the 'ImmutableConst' operation in TensorFlow can be tricked into reading arbitrary memory contents. This is because the 'tstring' TensorFlow string class has a special case for memory mapped strings but the operation itself does not offer any support for this datatype. The fix is also included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8c8-67vp-6mx7\nhttps://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b\nhttps://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585", + "cve": "CVE-2021-41227", + "id": "pyup.io-57277", + "more_info_path": "/vulnerabilities/CVE-2021-41227/57277", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66292,10 +66646,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41210: In affected versions, the shape inference functions for 'SparseCountSparseOutput' can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m342-ff57-4jcc\r\nhttps://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2", - "cve": "CVE-2021-41210", - "id": "pyup.io-57308", - "more_info_path": "/vulnerabilities/CVE-2021-41210/57308", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41221: In affected versions, the shape inference code for the 'Cudnn*' operations can be tricked into accessing invalid memory via a heap buffer overflow. This occurs because the ranks of the 'input', 'input_h' and 'input_c' parameters are not validated, but code assumes they have certain values. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqv6-3phm-hcwx\nhttps://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6", + "cve": "CVE-2021-41221", + "id": "pyup.io-57304", + "more_info_path": "/vulnerabilities/CVE-2021-41221/57304", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66304,10 +66658,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41221: In affected versions, the shape inference code for the 'Cudnn*' operations can be tricked into accessing invalid memory via a heap buffer overflow. This occurs because the ranks of the 'input', 'input_h' and 'input_c' parameters are not validated, but code assumes they have certain values. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqv6-3phm-hcwx\nhttps://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6", - "cve": "CVE-2021-41221", - "id": "pyup.io-57304", - "more_info_path": "/vulnerabilities/CVE-2021-41221/57304", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41216: In affected versions, the shape inference function for 'Transpose' is vulnerable to a heap buffer overflow. This occurs whenever 'perm' contains negative elements. The shape inference function does not validate that the indices in 'perm' are all valid. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-3ff2-r28g-w7h9\nhttps://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14", + "cve": "CVE-2021-41216", + "id": "pyup.io-57294", + "more_info_path": "/vulnerabilities/CVE-2021-41216/57294", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66316,10 +66670,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22926.", - "cve": "CVE-2021-22926", - "id": "pyup.io-57284", - "more_info_path": "/vulnerabilities/CVE-2021-22926/57284", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41213: In affected versions, the code behind 'tf.function' API can be made to deadlock when two 'tf.function' decorated Python functions are mutually recursive. This occurs due to using a non-reentrant 'Lock' Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive 'tf.function', although this is not a frequent scenario.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf\nhttps://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7", + "cve": "CVE-2021-41213", + "id": "pyup.io-57301", + "more_info_path": "/vulnerabilities/CVE-2021-41213/57301", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66328,10 +66682,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41196: In affected versions, the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m539-j985-hcr8", - "cve": "CVE-2021-41196", - "id": "pyup.io-57279", - "more_info_path": "/vulnerabilities/CVE-2021-41196/57279", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41218: In affected versions, the shape inference code for 'AllToAll' can be made to execute a division by 0. This occurs whenever the 'split_count' argument is 0. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9crf-c6qr-r273\nhttps://github.com/tensorflow/tensorflow/commit/a8ad3e5e79c75f36edb81e0ba3f3c0c5442aeddc", + "cve": "CVE-2021-41218", + "id": "pyup.io-57278", + "more_info_path": "/vulnerabilities/CVE-2021-41218/57278", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66352,10 +66706,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41227: In affected versions, the 'ImmutableConst' operation in TensorFlow can be tricked into reading arbitrary memory contents. This is because the 'tstring' TensorFlow string class has a special case for memory mapped strings but the operation itself does not offer any support for this datatype. The fix is also included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8c8-67vp-6mx7\nhttps://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b\nhttps://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585", - "cve": "CVE-2021-41227", - "id": "pyup.io-57277", - "more_info_path": "/vulnerabilities/CVE-2021-41227/57277", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41207: In affected versions, the implementation of 'ParallelConcat' misses some input validation and can produce a division by 0. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7v94-64hj-m82h\nhttps://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235", + "cve": "CVE-2021-41207", + "id": "pyup.io-57295", + "more_info_path": "/vulnerabilities/CVE-2021-41207/57295", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66364,10 +66718,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41195: In affected versions, the implementation of 'tf.math.segment_*' operations results in a 'CHECK'-fail related abort (and denial of service) if a segment id in 'segment_ids' is large. This is similar to CVE-2021-29584 (and similar to other reported vulnerabilities in TensorFlow localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using 'AddDim'. However, if the number of elements in the tensor overflows an 'int64_t' value, 'AddDim' results in a 'CHECK' failure which provokes a 'std::abort'. Instead, code should use 'AddDimWithStatus'. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cq76-mxrc-vchh", - "cve": "CVE-2021-41195", - "id": "pyup.io-57282", - "more_info_path": "/vulnerabilities/CVE-2021-41195/57282", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41202: In affected versions, while calculating the size of the output within the 'tf.range' kernel, there is a conditional statement of type 'int64 = condition ? int64 : double'. Due to C++ implicit conversion rules, both branches of the condition will be cast to 'double' and the result would be truncated before the assignment. This result in overflows. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xrqm-fpgr-6hhx", + "cve": "CVE-2021-41202", + "id": "pyup.io-57296", + "more_info_path": "/vulnerabilities/CVE-2021-41202/57296", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66376,10 +66730,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41228: In affected versions, TensorFlow's 'saved_model_cli' tool is vulnerable to a code injection as it calls 'eval' on user supplied strings. This can be used by attackers to run arbitrary code on the plaform where the CLI tool runs. However, given that the tool is always run manually, the impact of this is not severe. The issue has been patched by adding a 'safe' flag which defaults to 'True' and an explicit warning for users.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-3rcw-9p9x-582v\nhttps://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7", - "cve": "CVE-2021-41228", - "id": "pyup.io-57283", - "more_info_path": "/vulnerabilities/CVE-2021-41228/57283", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41226: In affected versions, the implementation of 'SparseBinCount' is vulnerable to a heap OOB access. This is because of missing validation between the elements of the 'values' argument and the shape of the sparse output. The fix is also included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-374m-jm66-3vj8\nhttps://github.com/tensorflow/tensorflow/commit/f410212e373eb2aec4c9e60bf3702eba99a38aba", + "cve": "CVE-2021-41226", + "id": "pyup.io-57298", + "more_info_path": "/vulnerabilities/CVE-2021-41226/57298", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66388,10 +66742,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41215: In affected versions, the shape inference code for 'DeserializeSparse' can trigger a null pointer dereference. This is because the shape inference function assumes that the 'serialize_sparse' tensor is a tensor with positive rank (and having '3' as the last dimension). The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x3v8-c8qx-3j3r\nhttps://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850", - "cve": "CVE-2021-41215", - "id": "pyup.io-57285", - "more_info_path": "/vulnerabilities/CVE-2021-41215/57285", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41224: In affected versions, the implementation of 'SparseFillEmptyRows' can be made to trigger a heap OOB access. This occurs whenever the size of 'indices' does not match the size of 'values'. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rg3m-hqc5-344v\nhttps://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b", + "cve": "CVE-2021-41224", + "id": "pyup.io-57287", + "more_info_path": "/vulnerabilities/CVE-2021-41224/57287", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66400,10 +66754,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41224: In affected versions, the implementation of 'SparseFillEmptyRows' can be made to trigger a heap OOB access. This occurs whenever the size of 'indices' does not match the size of 'values'. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rg3m-hqc5-344v\nhttps://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b", - "cve": "CVE-2021-41224", - "id": "pyup.io-57287", - "more_info_path": "/vulnerabilities/CVE-2021-41224/57287", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41217: In affected versions, the process of building the control flow graph for a TensorFlow model is vulnerable to a null pointer exception when nodes that should be paired are not. This occurs because the code assumes that the first node in the pairing (e.g., an 'Enter' node) always exists when encountering the second node (e.g., an 'Exit' node). When this is not the case, 'parent' is 'nullptr' so dereferencing it causes a crash. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5crj-c72x-m7gq\nhttps://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff", + "cve": "CVE-2021-41217", + "id": "pyup.io-57288", + "more_info_path": "/vulnerabilities/CVE-2021-41217/57288", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66412,10 +66766,10 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41207: In affected versions, the implementation of 'ParallelConcat' misses some input validation and can produce a division by 0. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7v94-64hj-m82h\nhttps://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235", - "cve": "CVE-2021-41207", - "id": "pyup.io-57295", - "more_info_path": "/vulnerabilities/CVE-2021-41207/57295", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41201: In affected versions, during execution, 'EinsumHelper::ParseEquation()' is supposed to set the flags in 'input_has_ellipsis' vector and '*output_has_ellipsis' boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to 'true' and never assigns 'false'. This results in unitialized variable access if callers assume that 'EinsumHelper::ParseEquation()' always sets these flags. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-j86v-p27c-73fm\nhttps://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6", + "cve": "CVE-2021-41201", + "id": "pyup.io-57280", + "more_info_path": "/vulnerabilities/CVE-2021-41201/57280", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66436,10 +66790,34 @@ "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41212: In affected versions, the shape inference code for 'tf.ragged.cross' can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fr77-rrx3-cp7g\nhttps://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8", - "cve": "CVE-2021-41212", - "id": "pyup.io-57305", - "more_info_path": "/vulnerabilities/CVE-2021-41212/57305", + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41198: In affected versions, if 'tf.tile' is called with a large input argument, then the TensorFlow process will crash due to a 'CHECK'-failure caused by an overflow. The number of elements in the output tensor is too much for the 'int64_t' type and the overflow is detected via a 'CHECK' statement. This aborts the process. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-2p25-55c9-h58q", + "cve": "CVE-2021-41198", + "id": "pyup.io-57307", + "more_info_path": "/vulnerabilities/CVE-2021-41198/57307", + "specs": [ + "<2.4.4", + ">=2.5.0rc0,<2.5.2", + ">=2.6.0rc0,<2.6.1" + ], + "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" + }, + { + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 update its dependency 'curl' to v7.78.0 to handle CVE-2021-22922.", + "cve": "CVE-2021-22922", + "id": "pyup.io-57276", + "more_info_path": "/vulnerabilities/CVE-2021-22922/57276", + "specs": [ + "<2.4.4", + ">=2.5.0rc0,<2.5.2", + ">=2.6.0rc0,<2.6.1" + ], + "v": "<2.4.4,>=2.5.0rc0,<2.5.2,>=2.6.0rc0,<2.6.1" + }, + { + "advisory": "Intel-tensorflow-avx512 versions 2.4.4, 2.5.2 and 2.6.1 include a fix for CVE-2021-41205: In affected versions, the shape inference functions for the 'QuantizeAndDequantizeV*' operations can trigger a read outside of bounds of heap allocated array. The fix is included in TensorFlow 2.7.0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rx-x2rw-pc6f\nhttps://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d", + "cve": "CVE-2021-41205", + "id": "pyup.io-57289", + "more_info_path": "/vulnerabilities/CVE-2021-41205/57289", "specs": [ "<2.4.4", ">=2.5.0rc0,<2.5.2", @@ -66473,10 +66851,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1" }, { - "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. The implementation of `GetInitOp` is vulnerable to a crash caused by dereferencing a null pointer. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23577", - "id": "pyup.io-57261", - "more_info_path": "/vulnerabilities/CVE-2022-23577/57261", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21738: The implementation of 'SparseCountSparseOutput' can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4qx-4fjv-hmw6", + "cve": "CVE-2022-21738", + "id": "pyup.io-57268", + "more_info_path": "/vulnerabilities/CVE-2022-21738/57268", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66486,10 +66864,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. A malicious user can cause a use after free behavior when decoding PNG images. After `png::CommonFreeDecode(&decode)` gets called, the values of `decode.width` and `decode.height` are in an unspecified state. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23584", - "id": "pyup.io-57270", - "more_info_path": "/vulnerabilities/CVE-2022-23584/57270", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23595: When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference. In the default scenario, all devices are allowed, so 'flr->config_proto' is 'nullptr'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fpcp-9h7m-ffpx", + "cve": "CVE-2022-23595", + "id": "pyup.io-57228", + "more_info_path": "/vulnerabilities/CVE-2022-23595/57228", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66499,10 +66877,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21738: The implementation of 'SparseCountSparseOutput' can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4qx-4fjv-hmw6", - "cve": "CVE-2022-21738", - "id": "pyup.io-57268", - "more_info_path": "/vulnerabilities/CVE-2022-21738/57268", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21727: The implementation of shape inference for 'Dequantize' is vulnerable to an integer overflow weakness. The 'axis' argument can be '-1' (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes 'axis + 1', an attacker can trigger an integer overflow.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-c6fh-56w7-fvjw", + "cve": "CVE-2022-21727", + "id": "pyup.io-57249", + "more_info_path": "/vulnerabilities/CVE-2022-21727/57249", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66512,10 +66890,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21736: The implementation of 'SparseTensorSliceDataset' has an undefined behavior: under certain conditions, it can be made to dereference a 'nullptr' value. The 3 input arguments to 'SparseTensorSliceDataset' represent a sparse tensor. However, there are some preconditions that these arguments must satisfy, but these are not validated in the implementation.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pfjj-m3jj-9jc9", - "cve": "CVE-2022-21736", - "id": "pyup.io-57227", - "more_info_path": "/vulnerabilities/CVE-2022-21736/57227", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23560: An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvf-hxvg-f67v", + "cve": "CVE-2022-23560", + "id": "pyup.io-57253", + "more_info_path": "/vulnerabilities/CVE-2022-23560/57253", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66525,10 +66903,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23595: When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference. In the default scenario, all devices are allowed, so 'flr->config_proto' is 'nullptr'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fpcp-9h7m-ffpx", - "cve": "CVE-2022-23595", - "id": "pyup.io-57228", - "more_info_path": "/vulnerabilities/CVE-2022-23595/57228", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21740: The implementation of 'SparseCountSparseOutput' is vulnerable to a heap overflow.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-44qp-9wwf-734r", + "cve": "CVE-2022-21740", + "id": "pyup.io-57264", + "more_info_path": "/vulnerabilities/CVE-2022-21740/57264", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66538,10 +66916,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21728: The implementation of shape inference for 'ReverseSequence' does not fully validate the value of 'batch_dim' and can result in a heap OOB read. There is a check to make sure the value of 'batch_dim' does not go over the rank of the input, but there is no check for negative values. Negative dimensions are allowed in some cases to mimic Python's negative indexing (i.e., indexing from the end of the array), however if the value is too negative then the implementation of 'Dim' would access elements before the start of an array.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6gmv-pjp9-p8w8", - "cve": "CVE-2022-21728", - "id": "pyup.io-57224", - "more_info_path": "/vulnerabilities/CVE-2022-21728/57224", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21732: The implementation of 'ThreadPoolHandle' can be used to trigger a denial of service attack by allocating too much memory. This is because the 'num_threads' argument is only checked to not be negative, but there is no upper bound on its value.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-c582-c96p-r5cq", + "cve": "CVE-2022-21732", + "id": "pyup.io-57263", + "more_info_path": "/vulnerabilities/CVE-2022-21732/57263", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66551,10 +66929,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21727: The implementation of shape inference for 'Dequantize' is vulnerable to an integer overflow weakness. The 'axis' argument can be '-1' (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes 'axis + 1', an attacker can trigger an integer overflow.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-c6fh-56w7-fvjw", - "cve": "CVE-2022-21727", - "id": "pyup.io-57249", - "more_info_path": "/vulnerabilities/CVE-2022-21727/57249", + "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that Grappler optimizer would attempt to build a tensor using a reference `dtype`. This would result in a crash due to a `CHECK`-fail in the `Tensor` constructor as reference types are not allowed. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23588", + "id": "pyup.io-57269", + "more_info_path": "/vulnerabilities/CVE-2022-23588/57269", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66564,10 +66942,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that assertions in `function.cc` would be falsified and crash the Python interpreter. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23586", - "id": "pyup.io-57248", - "more_info_path": "/vulnerabilities/CVE-2022-23586/57248", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21735: The implementation of 'FractionalMaxPool' can be made to crash a TensorFlow process via a division by 0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-87v6-crgm-2gfj", + "cve": "CVE-2022-21735", + "id": "pyup.io-57256", + "more_info_path": "/vulnerabilities/CVE-2022-21735/57256", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66577,10 +66955,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23560: An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvf-hxvg-f67v", - "cve": "CVE-2022-23560", - "id": "pyup.io-57253", - "more_info_path": "/vulnerabilities/CVE-2022-23560/57253", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21733: The implementation of 'StringNGrams' can be used to trigger a denial of service attack by causing an out of memory condition after an integer overflow. There is missing a validation on 'pad_witdh' and that result in computing a negative value for 'ngram_width' which is later used to allocate parts of the output.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-98j8-c9q4-r38g", + "cve": "CVE-2022-21733", + "id": "pyup.io-57252", + "more_info_path": "/vulnerabilities/CVE-2022-21733/57252", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66590,10 +66968,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21725: The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f", - "cve": "CVE-2022-21725", - "id": "pyup.io-57255", - "more_info_path": "/vulnerabilities/CVE-2022-21725/57255", + "advisory": "Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, TensorFlow can fail to specialize a type during shape inference. This case is covered by the 'DCHECK' function however, 'DCHECK' is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the 'ValueOrDie' line. This results in an assertion failure as 'ret' contains an error 'Status', not a value. In the second case we also get a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23572", + "id": "pyup.io-57258", + "more_info_path": "/vulnerabilities/CVE-2022-23572/57258", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66603,10 +66981,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21740: The implementation of 'SparseCountSparseOutput' is vulnerable to a heap overflow.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-44qp-9wwf-734r", - "cve": "CVE-2022-21740", - "id": "pyup.io-57264", - "more_info_path": "/vulnerabilities/CVE-2022-21740/57264", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21730: The implementation of 'FractionalAvgPoolGrad' does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vjg4-v33c-ggc4", + "cve": "CVE-2022-21730", + "id": "pyup.io-57226", + "more_info_path": "/vulnerabilities/CVE-2022-21730/57226", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66616,10 +66994,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23561: An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c78-vcq7-7vxq", - "cve": "CVE-2022-23561", - "id": "pyup.io-57273", - "more_info_path": "/vulnerabilities/CVE-2022-23561/57273", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21739: The implementation of 'QuantizedMaxPool' has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-3mw4-6rj6-74g5", + "cve": "CVE-2022-21739", + "id": "pyup.io-57245", + "more_info_path": "/vulnerabilities/CVE-2022-21739/57245", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66629,10 +67007,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21726: The implementation of 'Dequantize' does not fully validate the value of 'axis' and can result in heap OOB accesses. The 'axis' argument can be '-1' (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72", - "cve": "CVE-2022-21726", - "id": "pyup.io-57272", - "more_info_path": "/vulnerabilities/CVE-2022-21726/57272", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21729: The implementation of 'UnravelIndex' is vulnerable to a division by zero caused by an integer overflow bug.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-34f9-hjfq-rr8j", + "cve": "CVE-2022-21729", + "id": "pyup.io-57247", + "more_info_path": "/vulnerabilities/CVE-2022-21729/57247", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66642,10 +67020,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21732: The implementation of 'ThreadPoolHandle' can be used to trigger a denial of service attack by allocating too much memory. This is because the 'num_threads' argument is only checked to not be negative, but there is no upper bound on its value.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-c582-c96p-r5cq", - "cve": "CVE-2022-21732", - "id": "pyup.io-57263", - "more_info_path": "/vulnerabilities/CVE-2022-21732/57263", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23564: When decoding a resource handle tensor from protobuf, a TensorFlow process can encounter cases where a 'CHECK' assertion is invalidated based on user controlled arguments. This allows attackers to cause denial of services in TensorFlow processes.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-8rcj-c8pj-v3m3", + "cve": "CVE-2022-23564", + "id": "pyup.io-57251", + "more_info_path": "/vulnerabilities/CVE-2022-23564/57251", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66655,10 +67033,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. TensorFlow is vulnerable to a heap OOB write in `Grappler`. The `set_output` function writes to an array at the specified index. Hence, this gives a malicious user a write primitive. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23566", - "id": "pyup.io-57265", - "more_info_path": "/vulnerabilities/CVE-2022-23566/57265", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23565: An attacker can trigger denial of service via assertion failure by altering a 'SavedModel' on disk such that 'AttrDef's of some operation are duplicated.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4v5p-v5h9-6xjx", + "cve": "CVE-2022-23565", + "id": "pyup.io-57259", + "more_info_path": "/vulnerabilities/CVE-2022-23565/57259", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66668,10 +67046,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, TensorFlow might do a null-dereference if attributes of some mutable arguments to some operations are missing from the proto. This is guarded by a `DCHECK`. However, `DCHECK` is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the dereferencing of the null pointer, whereas in the second case it results in a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23570", - "id": "pyup.io-57230", - "more_info_path": "/vulnerabilities/CVE-2022-23570/57230", + "advisory": "Tensorflow is an Open Source Machine Learning Framework. The 'GraphDef' format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a 'GraphDef' containing a fragment such as the following can be consumed when loading a 'SavedModel'. This would result in a stack overflow during execution as resolving each 'NodeDef' means resolving the function itself and its nodes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23591", + "id": "pyup.io-57262", + "more_info_path": "/vulnerabilities/CVE-2022-23591/57262", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66681,10 +67059,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. When decoding PNG images TensorFlow can produce a memory leak if the image is invalid. After calling `png::CommonInitDecode(..., &decode)`, the `decode` value contains allocated buffers which can only be freed by calling `png::CommonFreeDecode(&decode)`. However, several error case in the function implementation invoke the `OP_REQUIRES` macro which immediately terminates the execution of the function, without allowing for the memory free to occur. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23585", - "id": "pyup.io-57232", - "more_info_path": "/vulnerabilities/CVE-2022-23585/57232", + "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a `SavedModel` file (fixing the first one would trigger the same dereference in the second place). First, during constant folding, the `GraphDef` might not have the required nodes for the binary operation. If a node is missing, the correposning `mul_*child` would be null, and the dereference in the subsequent line would be incorrect. We have a similar issue during `IsIdentityConsumingSwitch`. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23589", + "id": "pyup.io-57237", + "more_info_path": "/vulnerabilities/CVE-2022-23589/57237", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66694,10 +67072,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23563: In multiple places, TensorFlow uses 'tempfile.mktemp' to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in 'mktemp' and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness). In several instances, TensorFlow was supposed to actually create a temporary directory instead of a file. This logic bug is hidden away by the 'mktemp' function usage. It was replaced 'mktemp' with the safer 'mkstemp'/'mkdtemp' functions, according to the usage pattern.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-wc4g-r73w-x8mm", - "cve": "CVE-2022-23563", - "id": "pyup.io-57225", - "more_info_path": "/vulnerabilities/CVE-2022-23563/57225", + "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23587", + "id": "pyup.io-57254", + "more_info_path": "/vulnerabilities/CVE-2022-23587/57254", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66707,10 +67085,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that Grappler optimizer would attempt to build a tensor using a reference `dtype`. This would result in a crash due to a `CHECK`-fail in the `Tensor` constructor as reference types are not allowed. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23588", - "id": "pyup.io-57269", - "more_info_path": "/vulnerabilities/CVE-2022-23588/57269", + "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that assertions in `function.cc` would be falsified and crash the Python interpreter. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23586", + "id": "pyup.io-57248", + "more_info_path": "/vulnerabilities/CVE-2022-23586/57248", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66720,10 +67098,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. The implementation of `AssignOp` can result in copying uninitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23573", - "id": "pyup.io-57235", - "more_info_path": "/vulnerabilities/CVE-2022-23573/57235", + "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. When decoding PNG images TensorFlow can produce a memory leak if the image is invalid. After calling `png::CommonInitDecode(..., &decode)`, the `decode` value contains allocated buffers which can only be freed by calling `png::CommonFreeDecode(&decode)`. However, several error case in the function implementation invoke the `OP_REQUIRES` macro which immediately terminates the execution of the function, without allowing for the memory free to occur. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23585", + "id": "pyup.io-57232", + "more_info_path": "/vulnerabilities/CVE-2022-23585/57232", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66733,10 +67111,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23562: The implementation of 'Range' suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qx3f-p745-w4hr", - "cve": "CVE-2022-23562", - "id": "pyup.io-57267", - "more_info_path": "/vulnerabilities/CVE-2022-23562/57267", + "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. A malicious user can cause a use after free behavior when decoding PNG images. After `png::CommonFreeDecode(&decode)` gets called, the values of `decode.width` and `decode.height` are in an unspecified state. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23584", + "id": "pyup.io-57270", + "more_info_path": "/vulnerabilities/CVE-2022-23584/57270", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66746,10 +67124,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Tensorflow is an Open Source Machine Learning Framework. The 'GraphDef' format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a 'GraphDef' containing a fragment such as the following can be consumed when loading a 'SavedModel'. This would result in a stack overflow during execution as resolving each 'NodeDef' means resolving the function itself and its nodes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23591", - "id": "pyup.io-57262", - "more_info_path": "/vulnerabilities/CVE-2022-23591/57262", + "advisory": "Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a 'SavedModel' such that any binary op would trigger 'CHECK' failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the 'dtype' no longer matches the 'dtype' expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved. If 'Tin' and 'Tout' don't match the type of data in 'out' and 'input_*' tensors then 'flat<*>' would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a 'CHECK' crash, hence a denial of service. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23583", + "id": "pyup.io-57234", + "more_info_path": "/vulnerabilities/CVE-2022-23583/57234", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66759,10 +67137,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a `SavedModel` file (fixing the first one would trigger the same dereference in the second place). First, during constant folding, the `GraphDef` might not have the required nodes for the binary operation. If a node is missing, the correposning `mul_*child` would be null, and the dereference in the subsequent line would be incorrect. We have a similar issue during `IsIdentityConsumingSwitch`. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23589", - "id": "pyup.io-57237", - "more_info_path": "/vulnerabilities/CVE-2022-23589/57237", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23582: A malicious user can cause a denial of service by altering a 'SavedModel' such that 'TensorByteSize' would trigger 'CHECK' failures. 'TensorShape' constructor throws a 'CHECK'-fail if shape is partial or has a number of elements that would overflow the size of an 'int'. The 'PartialTensorShape' constructor instead does not cause a 'CHECK'-abort if the shape is partial, which is exactly what this function needs to be able to return '-1'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4j82-5ccr-4r8v", + "cve": "CVE-2022-23582", + "id": "pyup.io-57271", + "more_info_path": "/vulnerabilities/CVE-2022-23582/57271", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66772,10 +67150,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23568: The implementation of 'AddManySparseToTensorsMap' is vulnerable to an integer overflow which results in a 'CHECK'-fail when building new 'TensorShape' objects (so, an assert failure based denial of service). There are missing some validation on the shapes of the input tensors as well as directly constructing a large 'TensorShape' with user-provided dimensions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6445-fm66-fvq2", - "cve": "CVE-2022-23568", - "id": "pyup.io-57223", - "more_info_path": "/vulnerabilities/CVE-2022-23568/57223", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23581: The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a 'SavedModel' such that 'IsSimplifiableReshape' would trigger 'CHECK' failures.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fq86-3f29-px2c", + "cve": "CVE-2022-23581", + "id": "pyup.io-57238", + "more_info_path": "/vulnerabilities/CVE-2022-23581/57238", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66785,10 +67163,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. If a graph node is invalid, TensorFlow can leak memory in the implementation of `ImmutableExecutorState::Initialize`. Here, we set `item->kernel` to `nullptr` but it is a simple `OpKernel*` pointer so the memory that was previously allocated to it would leak. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23578", - "id": "pyup.io-57236", - "more_info_path": "/vulnerabilities/CVE-2022-23578/57236", + "advisory": "Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23580: During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-627q-g293-49q7", + "cve": "CVE-2022-23580", + "id": "pyup.io-57260", + "more_info_path": "/vulnerabilities/CVE-2022-23580/57260", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66798,10 +67176,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21737: The implementation of '*Bincount' operations allows malicious users to cause denial of service by passing in arguments which would trigger a 'CHECK'-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in 'CHECK' failures later when the output tensors get allocated.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2vv-v9cg-qhh7", - "cve": "CVE-2022-21737", - "id": "pyup.io-57239", - "more_info_path": "/vulnerabilities/CVE-2022-21737/57239", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23579: The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a 'SavedModel' such that 'SafeToRemoveIdentity' would trigger 'CHECK' failures.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5f2r-qp73-37mr", + "cve": "CVE-2022-23579", + "id": "pyup.io-57266", + "more_info_path": "/vulnerabilities/CVE-2022-23579/57266", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66811,10 +67189,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21735: The implementation of 'FractionalMaxPool' can be made to crash a TensorFlow process via a division by 0.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-87v6-crgm-2gfj", - "cve": "CVE-2022-21735", - "id": "pyup.io-57256", - "more_info_path": "/vulnerabilities/CVE-2022-21735/57256", + "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. If a graph node is invalid, TensorFlow can leak memory in the implementation of `ImmutableExecutorState::Initialize`. Here, we set `item->kernel` to `nullptr` but it is a simple `OpKernel*` pointer so the memory that was previously allocated to it would leak. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23578", + "id": "pyup.io-57236", + "more_info_path": "/vulnerabilities/CVE-2022-23578/57236", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66824,10 +67202,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23579: The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a 'SavedModel' such that 'SafeToRemoveIdentity' would trigger 'CHECK' failures.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5f2r-qp73-37mr", - "cve": "CVE-2022-23579", - "id": "pyup.io-57266", - "more_info_path": "/vulnerabilities/CVE-2022-23579/57266", + "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. The implementation of `GetInitOp` is vulnerable to a crash caused by dereferencing a null pointer. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23577", + "id": "pyup.io-57261", + "more_info_path": "/vulnerabilities/CVE-2022-23577/57261", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66837,10 +67215,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23580: During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-627q-g293-49q7", - "cve": "CVE-2022-23580", - "id": "pyup.io-57260", - "more_info_path": "/vulnerabilities/CVE-2022-23580/57260", + "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateOutputSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve tensors with large enough number of elements. We can have a large enough number of dimensions in `output_shape.dim()` or just a small number of dimensions being large enough to cause an overflow in the multiplication. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23576", + "id": "pyup.io-57233", + "more_info_path": "/vulnerabilities/CVE-2022-23576/57233", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66850,10 +67228,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21731: The implementation of shape inference for 'ConcatV2' can be used to trigger a denial of service attack via a segfault caused by a type confusion. The 'axis' argument is translated into 'concat_dim' in the 'ConcatShapeHelper' helper function. Then, a value for 'min_rank' is computed based on 'concat_dim'. This is then used to validate that the 'values' tensor has at least the required rank. However, 'WithRankAtLeast' receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented. Due to the fact that 'min_rank' is a 32-bits value and the value of 'axis', the 'rank' argument is a negative value, so the error check is bypassed.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m4hf-j54p-p353", - "cve": "CVE-2022-21731", - "id": "pyup.io-57274", - "more_info_path": "/vulnerabilities/CVE-2022-21731/57274", + "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateTensorSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23575", + "id": "pyup.io-57242", + "more_info_path": "/vulnerabilities/CVE-2022-23575/57242", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66863,10 +67241,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateOutputSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve tensors with large enough number of elements. We can have a large enough number of dimensions in `output_shape.dim()` or just a small number of dimensions being large enough to cause an overflow in the multiplication. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23576", - "id": "pyup.io-57233", - "more_info_path": "/vulnerabilities/CVE-2022-23576/57233", + "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. There is a typo in TensorFlow's `SpecializeType` which results in heap OOB read/write. Due to a typo, `arg` is initialized to the `i`th mutable argument in a loop where the loop index is `j`. Hence it is possible to assign to `arg` from outside the vector of arguments. Since this is a mutable proto value, it allows both read and write to outside of bounds data. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23574", + "id": "pyup.io-57240", + "more_info_path": "/vulnerabilities/CVE-2022-23574/57240", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66876,10 +67254,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23581: The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a 'SavedModel' such that 'IsSimplifiableReshape' would trigger 'CHECK' failures.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fq86-3f29-px2c", - "cve": "CVE-2022-23581", - "id": "pyup.io-57238", - "more_info_path": "/vulnerabilities/CVE-2022-23581/57238", + "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. The implementation of `AssignOp` can result in copying uninitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23573", + "id": "pyup.io-57235", + "more_info_path": "/vulnerabilities/CVE-2022-23573/57235", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66889,10 +67267,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. There is a typo in TensorFlow's `SpecializeType` which results in heap OOB read/write. Due to a typo, `arg` is initialized to the `i`th mutable argument in a loop where the loop index is `j`. Hence it is possible to assign to `arg` from outside the vector of arguments. Since this is a mutable proto value, it allows both read and write to outside of bounds data. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23574", - "id": "pyup.io-57240", - "more_info_path": "/vulnerabilities/CVE-2022-23574/57240", + "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a `CHECK` assertion is invalidated based on user controlled arguments, if the tensors have an invalid `dtype` and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow processes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23571", + "id": "pyup.io-57250", + "more_info_path": "/vulnerabilities/CVE-2022-23571/57250", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66902,10 +67280,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23559: An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both 'embedding_size' and 'lookup_size' are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-98p5-x8x4-c9m5", - "cve": "CVE-2022-23559", - "id": "pyup.io-57246", - "more_info_path": "/vulnerabilities/CVE-2022-23559/57246", + "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, TensorFlow might do a null-dereference if attributes of some mutable arguments to some operations are missing from the proto. This is guarded by a `DCHECK`. However, `DCHECK` is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the dereferencing of the null pointer, whereas in the second case it results in a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23570", + "id": "pyup.io-57230", + "more_info_path": "/vulnerabilities/CVE-2022-23570/57230", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66915,10 +67293,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21733: The implementation of 'StringNGrams' can be used to trigger a denial of service attack by causing an out of memory condition after an integer overflow. There is missing a validation on 'pad_witdh' and that result in computing a negative value for 'ngram_width' which is later used to allocate parts of the output.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-98j8-c9q4-r38g", - "cve": "CVE-2022-21733", - "id": "pyup.io-57252", - "more_info_path": "/vulnerabilities/CVE-2022-21733/57252", + "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. TensorFlow is vulnerable to a heap OOB write in `Grappler`. The `set_output` function writes to an array at the specified index. Hence, this gives a malicious user a write primitive. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", + "cve": "CVE-2022-23566", + "id": "pyup.io-57265", + "more_info_path": "/vulnerabilities/CVE-2022-23566/57265", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66928,10 +67306,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23587", - "id": "pyup.io-57254", - "more_info_path": "/vulnerabilities/CVE-2022-23587/57254", + "advisory": "Tensorflow versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23563: In multiple places, TensorFlow uses 'tempfile.mktemp' to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in 'mktemp' and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness). In several instances, TensorFlow was supposed to actually create a temporary directory instead of a file. This logic bug is hidden away by the 'mktemp' function usage. It was replaced 'mktemp' with the safer 'mkstemp'/'mkdtemp' functions, according to the usage pattern.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-wc4g-r73w-x8mm", + "cve": "CVE-2022-23563", + "id": "pyup.io-57225", + "more_info_path": "/vulnerabilities/CVE-2022-23563/57225", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66941,10 +67319,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23557: An attacker can craft a TFLite model that would trigger a division by zero in 'BiasAndClamp' implementation. There is no check that the 'bias_size' is non zero.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf2j-f278-xh4v", - "cve": "CVE-2022-23557", - "id": "pyup.io-57257", - "more_info_path": "/vulnerabilities/CVE-2022-23557/57257", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23562: The implementation of 'Range' suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qx3f-p745-w4hr", + "cve": "CVE-2022-23562", + "id": "pyup.io-57267", + "more_info_path": "/vulnerabilities/CVE-2022-23562/57267", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66954,10 +67332,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21741: An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-428x-9xc2-m8mj", - "cve": "CVE-2022-21741", - "id": "pyup.io-57229", - "more_info_path": "/vulnerabilities/CVE-2022-21741/57229", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23561: An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c78-vcq7-7vxq", + "cve": "CVE-2022-23561", + "id": "pyup.io-57273", + "more_info_path": "/vulnerabilities/CVE-2022-23561/57273", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66967,10 +67345,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, TensorFlow can fail to specialize a type during shape inference. This case is covered by the 'DCHECK' function however, 'DCHECK' is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the 'ValueOrDie' line. This results in an assertion failure as 'ret' contains an error 'Status', not a value. In the second case we also get a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23572", - "id": "pyup.io-57258", - "more_info_path": "/vulnerabilities/CVE-2022-23572/57258", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23559: An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both 'embedding_size' and 'lookup_size' are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-98p5-x8x4-c9m5", + "cve": "CVE-2022-23559", + "id": "pyup.io-57246", + "more_info_path": "/vulnerabilities/CVE-2022-23559/57246", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66980,10 +67358,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a 'SavedModel' such that any binary op would trigger 'CHECK' failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the 'dtype' no longer matches the 'dtype' expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved. If 'Tin' and 'Tout' don't match the type of data in 'out' and 'input_*' tensors then 'flat<*>' would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a 'CHECK' crash, hence a denial of service. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23583", - "id": "pyup.io-57234", - "more_info_path": "/vulnerabilities/CVE-2022-23583/57234", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23558: An attacker can craft a TFLite model that would cause an integer overflow in 'TfLiteIntArrayCreate'. The 'TfLiteIntArrayGetSizeInBytes' returns an 'int' instead of a 'size_t'. An attacker can control model inputs such that 'computed_size' overflows the size of 'int' datatype.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9gwq-6cwj-47h3", + "cve": "CVE-2022-23558", + "id": "pyup.io-57243", + "more_info_path": "/vulnerabilities/CVE-2022-23558/57243", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -66993,10 +67371,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21730: The implementation of 'FractionalAvgPoolGrad' does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vjg4-v33c-ggc4", - "cve": "CVE-2022-21730", - "id": "pyup.io-57226", - "more_info_path": "/vulnerabilities/CVE-2022-21730/57226", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23557: An attacker can craft a TFLite model that would trigger a division by zero in 'BiasAndClamp' implementation. There is no check that the 'bias_size' is non zero.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf2j-f278-xh4v", + "cve": "CVE-2022-23557", + "id": "pyup.io-57257", + "more_info_path": "/vulnerabilities/CVE-2022-23557/57257", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -67006,10 +67384,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21734: The implementation of 'MapStage' is vulnerable to a 'CHECK'-fail if the key tensor is not a scalar.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gcvh-66ff-4mwm", - "cve": "CVE-2022-21734", - "id": "pyup.io-57231", - "more_info_path": "/vulnerabilities/CVE-2022-21734/57231", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21741: An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-428x-9xc2-m8mj", + "cve": "CVE-2022-21741", + "id": "pyup.io-57229", + "more_info_path": "/vulnerabilities/CVE-2022-21741/57229", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -67019,10 +67397,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23567: The implementations of 'Sparse*Cwise*' ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or 'CHECK'-fails when building new 'TensorShape' objects (so, assert failures based denial of service). There are missing some validation on the shapes of the input tensors as well as directly constructing a large 'TensorShape' with user-provided dimensions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrx2-r989-2c43", - "cve": "CVE-2022-23567", - "id": "pyup.io-57244", - "more_info_path": "/vulnerabilities/CVE-2022-23567/57244", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21737: The implementation of '*Bincount' operations allows malicious users to cause denial of service by passing in arguments which would trigger a 'CHECK'-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in 'CHECK' failures later when the output tensors get allocated.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2vv-v9cg-qhh7", + "cve": "CVE-2022-21737", + "id": "pyup.io-57239", + "more_info_path": "/vulnerabilities/CVE-2022-21737/57239", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -67045,10 +67423,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateTensorSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23575", - "id": "pyup.io-57242", - "more_info_path": "/vulnerabilities/CVE-2022-23575/57242", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21734: The implementation of 'MapStage' is vulnerable to a 'CHECK'-fail if the key tensor is not a scalar.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gcvh-66ff-4mwm", + "cve": "CVE-2022-21734", + "id": "pyup.io-57231", + "more_info_path": "/vulnerabilities/CVE-2022-21734/57231", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -67058,10 +67436,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23558: An attacker can craft a TFLite model that would cause an integer overflow in 'TfLiteIntArrayCreate'. The 'TfLiteIntArrayGetSizeInBytes' returns an 'int' instead of a 'size_t'. An attacker can control model inputs such that 'computed_size' overflows the size of 'int' datatype.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9gwq-6cwj-47h3", - "cve": "CVE-2022-23558", - "id": "pyup.io-57243", - "more_info_path": "/vulnerabilities/CVE-2022-23558/57243", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21725: The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f", + "cve": "CVE-2022-21725", + "id": "pyup.io-57255", + "more_info_path": "/vulnerabilities/CVE-2022-21725/57255", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -67071,10 +67449,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21739: The implementation of 'QuantizedMaxPool' has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-3mw4-6rj6-74g5", - "cve": "CVE-2022-21739", - "id": "pyup.io-57245", - "more_info_path": "/vulnerabilities/CVE-2022-21739/57245", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23568: The implementation of 'AddManySparseToTensorsMap' is vulnerable to an integer overflow which results in a 'CHECK'-fail when building new 'TensorShape' objects (so, an assert failure based denial of service). There are missing some validation on the shapes of the input tensors as well as directly constructing a large 'TensorShape' with user-provided dimensions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6445-fm66-fvq2", + "cve": "CVE-2022-23568", + "id": "pyup.io-57223", + "more_info_path": "/vulnerabilities/CVE-2022-23568/57223", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -67084,10 +67462,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a `CHECK` assertion is invalidated based on user controlled arguments, if the tensors have an invalid `dtype` and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow processes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.", - "cve": "CVE-2022-23571", - "id": "pyup.io-57250", - "more_info_path": "/vulnerabilities/CVE-2022-23571/57250", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23567: The implementations of 'Sparse*Cwise*' ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or 'CHECK'-fails when building new 'TensorShape' objects (so, assert failures based denial of service). There are missing some validation on the shapes of the input tensors as well as directly constructing a large 'TensorShape' with user-provided dimensions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrx2-r989-2c43", + "cve": "CVE-2022-23567", + "id": "pyup.io-57244", + "more_info_path": "/vulnerabilities/CVE-2022-23567/57244", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -67097,10 +67475,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23582: A malicious user can cause a denial of service by altering a 'SavedModel' such that 'TensorByteSize' would trigger 'CHECK' failures. 'TensorShape' constructor throws a 'CHECK'-fail if shape is partial or has a number of elements that would overflow the size of an 'int'. The 'PartialTensorShape' constructor instead does not cause a 'CHECK'-abort if the shape is partial, which is exactly what this function needs to be able to return '-1'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4j82-5ccr-4r8v", - "cve": "CVE-2022-23582", - "id": "pyup.io-57271", - "more_info_path": "/vulnerabilities/CVE-2022-23582/57271", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21736: The implementation of 'SparseTensorSliceDataset' has an undefined behavior: under certain conditions, it can be made to dereference a 'nullptr' value. The 3 input arguments to 'SparseTensorSliceDataset' represent a sparse tensor. However, there are some preconditions that these arguments must satisfy, but these are not validated in the implementation.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pfjj-m3jj-9jc9", + "cve": "CVE-2022-21736", + "id": "pyup.io-57227", + "more_info_path": "/vulnerabilities/CVE-2022-21736/57227", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -67110,10 +67488,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21729: The implementation of 'UnravelIndex' is vulnerable to a division by zero caused by an integer overflow bug.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-34f9-hjfq-rr8j", - "cve": "CVE-2022-21729", - "id": "pyup.io-57247", - "more_info_path": "/vulnerabilities/CVE-2022-21729/57247", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21731: The implementation of shape inference for 'ConcatV2' can be used to trigger a denial of service attack via a segfault caused by a type confusion. The 'axis' argument is translated into 'concat_dim' in the 'ConcatShapeHelper' helper function. Then, a value for 'min_rank' is computed based on 'concat_dim'. This is then used to validate that the 'values' tensor has at least the required rank. However, 'WithRankAtLeast' receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented. Due to the fact that 'min_rank' is a 32-bits value and the value of 'axis', the 'rank' argument is a negative value, so the error check is bypassed.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m4hf-j54p-p353", + "cve": "CVE-2022-21731", + "id": "pyup.io-57274", + "more_info_path": "/vulnerabilities/CVE-2022-21731/57274", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -67123,10 +67501,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23564: When decoding a resource handle tensor from protobuf, a TensorFlow process can encounter cases where a 'CHECK' assertion is invalidated based on user controlled arguments. This allows attackers to cause denial of services in TensorFlow processes.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-8rcj-c8pj-v3m3", - "cve": "CVE-2022-23564", - "id": "pyup.io-57251", - "more_info_path": "/vulnerabilities/CVE-2022-23564/57251", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21728: The implementation of shape inference for 'ReverseSequence' does not fully validate the value of 'batch_dim' and can result in a heap OOB read. There is a check to make sure the value of 'batch_dim' does not go over the rank of the input, but there is no check for negative values. Negative dimensions are allowed in some cases to mimic Python's negative indexing (i.e., indexing from the end of the array), however if the value is too negative then the implementation of 'Dim' would access elements before the start of an array.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-6gmv-pjp9-p8w8", + "cve": "CVE-2022-21728", + "id": "pyup.io-57224", + "more_info_path": "/vulnerabilities/CVE-2022-21728/57224", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -67136,10 +67514,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-23565: An attacker can trigger denial of service via assertion failure by altering a 'SavedModel' on disk such that 'AttrDef's of some operation are duplicated.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4v5p-v5h9-6xjx", - "cve": "CVE-2022-23565", - "id": "pyup.io-57259", - "more_info_path": "/vulnerabilities/CVE-2022-23565/57259", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3, 2.7.1 and 2.8.0 include a fix for CVE-2022-21726: The implementation of 'Dequantize' does not fully validate the value of 'axis' and can result in heap OOB accesses. The 'axis' argument can be '-1' (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72", + "cve": "CVE-2022-21726", + "id": "pyup.io-57272", + "more_info_path": "/vulnerabilities/CVE-2022-21726/57272", "specs": [ "<2.5.3", ">=2.6.0a0,<2.6.3", @@ -67149,10 +67527,10 @@ "v": "<2.5.3,>=2.6.0a0,<2.6.3,>=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3 and 2.7.1 include a fix for CVE-2021-41206: In affected versions, several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or 'CHECK'-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible. These issues were discovered internally via tooling while working on improving/testing GPU op determinism. As such, there aren't reproducers and there will be multiple fixes for these issues.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pgcq-h79j-2f69\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-43q8-3fv7-pr5x", - "cve": "CVE-2021-41206", - "id": "pyup.io-57220", - "more_info_path": "/vulnerabilities/CVE-2021-41206/57220", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3 and 2.7.1 include a fix for CVE-2021-41208: In affected versions, the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service (via dereferencing 'nullptr's or via 'CHECK'-failures) as well as abuse undefined behavior (binding references to 'nullptr's). An attacker can also read and write from heap buffers, depending on the API that gets used and the arguments that are passed to the call. Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. TensorFlow's boosted trees APIs will be deprecated in subsequent releases.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-57wx-m983-2f88\nhttps://github.com/tensorflow/tensorflow/commit/5c8c9a8bfe750f9743d0c859bae112060b216f5c\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6gw-r52c-724r", + "cve": "CVE-2021-41208", + "id": "pyup.io-57221", + "more_info_path": "/vulnerabilities/CVE-2021-41208/57221", "specs": [ "<2.5.3", ">=2.6.0rc0,<2.6.3", @@ -67161,10 +67539,10 @@ "v": "<2.5.3,>=2.6.0rc0,<2.6.3,>=2.7.0rc0,<2.7.1" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3 and 2.7.1 include a fix for CVE-2021-41208: In affected versions, the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service (via dereferencing 'nullptr's or via 'CHECK'-failures) as well as abuse undefined behavior (binding references to 'nullptr's). An attacker can also read and write from heap buffers, depending on the API that gets used and the arguments that are passed to the call. Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. TensorFlow's boosted trees APIs will be deprecated in subsequent releases.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-57wx-m983-2f88\nhttps://github.com/tensorflow/tensorflow/commit/5c8c9a8bfe750f9743d0c859bae112060b216f5c\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6gw-r52c-724r", - "cve": "CVE-2021-41208", - "id": "pyup.io-57221", - "more_info_path": "/vulnerabilities/CVE-2021-41208/57221", + "advisory": "Intel-tensorflow-avx512 versions 2.5.3, 2.6.3 and 2.7.1 include a fix for CVE-2021-41206: In affected versions, several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or 'CHECK'-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible. These issues were discovered internally via tooling while working on improving/testing GPU op determinism. As such, there aren't reproducers and there will be multiple fixes for these issues.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pgcq-h79j-2f69\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-43q8-3fv7-pr5x", + "cve": "CVE-2021-41206", + "id": "pyup.io-57220", + "more_info_path": "/vulnerabilities/CVE-2021-41206/57220", "specs": [ "<2.5.3", ">=2.6.0rc0,<2.6.3", @@ -67212,10 +67590,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29209: Type confusion leading to 'CHECK'-failure based denial of service.", - "cve": "CVE-2022-29209", - "id": "pyup.io-57212", - "more_info_path": "/vulnerabilities/CVE-2022-29209/57212", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29198: Missing validation which causes denial of service via 'SparseTensorToCSRSparseMatrix'.", + "cve": "CVE-2022-29198", + "id": "pyup.io-57186", + "more_info_path": "/vulnerabilities/CVE-2022-29198/57186", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67225,10 +67603,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29211: Segfault when 'tf.histogram_fixed_width' is called with NaN values.", - "cve": "CVE-2022-29211", - "id": "pyup.io-57205", - "more_info_path": "/vulnerabilities/CVE-2022-29211/57205", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29204: Missing validation which causes denial of service via 'Conv3DBackpropFilterV2'.", + "cve": "CVE-2022-29204", + "id": "pyup.io-57204", + "more_info_path": "/vulnerabilities/CVE-2022-29204/57204", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67238,10 +67616,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29198: Missing validation which causes denial of service via 'SparseTensorToCSRSparseMatrix'.", - "cve": "CVE-2022-29198", - "id": "pyup.io-57186", - "more_info_path": "/vulnerabilities/CVE-2022-29198/57186", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-22576.", + "cve": "CVE-2022-22576", + "id": "pyup.io-57211", + "more_info_path": "/vulnerabilities/CVE-2022-22576/57211", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67251,10 +67629,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29204: Missing validation which causes denial of service via 'Conv3DBackpropFilterV2'.", - "cve": "CVE-2022-29204", - "id": "pyup.io-57204", - "more_info_path": "/vulnerabilities/CVE-2022-29204/57204", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27780.", + "cve": "CVE-2022-27780", + "id": "pyup.io-57207", + "more_info_path": "/vulnerabilities/CVE-2022-27780/57207", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67264,12 +67642,12 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29193: missing validation which causes 'TensorSummaryV2' to crash.", - "cve": "CVE-2022-29193", - "id": "pyup.io-57183", - "more_info_path": "/vulnerabilities/CVE-2022-29193/57183", - "specs": [ - "<2.6.4", + "advisory": "Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29200: Missing validation which causes denial of service via 'LSTMBlockCell'.", + "cve": "CVE-2022-29200", + "id": "pyup.io-57185", + "more_info_path": "/vulnerabilities/CVE-2022-29200/57185", + "specs": [ + "<2.6.4", ">=2.7.0rc0,<2.7.2", ">=2.8.0rc0,<2.8.1", ">=2.9.0rc0,<2.9.0" @@ -67277,10 +67655,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29207: Issues arising from undefined behavior stemming from users supplying invalid resource handles.", - "cve": "CVE-2022-29207", - "id": "pyup.io-57188", - "more_info_path": "/vulnerabilities/CVE-2022-29207/57188", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27781.", + "cve": "CVE-2022-27781", + "id": "pyup.io-57197", + "more_info_path": "/vulnerabilities/CVE-2022-27781/57197", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67290,10 +67668,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29194: Missing validation which causes denial of service via 'DeleteSessionTensor'.", - "cve": "CVE-2022-29194", - "id": "pyup.io-57192", - "more_info_path": "/vulnerabilities/CVE-2022-29194/57192", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-30115.", + "cve": "CVE-2022-30115", + "id": "pyup.io-57198", + "more_info_path": "/vulnerabilities/CVE-2022-30115/57198", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67303,10 +67681,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29199: Missing validation which causes denial of service via 'LoadAndRemapMatrix'.", - "cve": "CVE-2022-29199", - "id": "pyup.io-57209", - "more_info_path": "/vulnerabilities/CVE-2022-29199/57209", + "advisory": "Affected versions of intel-tensorflow-avx512 are vulnerable to Denial of Service in the implementation of depthwise ops via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor. This is another instance of TFSA-2021-198 (CVE-2021-41197).", + "cve": "PVE-2024-71511", + "id": "pyup.io-71773", + "more_info_path": "/vulnerabilities/PVE-2024-71511/71773", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67316,10 +67694,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29201: Missing validation which results in undefined behavior in 'QuantizedConv2D'.", - "cve": "CVE-2022-29201", - "id": "pyup.io-57202", - "more_info_path": "/vulnerabilities/CVE-2022-29201/57202", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29216: Code injection in 'saved_model_cli'.", + "cve": "CVE-2022-29216", + "id": "pyup.io-57182", + "more_info_path": "/vulnerabilities/CVE-2022-29216/57182", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67329,10 +67707,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27780.", - "cve": "CVE-2022-27780", - "id": "pyup.io-57207", - "more_info_path": "/vulnerabilities/CVE-2022-27780/57207", + "advisory": "Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29213: Crashes stemming from incomplete validation in signal ops.", + "cve": "CVE-2022-29213", + "id": "pyup.io-57210", + "more_info_path": "/vulnerabilities/CVE-2022-29213/57210", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67342,10 +67720,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29200: Missing validation which causes denial of service via 'LSTMBlockCell'.", - "cve": "CVE-2022-29200", - "id": "pyup.io-57185", - "more_info_path": "/vulnerabilities/CVE-2022-29200/57185", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'zlib' to v1.2.12 to handle CVE-2018-25032.", + "cve": "CVE-2018-25032", + "id": "pyup.io-57199", + "more_info_path": "/vulnerabilities/CVE-2018-25032/57199", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67355,10 +67733,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27781.", - "cve": "CVE-2022-27781", - "id": "pyup.io-57197", - "more_info_path": "/vulnerabilities/CVE-2022-27781/57197", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29212: Core dump when loading TFLite models with quantization.", + "cve": "CVE-2022-29212", + "id": "pyup.io-57215", + "more_info_path": "/vulnerabilities/CVE-2022-29212/57215", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67368,10 +67746,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-30115.", - "cve": "CVE-2022-30115", - "id": "pyup.io-57198", - "more_info_path": "/vulnerabilities/CVE-2022-30115/57198", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29211: Segfault when 'tf.histogram_fixed_width' is called with NaN values.", + "cve": "CVE-2022-29211", + "id": "pyup.io-57205", + "more_info_path": "/vulnerabilities/CVE-2022-29211/57205", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67381,10 +67759,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29206: Missing validation which results in undefined behavior in 'SparseTensorDenseAdd'.", - "cve": "CVE-2022-29206", - "id": "pyup.io-57203", - "more_info_path": "/vulnerabilities/CVE-2022-29206/57203", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29209: Type confusion leading to 'CHECK'-failure based denial of service.", + "cve": "CVE-2022-29209", + "id": "pyup.io-57212", + "more_info_path": "/vulnerabilities/CVE-2022-29209/57212", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67394,10 +67772,23 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29195: Missing validation which causes denial of service via 'StagePeek'.", - "cve": "CVE-2022-29195", - "id": "pyup.io-57191", - "more_info_path": "/vulnerabilities/CVE-2022-29195/57191", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29208: Segfault and OOB write due to incomplete validation in 'EditDistance'.", + "cve": "CVE-2022-29208", + "id": "pyup.io-57190", + "more_info_path": "/vulnerabilities/CVE-2022-29208/57190", + "specs": [ + "<2.6.4", + ">=2.7.0rc0,<2.7.2", + ">=2.8.0rc0,<2.8.1", + ">=2.9.0rc0,<2.9.0" + ], + "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" + }, + { + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29206: Missing validation which results in undefined behavior in 'SparseTensorDenseAdd'.", + "cve": "CVE-2022-29206", + "id": "pyup.io-57203", + "more_info_path": "/vulnerabilities/CVE-2022-29206/57203", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67420,10 +67811,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Affected versions of intel-tensorflow-avx512 are vulnerable to Denial of Service in the implementation of depthwise ops via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor. This is another instance of TFSA-2021-198 (CVE-2021-41197).", - "cve": "PVE-2024-71511", - "id": "pyup.io-71773", - "more_info_path": "/vulnerabilities/PVE-2024-71511/71773", + "advisory": "Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29202: Denial of service in 'tf.ragged.constant' due to lack of validation.", + "cve": "CVE-2022-29202", + "id": "pyup.io-57201", + "more_info_path": "/vulnerabilities/CVE-2022-29202/57201", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67433,10 +67824,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29197: Missing validation which causes denial of service via 'UnsortedSegmentJoin'.", - "cve": "CVE-2022-29197", - "id": "pyup.io-57189", - "more_info_path": "/vulnerabilities/CVE-2022-29197/57189", + "advisory": "Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29201: Missing validation which results in undefined behavior in 'QuantizedConv2D'.", + "cve": "CVE-2022-29201", + "id": "pyup.io-57202", + "more_info_path": "/vulnerabilities/CVE-2022-29201/57202", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67446,10 +67837,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'zlib' to v1.2.12 to handle CVE-2018-25032.", - "cve": "CVE-2018-25032", - "id": "pyup.io-57199", - "more_info_path": "/vulnerabilities/CVE-2018-25032/57199", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29207: Issues arising from undefined behavior stemming from users supplying invalid resource handles.", + "cve": "CVE-2022-29207", + "id": "pyup.io-57188", + "more_info_path": "/vulnerabilities/CVE-2022-29207/57188", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67459,10 +67850,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-22576.", - "cve": "CVE-2022-22576", - "id": "pyup.io-57211", - "more_info_path": "/vulnerabilities/CVE-2022-22576/57211", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29199: Missing validation which causes denial of service via 'LoadAndRemapMatrix'.", + "cve": "CVE-2022-29199", + "id": "pyup.io-57209", + "more_info_path": "/vulnerabilities/CVE-2022-29199/57209", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67472,10 +67863,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27775.", - "cve": "CVE-2022-27775", - "id": "pyup.io-57181", - "more_info_path": "/vulnerabilities/CVE-2022-27775/57181", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29197: Missing validation which causes denial of service via 'UnsortedSegmentJoin'.", + "cve": "CVE-2022-29197", + "id": "pyup.io-57189", + "more_info_path": "/vulnerabilities/CVE-2022-29197/57189", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67485,10 +67876,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29208: Segfault and OOB write due to incomplete validation in 'EditDistance'.", - "cve": "CVE-2022-29208", - "id": "pyup.io-57190", - "more_info_path": "/vulnerabilities/CVE-2022-29208/57190", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29195: Missing validation which causes denial of service via 'StagePeek'.", + "cve": "CVE-2022-29195", + "id": "pyup.io-57191", + "more_info_path": "/vulnerabilities/CVE-2022-29195/57191", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67498,10 +67889,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29203: Integer overflow in 'SpaceToBatchND'.", - "cve": "CVE-2022-29203", - "id": "pyup.io-57200", - "more_info_path": "/vulnerabilities/CVE-2022-29203/57200", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29193: missing validation which causes 'TensorSummaryV2' to crash.", + "cve": "CVE-2022-29193", + "id": "pyup.io-57183", + "more_info_path": "/vulnerabilities/CVE-2022-29193/57183", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67511,10 +67902,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29202: Denial of service in 'tf.ragged.constant' due to lack of validation.", - "cve": "CVE-2022-29202", - "id": "pyup.io-57201", - "more_info_path": "/vulnerabilities/CVE-2022-29202/57201", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29194: Missing validation which causes denial of service via 'DeleteSessionTensor'.", + "cve": "CVE-2022-29194", + "id": "pyup.io-57192", + "more_info_path": "/vulnerabilities/CVE-2022-29194/57192", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67537,10 +67928,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29212: Core dump when loading TFLite models with quantization.", - "cve": "CVE-2022-29212", - "id": "pyup.io-57215", - "more_info_path": "/vulnerabilities/CVE-2022-29212/57215", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29191: Missing validation which causes denial of service via 'GetSessionTensor'.", + "cve": "CVE-2022-29191", + "id": "pyup.io-57214", + "more_info_path": "/vulnerabilities/CVE-2022-29191/57214", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67550,10 +67941,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29191: Missing validation which causes denial of service via 'GetSessionTensor'.", - "cve": "CVE-2022-29191", - "id": "pyup.io-57214", - "more_info_path": "/vulnerabilities/CVE-2022-29191/57214", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27775.", + "cve": "CVE-2022-27775", + "id": "pyup.io-57181", + "more_info_path": "/vulnerabilities/CVE-2022-27775/57181", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67563,10 +67954,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27776.", - "cve": "CVE-2022-27776", - "id": "pyup.io-57184", - "more_info_path": "/vulnerabilities/CVE-2022-27776/57184", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29203: Integer overflow in 'SpaceToBatchND'.", + "cve": "CVE-2022-29203", + "id": "pyup.io-57200", + "more_info_path": "/vulnerabilities/CVE-2022-29203/57200", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67576,10 +67967,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29216: Code injection in 'saved_model_cli'.", - "cve": "CVE-2022-29216", - "id": "pyup.io-57182", - "more_info_path": "/vulnerabilities/CVE-2022-29216/57182", + "advisory": "Intel-tensorflow-avx512 versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 update 'curl' to v7.83.1 to handle CVE-2022-27776.", + "cve": "CVE-2022-27776", + "id": "pyup.io-57184", + "more_info_path": "/vulnerabilities/CVE-2022-27776/57184", "specs": [ "<2.6.4", ">=2.7.0rc0,<2.7.2", @@ -67614,19 +68005,6 @@ ], "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, - { - "advisory": "Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29213: Crashes stemming from incomplete validation in signal ops.", - "cve": "CVE-2022-29213", - "id": "pyup.io-57210", - "more_info_path": "/vulnerabilities/CVE-2022-29213/57210", - "specs": [ - "<2.6.4", - ">=2.7.0rc0,<2.7.2", - ">=2.8.0rc0,<2.8.1", - ">=2.9.0rc0,<2.9.0" - ], - "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" - }, { "advisory": "Tensorflow versions 2.6.4, 2.7.2, 2.8.1 and 2.9.0 include a fix for CVE-2022-29196: Missing validation which causes denial of service via 'Conv3DBackpropFilterV2'.", "cve": "CVE-2022-29196", @@ -67641,10 +68019,10 @@ "v": "<2.6.4,>=2.7.0rc0,<2.7.2,>=2.8.0rc0,<2.8.1,>=2.9.0rc0,<2.9.0" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35960: 'CHECK' failure in 'TensorListReserve' via missing validation.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-v5xg-3q2c-c2r4", - "cve": "CVE-2022-35960", - "id": "pyup.io-57127", - "more_info_path": "/vulnerabilities/CVE-2022-35960/57127", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35934: 'CHECK' failure in tf.reshape via overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f4w6-h4f5-wx45", + "cve": "CVE-2022-35934", + "id": "pyup.io-57134", + "more_info_path": "/vulnerabilities/CVE-2022-35934/57134", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67653,10 +68031,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35934: 'CHECK' failure in tf.reshape via overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f4w6-h4f5-wx45", - "cve": "CVE-2022-35934", - "id": "pyup.io-57134", - "more_info_path": "/vulnerabilities/CVE-2022-35934/57134", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35988: 'CHECK' fail in 'tf.linalg.matrix_rank'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9vqj-64pv-w55c", + "cve": "CVE-2022-35988", + "id": "pyup.io-57163", + "more_info_path": "/vulnerabilities/CVE-2022-35988/57163", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67665,10 +68043,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35996: Floating point exception in 'Conv2D'. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-q5jv-m6qw-5g37", - "cve": "CVE-2022-35996", - "id": "pyup.io-57139", - "more_info_path": "/vulnerabilities/CVE-2022-35996/57139", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36003: 'CHECK' fail in 'RandomPoissonV2'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cv2p-32v3-vhwq", + "cve": "CVE-2022-36003", + "id": "pyup.io-57131", + "more_info_path": "/vulnerabilities/CVE-2022-36003/57131", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67677,10 +68055,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35959: 'CHECK' failures in 'AvgPool3DGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-wxjj-cgcx-r3vq", - "cve": "CVE-2022-35959", - "id": "pyup.io-57141", - "more_info_path": "/vulnerabilities/CVE-2022-35959/57141", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35985: 'CHECK' fail in 'LRNGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9942-r22v-78cp", + "cve": "CVE-2022-35985", + "id": "pyup.io-57132", + "more_info_path": "/vulnerabilities/CVE-2022-35985/57132", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67689,10 +68067,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35973: Segfault in 'QuantizedMatMul'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-689c-r7h2-fv9v", - "cve": "CVE-2022-35973", - "id": "pyup.io-57151", - "more_info_path": "/vulnerabilities/CVE-2022-35973/57151", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35983: 'CHECK' fail in 'Save' and 'SaveSlices'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m6vp-8q9j-whx4", + "cve": "CVE-2022-35983", + "id": "pyup.io-57137", + "more_info_path": "/vulnerabilities/CVE-2022-35983/57137", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67701,10 +68079,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35988: 'CHECK' fail in 'tf.linalg.matrix_rank'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9vqj-64pv-w55c", - "cve": "CVE-2022-35988", - "id": "pyup.io-57163", - "more_info_path": "/vulnerabilities/CVE-2022-35988/57163", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35964: Segfault in 'BlockLSTMGradV2'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f7r5-q7cx-h668", + "cve": "CVE-2022-35964", + "id": "pyup.io-57142", + "more_info_path": "/vulnerabilities/CVE-2022-35964/57142", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67713,10 +68091,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35940: Int overflow in 'RaggedRangeOp'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x989-q2pq-4q5x", - "cve": "CVE-2022-35940", - "id": "pyup.io-57170", - "more_info_path": "/vulnerabilities/CVE-2022-35940/57170", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35982: Segfault in 'SparseBincount'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-397c-5g2j-qxpv", + "cve": "CVE-2022-35982", + "id": "pyup.io-57157", + "more_info_path": "/vulnerabilities/CVE-2022-35982/57157", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67725,10 +68103,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36015: Integer overflow in math ops. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rh87-q4vg-m45j", - "cve": "CVE-2022-36015", - "id": "pyup.io-57130", - "more_info_path": "/vulnerabilities/CVE-2022-36015/57130", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35967: Segfault in 'QuantizedAdd'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-v6h3-348g-6h5x", + "cve": "CVE-2022-35967", + "id": "pyup.io-57166", + "more_info_path": "/vulnerabilities/CVE-2022-35967/57166", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67737,10 +68115,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36017: Segfault in 'Requantize'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-wqmc-pm8c-2jhc", - "cve": "CVE-2022-36017", - "id": "pyup.io-57129", - "more_info_path": "/vulnerabilities/CVE-2022-36017/57129", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35939: OOB write in 'scatter_nd' op in TF Lite.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-ffjm-4qwc-7cmf", + "cve": "CVE-2022-35939", + "id": "pyup.io-57125", + "more_info_path": "/vulnerabilities/CVE-2022-35939/57125", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67749,10 +68127,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36003: 'CHECK' fail in 'RandomPoissonV2'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cv2p-32v3-vhwq", - "cve": "CVE-2022-36003", - "id": "pyup.io-57131", - "more_info_path": "/vulnerabilities/CVE-2022-36003/57131", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35970: Segfault in 'QuantizedInstanceNorm'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g35r-369w-3fqp", + "cve": "CVE-2022-35970", + "id": "pyup.io-57168", + "more_info_path": "/vulnerabilities/CVE-2022-35970/57168", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67761,10 +68139,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35985: 'CHECK' fail in 'LRNGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9942-r22v-78cp", - "cve": "CVE-2022-35985", - "id": "pyup.io-57132", - "more_info_path": "/vulnerabilities/CVE-2022-35985/57132", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35941: 'CHECK' failure in 'AvgPoolOp'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mgmh-g2v6-mqw5", + "cve": "CVE-2022-35941", + "id": "pyup.io-57161", + "more_info_path": "/vulnerabilities/CVE-2022-35941/57161", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67773,10 +68151,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35969: 'CHECK' fail in 'Conv2DBackpropInput'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-q2c3-jpmc-gfjx", - "cve": "CVE-2022-35969", - "id": "pyup.io-57133", - "more_info_path": "/vulnerabilities/CVE-2022-35969/57133", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35986: Segfault in 'RaggedBincount'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-wr9v-g9vf-c74v", + "cve": "CVE-2022-35986", + "id": "pyup.io-57149", + "more_info_path": "/vulnerabilities/CVE-2022-35986/57149", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67785,10 +68163,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36002: 'CHECK' fail in 'Unbatch'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mh3m-62v7-68xg", - "cve": "CVE-2022-36002", - "id": "pyup.io-57135", - "more_info_path": "/vulnerabilities/CVE-2022-36002/57135", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36017: Segfault in 'Requantize'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-wqmc-pm8c-2jhc", + "cve": "CVE-2022-36017", + "id": "pyup.io-57129", + "more_info_path": "/vulnerabilities/CVE-2022-36017/57129", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67797,10 +68175,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35983: 'CHECK' fail in 'Save' and 'SaveSlices'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m6vp-8q9j-whx4", - "cve": "CVE-2022-35983", - "id": "pyup.io-57137", - "more_info_path": "/vulnerabilities/CVE-2022-35983/57137", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36016: 'CHECK'-fail in 'tensorflow::full_type::SubstituteFromAttrs'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g468-qj8g-vcjc", + "cve": "CVE-2022-36016", + "id": "pyup.io-57162", + "more_info_path": "/vulnerabilities/CVE-2022-36016/57162", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67809,10 +68187,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35984: 'CHECK' fail in 'ParameterizedTruncatedNormal'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2xf-8hgm-hpw5", - "cve": "CVE-2022-35984", - "id": "pyup.io-57138", - "more_info_path": "/vulnerabilities/CVE-2022-35984/57138", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36015: Integer overflow in math ops. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rh87-q4vg-m45j", + "cve": "CVE-2022-36015", + "id": "pyup.io-57130", + "more_info_path": "/vulnerabilities/CVE-2022-36015/57130", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67821,10 +68199,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35964: Segfault in 'BlockLSTMGradV2'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f7r5-q7cx-h668", - "cve": "CVE-2022-35964", - "id": "pyup.io-57142", - "more_info_path": "/vulnerabilities/CVE-2022-35964/57142", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36012: Assertion fail on MLIR empty edge names.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-jvhc-5hhr-w3v5", + "cve": "CVE-2022-36012", + "id": "pyup.io-57136", + "more_info_path": "/vulnerabilities/CVE-2022-36012/57136", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67833,10 +68211,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35997: 'CHECK' fail in 'tf.sparse.cross'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-p7hr-f446-x6qf", - "cve": "CVE-2022-35997", - "id": "pyup.io-57143", - "more_info_path": "/vulnerabilities/CVE-2022-35997/57143", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36011: Null dereference on MLIR on empty function attributes.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fv43-93gv-vm8f", + "cve": "CVE-2022-36011", + "id": "pyup.io-57155", + "more_info_path": "/vulnerabilities/CVE-2022-36011/57155", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67845,10 +68223,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35998: 'CHECK' fail in 'EmptyTensorList'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhw4-wwr7-gjc5", - "cve": "CVE-2022-35998", - "id": "pyup.io-57144", - "more_info_path": "/vulnerabilities/CVE-2022-35998/57144", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36005: 'CHECK' fail in 'FakeQuantWithMinMaxVarsGradient'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-r26c-679w-mrjm", + "cve": "CVE-2022-36005", + "id": "pyup.io-57179", + "more_info_path": "/vulnerabilities/CVE-2022-36005/57179", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67857,10 +68235,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35981: 'CHECK' fail in 'FractionalMaxPoolGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vxv8-r8q2-63xw", - "cve": "CVE-2022-35981", - "id": "pyup.io-57146", - "more_info_path": "/vulnerabilities/CVE-2022-35981/57146", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36002: 'CHECK' fail in 'Unbatch'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mh3m-62v7-68xg", + "cve": "CVE-2022-36002", + "id": "pyup.io-57135", + "more_info_path": "/vulnerabilities/CVE-2022-36002/57135", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67869,10 +68247,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35992: 'CHECK' fail in 'TensorListFromTensor'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9v8w-xmr4-wgxp", - "cve": "CVE-2022-35992", - "id": "pyup.io-57148", - "more_info_path": "/vulnerabilities/CVE-2022-35992/57148", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36001: 'CHECK' fail in 'DrawBoundingBoxes'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-jqm7-m5q7-3hm5", + "cve": "CVE-2022-36001", + "id": "pyup.io-57171", + "more_info_path": "/vulnerabilities/CVE-2022-36001/57171", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67881,10 +68259,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35974: Segfault in 'QuantizeDownAndShrinkRange'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vgvh-2pf4-jr2x", - "cve": "CVE-2022-35974", - "id": "pyup.io-57150", - "more_info_path": "/vulnerabilities/CVE-2022-35974/57150", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35999: 'CHECK' fail in 'Conv2DBackpropInput'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-37jf-mjv6-xfqw", + "cve": "CVE-2022-35999", + "id": "pyup.io-57173", + "more_info_path": "/vulnerabilities/CVE-2022-35999/57173", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67893,10 +68271,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36026: 'CHECK' fail in 'QuantizeAndDequantizeV3'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9cr2-8pwr-fhfq", - "cve": "CVE-2022-36026", - "id": "pyup.io-57153", - "more_info_path": "/vulnerabilities/CVE-2022-36026/57153", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35998: 'CHECK' fail in 'EmptyTensorList'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhw4-wwr7-gjc5", + "cve": "CVE-2022-35998", + "id": "pyup.io-57144", + "more_info_path": "/vulnerabilities/CVE-2022-35998/57144", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67905,10 +68283,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35987: 'CHECK' fail in 'DenseBincount'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-w62h-8xjm-fv49", - "cve": "CVE-2022-35987", - "id": "pyup.io-57152", - "more_info_path": "/vulnerabilities/CVE-2022-35987/57152", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35997: 'CHECK' fail in 'tf.sparse.cross'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-p7hr-f446-x6qf", + "cve": "CVE-2022-35997", + "id": "pyup.io-57143", + "more_info_path": "/vulnerabilities/CVE-2022-35997/57143", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67917,10 +68295,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36011: Null dereference on MLIR on empty function attributes.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fv43-93gv-vm8f", - "cve": "CVE-2022-36011", - "id": "pyup.io-57155", - "more_info_path": "/vulnerabilities/CVE-2022-36011/57155", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35996: Floating point exception in 'Conv2D'. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-q5jv-m6qw-5g37", + "cve": "CVE-2022-35996", + "id": "pyup.io-57139", + "more_info_path": "/vulnerabilities/CVE-2022-35996/57139", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67929,10 +68307,22 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35982: Segfault in 'SparseBincount'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-397c-5g2j-qxpv", - "cve": "CVE-2022-35982", - "id": "pyup.io-57157", - "more_info_path": "/vulnerabilities/CVE-2022-35982/57157", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35995: 'CHECK' fail in 'AudioSummaryV2'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g9h5-vr8m-x2h4", + "cve": "CVE-2022-35995", + "id": "pyup.io-57174", + "more_info_path": "/vulnerabilities/CVE-2022-35995/57174", + "specs": [ + "<2.7.4", + ">=2.8.0rc0,<2.8.3", + ">=2.9.0rc0,<2.9.2" + ], + "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" + }, + { + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35994: 'CHECK' fail in 'CollectiveGather'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fhfc-2q7x-929f", + "cve": "CVE-2022-35994", + "id": "pyup.io-57169", + "more_info_path": "/vulnerabilities/CVE-2022-35994/57169", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67953,10 +68343,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35965: Segfault in 'LowerBound' and 'UpperBound'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qxpx-j395-pw36", - "cve": "CVE-2022-35965", - "id": "pyup.io-57164", - "more_info_path": "/vulnerabilities/CVE-2022-35965/57164", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35992: 'CHECK' fail in 'TensorListFromTensor'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9v8w-xmr4-wgxp", + "cve": "CVE-2022-35992", + "id": "pyup.io-57148", + "more_info_path": "/vulnerabilities/CVE-2022-35992/57148", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67965,10 +68355,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35967: Segfault in 'QuantizedAdd'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-v6h3-348g-6h5x", - "cve": "CVE-2022-35967", - "id": "pyup.io-57166", - "more_info_path": "/vulnerabilities/CVE-2022-35967/57166", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36026: 'CHECK' fail in 'QuantizeAndDequantizeV3'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9cr2-8pwr-fhfq", + "cve": "CVE-2022-36026", + "id": "pyup.io-57153", + "more_info_path": "/vulnerabilities/CVE-2022-36026/57153", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67977,10 +68367,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35963: 'CHECK' failures in 'FractionalAvgPoolGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-84jm-4cf3-9jfm", - "cve": "CVE-2022-35963", - "id": "pyup.io-57172", - "more_info_path": "/vulnerabilities/CVE-2022-35963/57172", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36018: 'CHECK' fail in 'RaggedTensorToVariant'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m6cv-4fmf-66xf", + "cve": "CVE-2022-36018", + "id": "pyup.io-57177", + "more_info_path": "/vulnerabilities/CVE-2022-36018/57177", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -67989,10 +68379,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36001: 'CHECK' fail in 'DrawBoundingBoxes'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-jqm7-m5q7-3hm5", - "cve": "CVE-2022-36001", - "id": "pyup.io-57171", - "more_info_path": "/vulnerabilities/CVE-2022-36001/57171", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35990: 'CHECK' fail in 'FakeQuantWithMinMaxVarsPerChannelGradient'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h7ff-cfc9-wmmh", + "cve": "CVE-2022-35990", + "id": "pyup.io-57140", + "more_info_path": "/vulnerabilities/CVE-2022-35990/57140", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68001,10 +68391,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35995: 'CHECK' fail in 'AudioSummaryV2'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g9h5-vr8m-x2h4", - "cve": "CVE-2022-35995", - "id": "pyup.io-57174", - "more_info_path": "/vulnerabilities/CVE-2022-35995/57174", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35989: 'CHECK' fail in 'MaxPool'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-j43h-pgmg-5hjq", + "cve": "CVE-2022-35989", + "id": "pyup.io-57178", + "more_info_path": "/vulnerabilities/CVE-2022-35989/57178", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68013,10 +68403,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35999: 'CHECK' fail in 'Conv2DBackpropInput'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-37jf-mjv6-xfqw", - "cve": "CVE-2022-35999", - "id": "pyup.io-57173", - "more_info_path": "/vulnerabilities/CVE-2022-35999/57173", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35987: 'CHECK' fail in 'DenseBincount'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-w62h-8xjm-fv49", + "cve": "CVE-2022-35987", + "id": "pyup.io-57152", + "more_info_path": "/vulnerabilities/CVE-2022-35987/57152", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68025,10 +68415,22 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36005: 'CHECK' fail in 'FakeQuantWithMinMaxVarsGradient'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-r26c-679w-mrjm", - "cve": "CVE-2022-36005", - "id": "pyup.io-57179", - "more_info_path": "/vulnerabilities/CVE-2022-36005/57179", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35984: 'CHECK' fail in 'ParameterizedTruncatedNormal'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2xf-8hgm-hpw5", + "cve": "CVE-2022-35984", + "id": "pyup.io-57138", + "more_info_path": "/vulnerabilities/CVE-2022-35984/57138", + "specs": [ + "<2.7.4", + ">=2.8.0rc0,<2.8.3", + ">=2.9.0rc0,<2.9.2" + ], + "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" + }, + { + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35981: 'CHECK' fail in 'FractionalMaxPoolGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vxv8-r8q2-63xw", + "cve": "CVE-2022-35981", + "id": "pyup.io-57146", + "more_info_path": "/vulnerabilities/CVE-2022-35981/57146", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68049,10 +68451,22 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35990: 'CHECK' fail in 'FakeQuantWithMinMaxVarsPerChannelGradient'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h7ff-cfc9-wmmh", - "cve": "CVE-2022-35990", - "id": "pyup.io-57140", - "more_info_path": "/vulnerabilities/CVE-2022-35990/57140", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35974: Segfault in 'QuantizeDownAndShrinkRange'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vgvh-2pf4-jr2x", + "cve": "CVE-2022-35974", + "id": "pyup.io-57150", + "more_info_path": "/vulnerabilities/CVE-2022-35974/57150", + "specs": [ + "<2.7.4", + ">=2.8.0rc0,<2.8.3", + ">=2.9.0rc0,<2.9.2" + ], + "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" + }, + { + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35973: Segfault in 'QuantizedMatMul'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-689c-r7h2-fv9v", + "cve": "CVE-2022-35973", + "id": "pyup.io-57151", + "more_info_path": "/vulnerabilities/CVE-2022-35973/57151", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68073,10 +68487,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35939: OOB write in 'scatter_nd' op in TF Lite.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-ffjm-4qwc-7cmf", - "cve": "CVE-2022-35939", - "id": "pyup.io-57125", - "more_info_path": "/vulnerabilities/CVE-2022-35939/57125", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36000: 'CHECK' fail in 'Eig'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fqxc-pvf8-2w9v", + "cve": "CVE-2022-36000", + "id": "pyup.io-57147", + "more_info_path": "/vulnerabilities/CVE-2022-36000/57147", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68085,10 +68499,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35970: Segfault in 'QuantizedInstanceNorm'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g35r-369w-3fqp", - "cve": "CVE-2022-35970", - "id": "pyup.io-57168", - "more_info_path": "/vulnerabilities/CVE-2022-35970/57168", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36004: 'CHECK' fail in 'tf.random.gamma'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv8m-8x97-937q", + "cve": "CVE-2022-36004", + "id": "pyup.io-57176", + "more_info_path": "/vulnerabilities/CVE-2022-36004/57176", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68097,10 +68511,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35941: 'CHECK' failure in 'AvgPoolOp'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mgmh-g2v6-mqw5", - "cve": "CVE-2022-35941", - "id": "pyup.io-57161", - "more_info_path": "/vulnerabilities/CVE-2022-35941/57161", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35968: 'CHECK' fail in 'AvgPoolGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-2475-53vw-vp25", + "cve": "CVE-2022-35968", + "id": "pyup.io-57167", + "more_info_path": "/vulnerabilities/CVE-2022-35968/57167", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68109,10 +68523,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35966: Segfault in 'QuantizedAvgPool'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4w68-4x85-mjj9", - "cve": "CVE-2022-35966", - "id": "pyup.io-57154", - "more_info_path": "/vulnerabilities/CVE-2022-35966/57154", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35969: 'CHECK' fail in 'Conv2DBackpropInput'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-q2c3-jpmc-gfjx", + "cve": "CVE-2022-35969", + "id": "pyup.io-57133", + "more_info_path": "/vulnerabilities/CVE-2022-35969/57133", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68121,10 +68535,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35986: Segfault in 'RaggedBincount'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-wr9v-g9vf-c74v", - "cve": "CVE-2022-35986", - "id": "pyup.io-57149", - "more_info_path": "/vulnerabilities/CVE-2022-35986/57149", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35966: Segfault in 'QuantizedAvgPool'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-4w68-4x85-mjj9", + "cve": "CVE-2022-35966", + "id": "pyup.io-57154", + "more_info_path": "/vulnerabilities/CVE-2022-35966/57154", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68133,10 +68547,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36000: 'CHECK' fail in 'Eig'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fqxc-pvf8-2w9v", - "cve": "CVE-2022-36000", - "id": "pyup.io-57147", - "more_info_path": "/vulnerabilities/CVE-2022-36000/57147", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35965: Segfault in 'LowerBound' and 'UpperBound'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-qxpx-j395-pw36", + "cve": "CVE-2022-35965", + "id": "pyup.io-57164", + "more_info_path": "/vulnerabilities/CVE-2022-35965/57164", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68145,10 +68559,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35968: 'CHECK' fail in 'AvgPoolGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-2475-53vw-vp25", - "cve": "CVE-2022-35968", - "id": "pyup.io-57167", - "more_info_path": "/vulnerabilities/CVE-2022-35968/57167", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35963: 'CHECK' failures in 'FractionalAvgPoolGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-84jm-4cf3-9jfm", + "cve": "CVE-2022-35963", + "id": "pyup.io-57172", + "more_info_path": "/vulnerabilities/CVE-2022-35963/57172", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68157,10 +68571,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36004: 'CHECK' fail in 'tf.random.gamma'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv8m-8x97-937q", - "cve": "CVE-2022-36004", - "id": "pyup.io-57176", - "more_info_path": "/vulnerabilities/CVE-2022-36004/57176", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35960: 'CHECK' failure in 'TensorListReserve' via missing validation.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-v5xg-3q2c-c2r4", + "cve": "CVE-2022-35960", + "id": "pyup.io-57127", + "more_info_path": "/vulnerabilities/CVE-2022-35960/57127", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68169,10 +68583,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35989: 'CHECK' fail in 'MaxPool'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-j43h-pgmg-5hjq", - "cve": "CVE-2022-35989", - "id": "pyup.io-57178", - "more_info_path": "/vulnerabilities/CVE-2022-35989/57178", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35959: 'CHECK' failures in 'AvgPool3DGrad'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-wxjj-cgcx-r3vq", + "cve": "CVE-2022-35959", + "id": "pyup.io-57141", + "more_info_path": "/vulnerabilities/CVE-2022-35959/57141", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68193,10 +68607,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36027: Segfault TFLite converter on per-channel quantized transposed convolutions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-79h2-q768-fpxr", - "cve": "CVE-2022-36027", - "id": "pyup.io-57156", - "more_info_path": "/vulnerabilities/CVE-2022-36027/57156", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35940: Int overflow in 'RaggedRangeOp'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x989-q2pq-4q5x", + "cve": "CVE-2022-35940", + "id": "pyup.io-57170", + "more_info_path": "/vulnerabilities/CVE-2022-35940/57170", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68205,10 +68619,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36016: 'CHECK'-fail in 'tensorflow::full_type::SubstituteFromAttrs'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g468-qj8g-vcjc", - "cve": "CVE-2022-36016", - "id": "pyup.io-57162", - "more_info_path": "/vulnerabilities/CVE-2022-36016/57162", + "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36027: Segfault TFLite converter on per-channel quantized transposed convolutions.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-79h2-q768-fpxr", + "cve": "CVE-2022-36027", + "id": "pyup.io-57156", + "more_info_path": "/vulnerabilities/CVE-2022-36027/57156", "specs": [ "<2.7.4", ">=2.8.0rc0,<2.8.3", @@ -68240,18 +68654,6 @@ ], "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, - { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35994: 'CHECK' fail in 'CollectiveGather'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-fhfc-2q7x-929f", - "cve": "CVE-2022-35994", - "id": "pyup.io-57169", - "more_info_path": "/vulnerabilities/CVE-2022-35994/57169", - "specs": [ - "<2.7.4", - ">=2.8.0rc0,<2.8.3", - ">=2.9.0rc0,<2.9.2" - ], - "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" - }, { "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-35971: 'CHECK' fail in 'FakeQuantWithMinMaxVars'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9fpg-838v-wpv7", "cve": "CVE-2022-35971", @@ -68264,30 +68666,6 @@ ], "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, - { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36018: 'CHECK' fail in 'RaggedTensorToVariant'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-m6cv-4fmf-66xf", - "cve": "CVE-2022-36018", - "id": "pyup.io-57177", - "more_info_path": "/vulnerabilities/CVE-2022-36018/57177", - "specs": [ - "<2.7.4", - ">=2.8.0rc0,<2.8.3", - ">=2.9.0rc0,<2.9.2" - ], - "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" - }, - { - "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36012: Assertion fail on MLIR empty edge names.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-jvhc-5hhr-w3v5", - "cve": "CVE-2022-36012", - "id": "pyup.io-57136", - "more_info_path": "/vulnerabilities/CVE-2022-36012/57136", - "specs": [ - "<2.7.4", - ">=2.8.0rc0,<2.8.3", - ">=2.9.0rc0,<2.9.2" - ], - "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" - }, { "advisory": "Intel-tensorflow-avx512 2.7.4, 2.8.3 and 2.9.2 include a fix for CVE-2022-36014: Null-dereference in 'mlir::tfg::TFOp::nameAttr'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-7j3m-8g3c-9qqq", "cve": "CVE-2022-36014", @@ -68313,10 +68691,10 @@ "v": "<2.7.4,>=2.8.0rc0,<2.8.3,>=2.9.0rc0,<2.9.2" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41907: When 'tf.raw_ops.ResizeNearestNeighborGrad' is given a large 'size' input, it overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-368v-7v32-52fx", - "cve": "CVE-2022-41907", - "id": "pyup.io-57112", - "more_info_path": "/vulnerabilities/CVE-2022-41907/57112", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41910: The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-frqp-wp83-qggv", + "cve": "CVE-2022-41910", + "id": "pyup.io-57118", + "more_info_path": "/vulnerabilities/CVE-2022-41910/57118", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68325,10 +68703,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41897: If 'FractionMaxPoolGrad' is given outsize inputs 'row_pooling_sequence' and 'col_pooling_sequence', TensorFlow will crash.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2w8-jw48-fr7j", - "cve": "CVE-2022-41897", - "id": "pyup.io-57116", - "more_info_path": "/vulnerabilities/CVE-2022-41897/57116", + "advisory": "TensorFlow is an open source platform for machine learning. The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered. We have patched the issue in GitHub commit a65411a1d69edfb16b25907ffb8f73556ce36bb7. The fix will be included in TensorFlow 2.11.0. We will also cherrypick this commit on TensorFlow 2.8.4, 2.9.3, and 2.10.1.", + "cve": "CVE-2022-41902", + "id": "pyup.io-57113", + "more_info_path": "/vulnerabilities/CVE-2022-41902/57113", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68337,10 +68715,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41880: When the 'BaseCandidateSamplerOp' function receives a value in 'true_classes' larger than 'range_max', a heap oob read occurs.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-8w5g-3wcv-9g2j", - "cve": "CVE-2022-41880", - "id": "pyup.io-57111", - "more_info_path": "/vulnerabilities/CVE-2022-41880/57111", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41908: TensorFlow is an open source platform for machine learning. An input 'token' that is not a UTF-8 bytestring will trigger a 'CHECK' fail in 'tf.raw_ops.PyFunc'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv77-9g28-cwg3", + "cve": "CVE-2022-41908", + "id": "pyup.io-57108", + "more_info_path": "/vulnerabilities/CVE-2022-41908/57108", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68349,10 +68727,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41900: The security vulnerability results in FractionalMax(AVG)Pool with illegal pooling_ratio. Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or remote code execution.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvwp-h6jv-7472", - "cve": "CVE-2022-41900", - "id": "pyup.io-57109", - "more_info_path": "/vulnerabilities/CVE-2022-41900/57109", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41907: When 'tf.raw_ops.ResizeNearestNeighborGrad' is given a large 'size' input, it overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-368v-7v32-52fx", + "cve": "CVE-2022-41907", + "id": "pyup.io-57112", + "more_info_path": "/vulnerabilities/CVE-2022-41907/57112", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68361,10 +68739,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41895: If 'MirrorPadGrad' is given outsize input 'paddings', TensorFlow will give a heap OOB error.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gq2j-cr96-gvqx", - "cve": "CVE-2022-41895", - "id": "pyup.io-57100", - "more_info_path": "/vulnerabilities/CVE-2022-41895/57100", + "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41901: An input 'sparse_matrix' that is not a matrix with a shape with rank 0 will trigger a 'CHECK' fail in 'tf.raw_ops.SparseMatrixNNZ'.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g9fm-r5mm-rf9f", + "cve": "CVE-2022-41901", + "id": "pyup.io-57099", + "more_info_path": "/vulnerabilities/CVE-2022-41901/57099", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68373,10 +68751,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41885: When 'tf.raw_ops.FusedResizeAndPadConv2D' is given a large tensor shape, it overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-762h-vpvw-3rcx", - "cve": "CVE-2022-41885", - "id": "pyup.io-57104", - "more_info_path": "/vulnerabilities/CVE-2022-41885/57104", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41900: The security vulnerability results in FractionalMax(AVG)Pool with illegal pooling_ratio. Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or remote code execution.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvwp-h6jv-7472", + "cve": "CVE-2022-41900", + "id": "pyup.io-57109", + "more_info_path": "/vulnerabilities/CVE-2022-41900/57109", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68385,10 +68763,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41910: The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-frqp-wp83-qggv", - "cve": "CVE-2022-41910", - "id": "pyup.io-57118", - "more_info_path": "/vulnerabilities/CVE-2022-41910/57118", + "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41899: TensorFlow is an open source platform for machine learning. Inputs 'dense_features' or 'example_state_data' not of rank 2 will trigger a 'CHECK' fail in 'SdcaOptimizer'.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-27rc-728f-x5w2", + "cve": "CVE-2022-41899", + "id": "pyup.io-57105", + "more_info_path": "/vulnerabilities/CVE-2022-41899/57105", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68397,10 +68775,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41884: If a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-jq6x-99hj-q636", - "cve": "CVE-2022-41884", - "id": "pyup.io-57115", - "more_info_path": "/vulnerabilities/CVE-2022-41884/57115", + "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41898: If 'SparseFillEmptyRowsGrad' is given empty inputs, TensorFlow will crash.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-hq7g-wwwp-q46h", + "cve": "CVE-2022-41898", + "id": "pyup.io-57120", + "more_info_path": "/vulnerabilities/CVE-2022-41898/57120", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68409,10 +68787,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "TensorFlow is an open source platform for machine learning. The function MakeGrapplerFunctionItem takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered. We have patched the issue in GitHub commit a65411a1d69edfb16b25907ffb8f73556ce36bb7. The fix will be included in TensorFlow 2.11.0. We will also cherrypick this commit on TensorFlow 2.8.4, 2.9.3, and 2.10.1.", - "cve": "CVE-2022-41902", - "id": "pyup.io-57113", - "more_info_path": "/vulnerabilities/CVE-2022-41902/57113", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41897: If 'FractionMaxPoolGrad' is given outsize inputs 'row_pooling_sequence' and 'col_pooling_sequence', TensorFlow will crash.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2w8-jw48-fr7j", + "cve": "CVE-2022-41897", + "id": "pyup.io-57116", + "more_info_path": "/vulnerabilities/CVE-2022-41897/57116", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68421,10 +68799,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41908: TensorFlow is an open source platform for machine learning. An input 'token' that is not a UTF-8 bytestring will trigger a 'CHECK' fail in 'tf.raw_ops.PyFunc'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv77-9g28-cwg3", - "cve": "CVE-2022-41908", - "id": "pyup.io-57108", - "more_info_path": "/vulnerabilities/CVE-2022-41908/57108", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41895: If 'MirrorPadGrad' is given outsize input 'paddings', TensorFlow will give a heap OOB error.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gq2j-cr96-gvqx", + "cve": "CVE-2022-41895", + "id": "pyup.io-57100", + "more_info_path": "/vulnerabilities/CVE-2022-41895/57100", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68433,10 +68811,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41898: If 'SparseFillEmptyRowsGrad' is given empty inputs, TensorFlow will crash.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-hq7g-wwwp-q46h", - "cve": "CVE-2022-41898", - "id": "pyup.io-57120", - "more_info_path": "/vulnerabilities/CVE-2022-41898/57120", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41894: The reference kernel of the 'CONV_3D_TRANSPOSE' TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of 'data_ptr += num_channels;' it should be 'data_ptr += output_num_channels;' as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6q3-vv32-2cq5", + "cve": "CVE-2022-41894", + "id": "pyup.io-57121", + "more_info_path": "/vulnerabilities/CVE-2022-41894/57121", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68445,10 +68823,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41890: If 'BCast::ToShape' is given input larger than an 'int32', it will crash, despite being supposed to handle up to an 'int64'. An example can be seen in 'tf.experimental.numpy.outer' by passing in large input to the input 'b'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h246-cgh4-7475", - "cve": "CVE-2022-41890", - "id": "pyup.io-57110", - "more_info_path": "/vulnerabilities/CVE-2022-41890/57110", + "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41891: If 'tf.raw_ops.TensorListConcat' is given 'element_shape=[]', it results segmentation fault which can be used to trigger a denial of service attack.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-66vq-54fq-6jvv", + "cve": "CVE-2022-41891", + "id": "pyup.io-57101", + "more_info_path": "/vulnerabilities/CVE-2022-41891/57101", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68457,10 +68835,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41886: When 'tf.raw_ops.ImageProjectiveTransformV2' is given a large output shape, it overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-54pp-c6pp-7fpx", - "cve": "CVE-2022-41886", - "id": "pyup.io-57117", - "more_info_path": "/vulnerabilities/CVE-2022-41886/57117", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41889: If a list of quantized tensors is assigned to an attribute, the pywrap code fails to parse the tensor and returns a 'nullptr', which is not caught. An example can be seen in 'tf.compat.v1.extract_volume_patches' by passing in quantized tensors as input 'ksizes'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xxcj-rhqg-m46g", + "cve": "CVE-2022-41889", + "id": "pyup.io-57103", + "more_info_path": "/vulnerabilities/CVE-2022-41889/57103", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68481,10 +68859,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41909: An input 'encoded' that is not a valid 'CompositeTensorVariant' tensor will trigger a segfault in 'tf.raw_ops.CompositeTensorVariantToComponents'.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjx6-v474-2ch9", - "cve": "CVE-2022-41909", - "id": "pyup.io-57107", - "more_info_path": "/vulnerabilities/CVE-2022-41909/57107", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41885: When 'tf.raw_ops.FusedResizeAndPadConv2D' is given a large tensor shape, it overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-762h-vpvw-3rcx", + "cve": "CVE-2022-41885", + "id": "pyup.io-57104", + "more_info_path": "/vulnerabilities/CVE-2022-41885/57104", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68493,10 +68871,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41894: The reference kernel of the 'CONV_3D_TRANSPOSE' TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of 'data_ptr += num_channels;' it should be 'data_ptr += output_num_channels;' as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6q3-vv32-2cq5", - "cve": "CVE-2022-41894", - "id": "pyup.io-57121", - "more_info_path": "/vulnerabilities/CVE-2022-41894/57121", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41884: If a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-jq6x-99hj-q636", + "cve": "CVE-2022-41884", + "id": "pyup.io-57115", + "more_info_path": "/vulnerabilities/CVE-2022-41884/57115", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68505,10 +68883,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41901: An input 'sparse_matrix' that is not a matrix with a shape with rank 0 will trigger a 'CHECK' fail in 'tf.raw_ops.SparseMatrixNNZ'.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g9fm-r5mm-rf9f", - "cve": "CVE-2022-41901", - "id": "pyup.io-57099", - "more_info_path": "/vulnerabilities/CVE-2022-41901/57099", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41880: When the 'BaseCandidateSamplerOp' function receives a value in 'true_classes' larger than 'range_max', a heap oob read occurs.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-8w5g-3wcv-9g2j", + "cve": "CVE-2022-41880", + "id": "pyup.io-57111", + "more_info_path": "/vulnerabilities/CVE-2022-41880/57111", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68517,10 +68895,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41891: If 'tf.raw_ops.TensorListConcat' is given 'element_shape=[]', it results segmentation fault which can be used to trigger a denial of service attack.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-66vq-54fq-6jvv", - "cve": "CVE-2022-41891", - "id": "pyup.io-57101", - "more_info_path": "/vulnerabilities/CVE-2022-41891/57101", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41890: If 'BCast::ToShape' is given input larger than an 'int32', it will crash, despite being supposed to handle up to an 'int64'. An example can be seen in 'tf.experimental.numpy.outer' by passing in large input to the input 'b'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-h246-cgh4-7475", + "cve": "CVE-2022-41890", + "id": "pyup.io-57110", + "more_info_path": "/vulnerabilities/CVE-2022-41890/57110", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68529,10 +68907,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41896: If 'ThreadUnsafeUnigramCandidateSampler' is given input 'filterbank_channel_count' greater than the allowed max size, TensorFlow will crash.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rmg2-f698-wq35", - "cve": "CVE-2022-41896", - "id": "pyup.io-57114", - "more_info_path": "/vulnerabilities/CVE-2022-41896/57114", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41886: When 'tf.raw_ops.ImageProjectiveTransformV2' is given a large output shape, it overflows.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-54pp-c6pp-7fpx", + "cve": "CVE-2022-41886", + "id": "pyup.io-57117", + "more_info_path": "/vulnerabilities/CVE-2022-41886/57117", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68541,10 +68919,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41899: TensorFlow is an open source platform for machine learning. Inputs 'dense_features' or 'example_state_data' not of rank 2 will trigger a 'CHECK' fail in 'SdcaOptimizer'.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-27rc-728f-x5w2", - "cve": "CVE-2022-41899", - "id": "pyup.io-57105", - "more_info_path": "/vulnerabilities/CVE-2022-41899/57105", + "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41909: An input 'encoded' that is not a valid 'CompositeTensorVariant' tensor will trigger a segfault in 'tf.raw_ops.CompositeTensorVariantToComponents'.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjx6-v474-2ch9", + "cve": "CVE-2022-41909", + "id": "pyup.io-57107", + "more_info_path": "/vulnerabilities/CVE-2022-41909/57107", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68553,10 +68931,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41893: If 'tf.raw_ops.TensorListResize' is given a nonscalar value for input 'size', it results 'CHECK' fail which can be used to trigger a denial of service attack.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-67pf-62xr-q35m", - "cve": "CVE-2022-41893", - "id": "pyup.io-57106", - "more_info_path": "/vulnerabilities/CVE-2022-41893/57106", + "advisory": "Tensorflow 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41896: If 'ThreadUnsafeUnigramCandidateSampler' is given input 'filterbank_channel_count' greater than the allowed max size, TensorFlow will crash.\r\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rmg2-f698-wq35", + "cve": "CVE-2022-41896", + "id": "pyup.io-57114", + "more_info_path": "/vulnerabilities/CVE-2022-41896/57114", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68565,10 +68943,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41889: If a list of quantized tensors is assigned to an attribute, the pywrap code fails to parse the tensor and returns a 'nullptr', which is not caught. An example can be seen in 'tf.compat.v1.extract_volume_patches' by passing in quantized tensors as input 'ksizes'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xxcj-rhqg-m46g", - "cve": "CVE-2022-41889", - "id": "pyup.io-57103", - "more_info_path": "/vulnerabilities/CVE-2022-41889/57103", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3 and 2.10.1 include a fix for CVE-2022-41893: If 'tf.raw_ops.TensorListResize' is given a nonscalar value for input 'size', it results 'CHECK' fail which can be used to trigger a denial of service attack.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-67pf-62xr-q35m", + "cve": "CVE-2022-41893", + "id": "pyup.io-57106", + "more_info_path": "/vulnerabilities/CVE-2022-41893/57106", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68589,10 +68967,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3, 2.10.1 and 2.11.0 include a fix for CVE-2022-35935: 'CHECK' failure in 'SobolSample' via missing validation.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-97p7-w86h-vcf9\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqvq-fvhr-v6hc", - "cve": "CVE-2022-35935", - "id": "pyup.io-57122", - "more_info_path": "/vulnerabilities/CVE-2022-35935/57122", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3, 2.10.1 and 2.11.0 include a fix for CVE-2022-35991: 'CHECK' fail in 'TensorListScatter' and 'TensorListScatterV2'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vm7x-4qhj-rrcq\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xf83-q765-xm6m", + "cve": "CVE-2022-35991", + "id": "pyup.io-57123", + "more_info_path": "/vulnerabilities/CVE-2022-35991/57123", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68602,10 +68980,10 @@ "v": "<2.8.4,>=2.9.0rc0,<2.9.3,>=2.10.0rc0,<2.10.1,>=2.11.0rc0,<2.11.0" }, { - "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3, 2.10.1 and 2.11.0 include a fix for CVE-2022-35991: 'CHECK' fail in 'TensorListScatter' and 'TensorListScatterV2'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vm7x-4qhj-rrcq\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-xf83-q765-xm6m", - "cve": "CVE-2022-35991", - "id": "pyup.io-57123", - "more_info_path": "/vulnerabilities/CVE-2022-35991/57123", + "advisory": "Intel-tensorflow-avx512 2.8.4, 2.9.3, 2.10.1 and 2.11.0 include a fix for CVE-2022-35935: 'CHECK' failure in 'SobolSample' via missing validation.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-97p7-w86h-vcf9\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqvq-fvhr-v6hc", + "cve": "CVE-2022-35935", + "id": "pyup.io-57122", + "more_info_path": "/vulnerabilities/CVE-2022-35935/57122", "specs": [ "<2.8.4", ">=2.9.0rc0,<2.9.3", @@ -68663,28 +69041,6 @@ ], "v": ">=2.10.0rc0,<2.10.1" }, - { - "advisory": "Intel-tensorflow-avx512 versions 2.2.1 and 2.3.1 include a fix for CVE-2020-15191: In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to 'dlpack.to_dlpack' the expected validations will cause variables to bind to 'nullptr' while setting a 'status' variable to the error condition. However, this 'status' argument is not properly checked. Hence, code following these methods will bind references to null pointers. This is undefined behavior and reported as an error if compiling with '-fsanitize=null'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-q8qj-fc9q-cphr", - "cve": "CVE-2020-15191", - "id": "pyup.io-57520", - "more_info_path": "/vulnerabilities/CVE-2020-15191/57520", - "specs": [ - ">=2.2.0rc0,<2.2.1", - ">=2.3.0rc0,<2.3.1" - ], - "v": ">=2.2.0rc0,<2.2.1,>=2.3.0rc0,<2.3.1" - }, - { - "advisory": "Intel-tensorflow-avx512 versions 2.2.1 and 2.3.1 includes a fix for CVE-2020-15213: In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimension of the output tensor, attackers can use a very large value to trigger a large allocation. The issue was patched in commit 204945b19e44b57906c9344c0d00120eeeae178a. A potential workaround is to add a custom \"Verifier\" to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87", - "cve": "CVE-2020-15213", - "id": "pyup.io-57521", - "more_info_path": "/vulnerabilities/CVE-2020-15213/57521", - "specs": [ - ">=2.2.0rc0,<2.2.1", - ">=2.3.0rc0,<2.3.1" - ], - "v": ">=2.2.0rc0,<2.2.1,>=2.3.0rc0,<2.3.1" - }, { "advisory": "Intel-tensorflow-avx512 versions 2.2.1 and 2.3.1 include a fix for CVE-2020-15193: In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of \"dlpack.to_dlpack\" can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a \"reinterpret_cast\". Since the \"PyObject\" is a Python object, not a Tensorflow tensor, the cast to \"EagerTensor\" fails. The issue was patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjjg-hgv6-h69v", "cve": "CVE-2020-15193", @@ -68730,14 +69086,26 @@ "v": ">=2.2.0rc0,<2.2.1,>=2.3.0rc0,<2.3.1" }, { - "advisory": "Intel-tensorflow-avx512 2.3.1 includes a fix for CVE-2020-15198: In Tensorflow before version 2.3.1, the \"SparseCountSparseOutput\" implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the \"indices\" tensor has the same shape as the \"values\" one. The values in these tensors are always accessed in parallel. Thus, a shape mismatch can result in accesses outside the bounds of heap allocated buffers. The issue was patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-jc87-6vpp-7ff3", - "cve": "CVE-2020-15198", - "id": "pyup.io-57515", - "more_info_path": "/vulnerabilities/CVE-2020-15198/57515", + "advisory": "Intel-tensorflow-avx512 versions 2.2.1 and 2.3.1 includes a fix for CVE-2020-15213: In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimension of the output tensor, attackers can use a very large value to trigger a large allocation. The issue was patched in commit 204945b19e44b57906c9344c0d00120eeeae178a. A potential workaround is to add a custom \"Verifier\" to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87", + "cve": "CVE-2020-15213", + "id": "pyup.io-57521", + "more_info_path": "/vulnerabilities/CVE-2020-15213/57521", "specs": [ + ">=2.2.0rc0,<2.2.1", ">=2.3.0rc0,<2.3.1" ], - "v": ">=2.3.0rc0,<2.3.1" + "v": ">=2.2.0rc0,<2.2.1,>=2.3.0rc0,<2.3.1" + }, + { + "advisory": "Intel-tensorflow-avx512 versions 2.2.1 and 2.3.1 include a fix for CVE-2020-15191: In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to 'dlpack.to_dlpack' the expected validations will cause variables to bind to 'nullptr' while setting a 'status' variable to the error condition. However, this 'status' argument is not properly checked. Hence, code following these methods will bind references to null pointers. This is undefined behavior and reported as an error if compiling with '-fsanitize=null'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-q8qj-fc9q-cphr", + "cve": "CVE-2020-15191", + "id": "pyup.io-57520", + "more_info_path": "/vulnerabilities/CVE-2020-15191/57520", + "specs": [ + ">=2.2.0rc0,<2.2.1", + ">=2.3.0rc0,<2.3.1" + ], + "v": ">=2.2.0rc0,<2.2.1,>=2.3.0rc0,<2.3.1" }, { "advisory": "Intel-tensorflow-avx512 2.3.1 includes a fix for CVE-2020-15200: In Tensorflow before version 2.3.1, the \"RaggedCountSparseOutput\" implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the \"splits\" tensor generate a valid partitioning of the \"values\" tensor. Thus, the code sets up conditions to cause a heap buffer overflow. A \"BatchedMap\" is equivalent to a vector where each element is a hashmap. However, if the first element of \"splits_values\" is not 0, \"batch_idx\" will never be 1, hence there will be no hashmap at index 0 in \"per_batch_counts\". Trying to access that in the user code results in a segmentation fault. The issue was patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x7rp-74x2-mjf3", @@ -68760,20 +69128,20 @@ "v": ">=2.3.0rc0,<2.3.1" }, { - "advisory": "Intel-tensorflow-avx512 version 2.3.1 includes a fix for CVE-2020-15196: In Tensorflow version 2.3.0, the \"SparseCountSparseOutput\" and \"RaggedCountSparseOutput\" implementations don't validate that the \"weights\" tensor has the same shape as the data. The check exists for \"DenseCountSparseOutput\", where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue was patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pg59-2f92-5cph", - "cve": "CVE-2020-15196", - "id": "pyup.io-57516", - "more_info_path": "/vulnerabilities/CVE-2020-15196/57516", + "advisory": "Intel-tensorflow-avx512 2.3.1 includes a fix for CVE-2020-15199: In Tensorflow before version 2.3.1, the \"RaggedCountSparseOutput\" does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the \"splits\" tensor has the minimum required number of elements. Code uses this quantity to initialize a different data structure. Since \"BatchedMap\" is equivalent to a vector, it needs to have at least one element to not be \"nullptr\". If user passes a \"splits\" tensor that is empty or has exactly one element, we get a \"SIGABRT\" signal raised by the operating system. The issue was patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x5cp-9pcf-pp3h", + "cve": "CVE-2020-15199", + "id": "pyup.io-57518", + "more_info_path": "/vulnerabilities/CVE-2020-15199/57518", "specs": [ ">=2.3.0rc0,<2.3.1" ], "v": ">=2.3.0rc0,<2.3.1" }, { - "advisory": "Intel-tensorflow-avx512 2.3.1 includes a fix for CVE-2020-15199: In Tensorflow before version 2.3.1, the \"RaggedCountSparseOutput\" does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the \"splits\" tensor has the minimum required number of elements. Code uses this quantity to initialize a different data structure. Since \"BatchedMap\" is equivalent to a vector, it needs to have at least one element to not be \"nullptr\". If user passes a \"splits\" tensor that is empty or has exactly one element, we get a \"SIGABRT\" signal raised by the operating system. The issue was patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-x5cp-9pcf-pp3h", - "cve": "CVE-2020-15199", - "id": "pyup.io-57518", - "more_info_path": "/vulnerabilities/CVE-2020-15199/57518", + "advisory": "Intel-tensorflow-avx512 version 2.3.1 includes a fix for CVE-2020-15196: In Tensorflow version 2.3.0, the \"SparseCountSparseOutput\" and \"RaggedCountSparseOutput\" implementations don't validate that the \"weights\" tensor has the same shape as the data. The check exists for \"DenseCountSparseOutput\", where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue was patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pg59-2f92-5cph", + "cve": "CVE-2020-15196", + "id": "pyup.io-57516", + "more_info_path": "/vulnerabilities/CVE-2020-15196/57516", "specs": [ ">=2.3.0rc0,<2.3.1" ], @@ -68790,17 +69158,14 @@ "v": ">=2.3.0rc0,<2.3.1" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37637: It is possible to trigger a null pointer dereference in TensorFlow by passing an invalid input to `tf.raw_ops.CompressElement`. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/data/compression_utils.cc#L34) was accessing the size of a buffer obtained from the return of a separate function call before validating that said buffer is valid. The Tensorflow team has patched the issue in GitHub commit 5dc7f6981fdaf74c8c5be41f393df705841fb7c5.", - "cve": "CVE-2021-37637", - "id": "pyup.io-57482", - "more_info_path": "/vulnerabilities/CVE-2021-37637/57482", + "advisory": "Intel-tensorflow-avx512 2.3.1 includes a fix for CVE-2020-15198: In Tensorflow before version 2.3.1, the \"SparseCountSparseOutput\" implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the \"indices\" tensor has the same shape as the \"values\" one. The values in these tensors are always accessed in parallel. Thus, a shape mismatch can result in accesses outside the bounds of heap allocated buffers. The issue was patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-jc87-6vpp-7ff3", + "cve": "CVE-2020-15198", + "id": "pyup.io-57515", + "more_info_path": "/vulnerabilities/CVE-2020-15198/57515", "specs": [ - ">=2.3.0rc0,<2.3.4", - ">=2.4.0rc0,<2.4.3", - ">=2.5.0rc0,<2.5.1", - ">=2.6.0rc0,<2.6.0" + ">=2.3.0rc0,<2.3.1" ], - "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" + "v": ">=2.3.0rc0,<2.3.1" }, { "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37680: In affected versions the implementation of fully connected layers in TFLite is vulnerable to a division by zero error (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/fully_connected.cc#L226). The Tensorflow team has patched the issue in GitHub commit 718721986aa137691ee23f03638867151f74935f.", @@ -68842,10 +69207,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37642: In affected versions the implementation of 'tf.raw_ops.ResourceScatterDiv' is vulnerable to a division by 0 error. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/resource_variable_ops.cc#L865) uses a common class for all binary operations but fails to treat the division by 0 case separately. The Tensorflow team has patched the issue in GitHub commit 4aacb30888638da75023e6601149415b39763d76.", - "cve": "CVE-2021-37642", - "id": "pyup.io-57485", - "more_info_path": "/vulnerabilities/CVE-2021-37642/57485", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37683: In affected versions the implementation of division in TFLite is vulnerable to a division by 0 error (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/div.cc). There is no check that the divisor tensor does not contain zero elements. The Tensorflow team has patched the issue in GitHub commit 1e206baedf8bef0334cca3eb92bab134ef525a28.", + "cve": "CVE-2021-37683", + "id": "pyup.io-57481", + "more_info_path": "/vulnerabilities/CVE-2021-37683/57481", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.4.0rc0,<2.4.3", @@ -68855,10 +69220,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37667: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in 'tf.raw_ops.UnicodeEncode'. The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/unicode_ops.cc#L533-L539) reads the first dimension of the 'input_splits' tensor before validating that this tensor is not empty. The Tensorflow team has patched the issue in GitHub commit 2e0ee46f1a47675152d3d865797a18358881d7a6.", - "cve": "CVE-2021-37667", - "id": "pyup.io-57480", - "more_info_path": "/vulnerabilities/CVE-2021-37667/57480", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37689: In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. This is caused by the MLIR optimization of 'L2NormalizeReduceAxis' operator. The implementation (https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements. The Tensorflow team has patched the issue in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955.", + "cve": "CVE-2021-37689", + "id": "pyup.io-57477", + "more_info_path": "/vulnerabilities/CVE-2021-37689/57477", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.4.0rc0,<2.4.3", @@ -68868,10 +69233,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37683: In affected versions the implementation of division in TFLite is vulnerable to a division by 0 error (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/div.cc). There is no check that the divisor tensor does not contain zero elements. The Tensorflow team has patched the issue in GitHub commit 1e206baedf8bef0334cca3eb92bab134ef525a28.", - "cve": "CVE-2021-37683", - "id": "pyup.io-57481", - "more_info_path": "/vulnerabilities/CVE-2021-37683/57481", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37671: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in 'tf.raw_ops.Map*' and 'tf.raw_ops.OrderedMap*' operations. The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L222-L248) has a check in place to ensure that 'indices' is in ascending order, but does not check that 'indices' is not empty. The Tensorflow team has patched the issue in GitHub commit 532f5c5a547126c634fefd43bbad1dc6417678ac.", + "cve": "CVE-2021-37671", + "id": "pyup.io-57476", + "more_info_path": "/vulnerabilities/CVE-2021-37671/57476", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.4.0rc0,<2.4.3", @@ -68881,10 +69246,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37636: In affected versions the implementation of 'tf.raw_ops.SparseDenseCwiseDiv' is vulnerable to a division by 0 error. The implementation (https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L56) uses a common class for all binary operations but fails to treat the division by 0 case separately. The Tensorflow team has patched the issue in GitHub commit d9204be9f49520cdaaeb2541d1dc5187b23f31d9.", - "cve": "CVE-2021-37636", - "id": "pyup.io-57484", - "more_info_path": "/vulnerabilities/CVE-2021-37636/57484", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37667: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in 'tf.raw_ops.UnicodeEncode'. The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/unicode_ops.cc#L533-L539) reads the first dimension of the 'input_splits' tensor before validating that this tensor is not empty. The Tensorflow team has patched the issue in GitHub commit 2e0ee46f1a47675152d3d865797a18358881d7a6.", + "cve": "CVE-2021-37667", + "id": "pyup.io-57480", + "more_info_path": "/vulnerabilities/CVE-2021-37667/57480", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.4.0rc0,<2.4.3", @@ -68894,10 +69259,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37671: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in 'tf.raw_ops.Map*' and 'tf.raw_ops.OrderedMap*' operations. The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L222-L248) has a check in place to ensure that 'indices' is in ascending order, but does not check that 'indices' is not empty. The Tensorflow team has patched the issue in GitHub commit 532f5c5a547126c634fefd43bbad1dc6417678ac.", - "cve": "CVE-2021-37671", - "id": "pyup.io-57476", - "more_info_path": "/vulnerabilities/CVE-2021-37671/57476", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37637: It is possible to trigger a null pointer dereference in TensorFlow by passing an invalid input to `tf.raw_ops.CompressElement`. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/data/compression_utils.cc#L34) was accessing the size of a buffer obtained from the return of a separate function call before validating that said buffer is valid. The Tensorflow team has patched the issue in GitHub commit 5dc7f6981fdaf74c8c5be41f393df705841fb7c5.", + "cve": "CVE-2021-37637", + "id": "pyup.io-57482", + "more_info_path": "/vulnerabilities/CVE-2021-37637/57482", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.4.0rc0,<2.4.3", @@ -68907,10 +69272,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37689: In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. This is caused by the MLIR optimization of 'L2NormalizeReduceAxis' operator. The implementation (https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements. The Tensorflow team has patched the issue in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955.", - "cve": "CVE-2021-37689", - "id": "pyup.io-57477", - "more_info_path": "/vulnerabilities/CVE-2021-37689/57477", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37642: In affected versions the implementation of 'tf.raw_ops.ResourceScatterDiv' is vulnerable to a division by 0 error. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/resource_variable_ops.cc#L865) uses a common class for all binary operations but fails to treat the division by 0 case separately. The Tensorflow team has patched the issue in GitHub commit 4aacb30888638da75023e6601149415b39763d76.", + "cve": "CVE-2021-37642", + "id": "pyup.io-57485", + "more_info_path": "/vulnerabilities/CVE-2021-37642/57485", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.4.0rc0,<2.4.3", @@ -68920,36 +69285,23 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37649: The code for 'tf.raw_ops.UncompressElement' can be made to trigger a null pointer dereference. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/compression_ops.cc#L50-L53) obtains a pointer to a 'CompressedElement' from a 'Variant' tensor and then proceeds to dereference it for decompressing. There is no check that the 'Variant' tensor contained a 'CompressedElement', so the pointer is actually 'nullptr'. The Tensorflow team has patched the issue in GitHub commit 7bdf50bb4f5c54a4997c379092888546c97c3ebd.", - "cve": "CVE-2021-37649", - "id": "pyup.io-57472", - "more_info_path": "/vulnerabilities/CVE-2021-37649/57472", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37636: In affected versions the implementation of 'tf.raw_ops.SparseDenseCwiseDiv' is vulnerable to a division by 0 error. The implementation (https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L56) uses a common class for all binary operations but fails to treat the division by 0 case separately. The Tensorflow team has patched the issue in GitHub commit d9204be9f49520cdaaeb2541d1dc5187b23f31d9.", + "cve": "CVE-2021-37636", + "id": "pyup.io-57484", + "more_info_path": "/vulnerabilities/CVE-2021-37636/57484", "specs": [ ">=2.3.0rc0,<2.3.4", - ">=2.5.0rc0,<2.5.1", ">=2.4.0rc0,<2.4.3", - ">=2.6.0rc0,<2.6.0" - ], - "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" - }, - { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37652: In affected versions the implementation for 'tf.raw_ops.BoostedTreesCreateEnsemble' can result in a use after free error if an attacker supplies specially crafted arguments. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/boosted_trees/resource_ops.cc#L55) uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent 'free'-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed. The Tensorflow team has patched the issue in GitHub commit 5ecec9c6fbdbc6be03295685190a45e7eee726ab.", - "cve": "CVE-2021-37652", - "id": "pyup.io-57465", - "more_info_path": "/vulnerabilities/CVE-2021-37652/57465", - "specs": [ - ">=2.3.0rc0,<2.3.4", ">=2.5.0rc0,<2.5.1", - ">=2.4.0rc0,<2.4.3", ">=2.6.0rc0,<2.6.0" ], - "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" + "v": ">=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37638: Sending invalid argument for 'row_partition_types' of 'tf.raw_ops.RaggedTensorToTensor' API results in a null pointer dereference and undefined behavior. The implementation (https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L328) accesses the first element of a user supplied list of values without validating that the provided list is not empty. The Tensorflow team has patched the issue in GitHub commit 301ae88b331d37a2a16159b65b255f4f9eb39314.", - "cve": "CVE-2021-37638", - "id": "pyup.io-57466", - "more_info_path": "/vulnerabilities/CVE-2021-37638/57466", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37649: The code for 'tf.raw_ops.UncompressElement' can be made to trigger a null pointer dereference. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/compression_ops.cc#L50-L53) obtains a pointer to a 'CompressedElement' from a 'Variant' tensor and then proceeds to dereference it for decompressing. There is no check that the 'Variant' tensor contained a 'CompressedElement', so the pointer is actually 'nullptr'. The Tensorflow team has patched the issue in GitHub commit 7bdf50bb4f5c54a4997c379092888546c97c3ebd.", + "cve": "CVE-2021-37649", + "id": "pyup.io-57472", + "more_info_path": "/vulnerabilities/CVE-2021-37649/57472", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.5.0rc0,<2.5.1", @@ -68959,10 +69311,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37654: In affected versions an attacker can trigger a crash via a 'CHECK'-fail in debug builds of TensorFlow using 'tf.raw_ops.ResourceGather' or a read from outside the bounds of heap allocated data in the same API in a release build. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L660-L668) does not check that the 'batch_dims' value that the user supplies is less than the rank of the input tensor. Since the implementation uses several for loops over the dimensions of 'tensor', this results in reading data from outside the bounds of heap allocated buffer backing the tensor. The Tensorflow team has patched the issue in GitHub commit bc9c546ce7015c57c2f15c168b3d9201de679a1d.", - "cve": "CVE-2021-37654", - "id": "pyup.io-57467", - "more_info_path": "/vulnerabilities/CVE-2021-37654/57467", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37659: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/cwise_ops_common.h#L264) assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr. We have patched the issue in GitHub commit 93f428fd1768df147171ed674fee1fc5ab8309ec.", + "cve": "CVE-2021-37659", + "id": "pyup.io-57473", + "more_info_path": "/vulnerabilities/CVE-2021-37659/57473", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.5.0rc0,<2.5.1", @@ -68972,10 +69324,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37659: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/cwise_ops_common.h#L264) assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr. We have patched the issue in GitHub commit 93f428fd1768df147171ed674fee1fc5ab8309ec.", - "cve": "CVE-2021-37659", - "id": "pyup.io-57473", - "more_info_path": "/vulnerabilities/CVE-2021-37659/57473", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37647: When a user does not supply arguments that determine a valid sparse tensor, 'tf.raw_ops.SparseTensorSliceDataset' implementation can be made to dereference a null pointer. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L240-L251) has some argument validation but fails to consider the case when either 'indices' or 'values' are provided for an empty sparse tensor when the other is not. If 'indices' is empty, then code that performs validation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L260-L261) (i.e., checking that the indices are monotonically increasing) results in a null pointer dereference. If 'indices' as provided by the user is empty, then 'indices' in the C++ code above is backed by an empty 'std::vector', hence calling 'indices->dim_size(0)' results in null pointer dereferencing (same as calling 'std::vector::at()' on an empty vector). The Tensorflow team has patched the issue in GitHub commit 02cc160e29d20631de3859c6653184e3f876b9d7.", + "cve": "CVE-2021-37647", + "id": "pyup.io-57475", + "more_info_path": "/vulnerabilities/CVE-2021-37647/57475", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.5.0rc0,<2.5.1", @@ -68998,10 +69350,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37639: When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer. Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration. The implementation (https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the 'tensor_name' user controlled input and immediately retrieves the tensor at the restoration index (controlled via 'preferred_shard' argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements and if the restoration index is outside the bounds, this results in heap OOB read. The Tensorflow team has patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622.", - "cve": "CVE-2021-37639", - "id": "pyup.io-57470", - "more_info_path": "/vulnerabilities/CVE-2021-37639/57470", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37654: In affected versions an attacker can trigger a crash via a 'CHECK'-fail in debug builds of TensorFlow using 'tf.raw_ops.ResourceGather' or a read from outside the bounds of heap allocated data in the same API in a release build. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L660-L668) does not check that the 'batch_dims' value that the user supplies is less than the rank of the input tensor. Since the implementation uses several for loops over the dimensions of 'tensor', this results in reading data from outside the bounds of heap allocated buffer backing the tensor. The Tensorflow team has patched the issue in GitHub commit bc9c546ce7015c57c2f15c168b3d9201de679a1d.", + "cve": "CVE-2021-37654", + "id": "pyup.io-57467", + "more_info_path": "/vulnerabilities/CVE-2021-37654/57467", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.5.0rc0,<2.5.1", @@ -69024,10 +69376,10 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37647: When a user does not supply arguments that determine a valid sparse tensor, 'tf.raw_ops.SparseTensorSliceDataset' implementation can be made to dereference a null pointer. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L240-L251) has some argument validation but fails to consider the case when either 'indices' or 'values' are provided for an empty sparse tensor when the other is not. If 'indices' is empty, then code that performs validation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L260-L261) (i.e., checking that the indices are monotonically increasing) results in a null pointer dereference. If 'indices' as provided by the user is empty, then 'indices' in the C++ code above is backed by an empty 'std::vector', hence calling 'indices->dim_size(0)' results in null pointer dereferencing (same as calling 'std::vector::at()' on an empty vector). The Tensorflow team has patched the issue in GitHub commit 02cc160e29d20631de3859c6653184e3f876b9d7.", - "cve": "CVE-2021-37647", - "id": "pyup.io-57475", - "more_info_path": "/vulnerabilities/CVE-2021-37647/57475", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37652: In affected versions the implementation for 'tf.raw_ops.BoostedTreesCreateEnsemble' can result in a use after free error if an attacker supplies specially crafted arguments. The implementation (https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/boosted_trees/resource_ops.cc#L55) uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent 'free'-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed. The Tensorflow team has patched the issue in GitHub commit 5ecec9c6fbdbc6be03295685190a45e7eee726ab.", + "cve": "CVE-2021-37652", + "id": "pyup.io-57465", + "more_info_path": "/vulnerabilities/CVE-2021-37652/57465", "specs": [ ">=2.3.0rc0,<2.3.4", ">=2.5.0rc0,<2.5.1", @@ -69063,25 +69415,51 @@ "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Several versions of TensorFlow are affected by CVE-2021-37686: In affected versions, the strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for ellipsis in axis definition (https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/strided_slice.cc#L103-L122). An attacker can craft a model such that 'ellipsis_end_idx' is smaller than 'i' (e.g., always negative). In this case, the inner loop does not increase 'i' and the 'continue' statement causes execution to skip over the preincrement at the end of the outer loop. The Tensorflow team has patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695.", - "cve": "CVE-2021-37686", - "id": "pyup.io-57464", - "more_info_path": "/vulnerabilities/CVE-2021-37686/57464", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37639: When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer. Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration. The implementation (https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the 'tensor_name' user controlled input and immediately retrieves the tensor at the restoration index (controlled via 'preferred_shard' argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements and if the restoration index is outside the bounds, this results in heap OOB read. The Tensorflow team has patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622.", + "cve": "CVE-2021-37639", + "id": "pyup.io-57470", + "more_info_path": "/vulnerabilities/CVE-2021-37639/57470", "specs": [ - ">=2.3.0rc0,<2.3.4rc0", - ">=2.4.0rc0,<2.4.3rc0", - ">=2.5.0rc0,<=2.5.0", + ">=2.3.0rc0,<2.3.4", + ">=2.5.0rc0,<2.5.1", + ">=2.4.0rc0,<2.4.3", ">=2.6.0rc0,<2.6.0" ], - "v": ">=2.3.0rc0,<2.3.4rc0,>=2.4.0rc0,<2.4.3rc0,>=2.5.0rc0,<=2.5.0,>=2.6.0rc0,<2.6.0" + "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.4.0 includes a fix for CVE-2020-26269: In TensorFlow release candidate versions 2.4.0rc*, the general implementation for matching filesystem paths to globbing pattern is vulnerable to an access out of bounds of the array holding the directories. There are multiple invariants and preconditions that are assumed by the parallel implementation of GetMatchingPaths but are not verified by the PRs introducing it (#40861 and #44310). Thus, we are completely rewriting the implementation to fully specify and validate these. This is patched in version 2.4.0. This issue only impacts master branch and the release candidates for TF version 2.4. The final release of the 2.4 release will be patched.", - "cve": "CVE-2020-26269", - "id": "pyup.io-57497", - "more_info_path": "/vulnerabilities/CVE-2020-26269/57497", - "specs": [ - ">=2.4.0rc0,<2.4.0" + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37638: Sending invalid argument for 'row_partition_types' of 'tf.raw_ops.RaggedTensorToTensor' API results in a null pointer dereference and undefined behavior. The implementation (https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L328) accesses the first element of a user supplied list of values without validating that the provided list is not empty. The Tensorflow team has patched the issue in GitHub commit 301ae88b331d37a2a16159b65b255f4f9eb39314.", + "cve": "CVE-2021-37638", + "id": "pyup.io-57466", + "more_info_path": "/vulnerabilities/CVE-2021-37638/57466", + "specs": [ + ">=2.3.0rc0,<2.3.4", + ">=2.5.0rc0,<2.5.1", + ">=2.4.0rc0,<2.4.3", + ">=2.6.0rc0,<2.6.0" + ], + "v": ">=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.6.0rc0,<2.6.0" + }, + { + "advisory": "Several versions of TensorFlow are affected by CVE-2021-37686: In affected versions, the strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for ellipsis in axis definition (https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/strided_slice.cc#L103-L122). An attacker can craft a model such that 'ellipsis_end_idx' is smaller than 'i' (e.g., always negative). In this case, the inner loop does not increase 'i' and the 'continue' statement causes execution to skip over the preincrement at the end of the outer loop. The Tensorflow team has patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695.", + "cve": "CVE-2021-37686", + "id": "pyup.io-57464", + "more_info_path": "/vulnerabilities/CVE-2021-37686/57464", + "specs": [ + ">=2.3.0rc0,<2.3.4rc0", + ">=2.4.0rc0,<2.4.3rc0", + ">=2.5.0rc0,<=2.5.0", + ">=2.6.0rc0,<2.6.0" + ], + "v": ">=2.3.0rc0,<2.3.4rc0,>=2.4.0rc0,<2.4.3rc0,>=2.5.0rc0,<=2.5.0,>=2.6.0rc0,<2.6.0" + }, + { + "advisory": "Intel-tensorflow-avx512 2.4.0 includes a fix for CVE-2020-26269: In TensorFlow release candidate versions 2.4.0rc*, the general implementation for matching filesystem paths to globbing pattern is vulnerable to an access out of bounds of the array holding the directories. There are multiple invariants and preconditions that are assumed by the parallel implementation of GetMatchingPaths but are not verified by the PRs introducing it (#40861 and #44310). Thus, we are completely rewriting the implementation to fully specify and validate these. This is patched in version 2.4.0. This issue only impacts master branch and the release candidates for TF version 2.4. The final release of the 2.4 release will be patched.", + "cve": "CVE-2020-26269", + "id": "pyup.io-57497", + "more_info_path": "/vulnerabilities/CVE-2020-26269/57497", + "specs": [ + ">=2.4.0rc0,<2.4.0" ], "v": ">=2.4.0rc0,<2.4.0" }, @@ -69100,10 +69478,10 @@ "v": ">=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4,>=2.5.0rc0,<2.5.0" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a heap buffer overflow in 'Conv3DBackprop*'. See CVE-2021-29520.", - "cve": "CVE-2021-29520", - "id": "pyup.io-57453", - "more_info_path": "/vulnerabilities/CVE-2021-29520/57453", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a division by 0 in 'Conv2DBackpropFilter'. See CVE-2021-29524.", + "cve": "CVE-2021-29524", + "id": "pyup.io-57446", + "more_info_path": "/vulnerabilities/CVE-2021-29524/57446", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69114,10 +69492,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a segfault in 'SparseCountSparseOutput'. See CVE-2021-29521.", - "cve": "CVE-2021-29521", - "id": "pyup.io-57457", - "more_info_path": "/vulnerabilities/CVE-2021-29521/57457", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a stack overflow in 'ParseAttrValue' with nested tensors. See CVE-2021-29615.", + "cve": "CVE-2021-29615", + "id": "pyup.io-57461", + "more_info_path": "/vulnerabilities/CVE-2021-29615/57461", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69128,10 +69506,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail in 'tf.raw_ops.EncodePng'. See CVE-2021-29531.", - "cve": "CVE-2021-29531", - "id": "pyup.io-57456", - "more_info_path": "/vulnerabilities/CVE-2021-29531/57456", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a heap buffer overflow in 'Conv3DBackprop*'. See CVE-2021-29520.", + "cve": "CVE-2021-29520", + "id": "pyup.io-57453", + "more_info_path": "/vulnerabilities/CVE-2021-29520/57453", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69142,10 +69520,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a 'CHECK'-fail in 'SparseCross' caused by type confusion. See CVE-2021-29519.", - "cve": "CVE-2021-29519", - "id": "pyup.io-57443", - "more_info_path": "/vulnerabilities/CVE-2021-29519/57443", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix vulnerabilities where session operations in eager mode lead to null pointer dereferences. See CVE-2021-29518.", + "cve": "CVE-2021-29518", + "id": "pyup.io-57447", + "more_info_path": "/vulnerabilities/CVE-2021-29518/57447", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69156,10 +69534,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a division by 0 in 'Conv2DBackpropFilter'. See CVE-2021-29524.", - "cve": "CVE-2021-29524", - "id": "pyup.io-57446", - "more_info_path": "/vulnerabilities/CVE-2021-29524/57446", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29552: An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar. Since the tensor is empty the `CHECK` involved in `.scalar()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination.", + "cve": "CVE-2021-29552", + "id": "pyup.io-57450", + "more_info_path": "/vulnerabilities/CVE-2021-29552/57450", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69170,10 +69548,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix vulnerabilities where session operations in eager mode lead to null pointer dereferences. See CVE-2021-29518.", - "cve": "CVE-2021-29518", - "id": "pyup.io-57447", - "more_info_path": "/vulnerabilities/CVE-2021-29518/57447", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29532: An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to `tf.raw_ops.RaggedCross`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/efea03b38fb8d3b81762237dc85e579cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments. Each of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads.", + "cve": "CVE-2021-29532", + "id": "pyup.io-57445", + "more_info_path": "/vulnerabilities/CVE-2021-29532/57445", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69184,10 +69562,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a 'CHECK'-fail in 'AddManySparseToTensorsMap'. See CVE-2021-29523.", - "cve": "CVE-2021-29523", - "id": "pyup.io-57448", - "more_info_path": "/vulnerabilities/CVE-2021-29523/57448", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail in 'tf.raw_ops.EncodePng'. See CVE-2021-29531.", + "cve": "CVE-2021-29531", + "id": "pyup.io-57456", + "more_info_path": "/vulnerabilities/CVE-2021-29531/57456", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69198,10 +69576,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a stack overflow in 'ParseAttrValue' with nested tensors. See CVE-2021-29615.", - "cve": "CVE-2021-29615", - "id": "pyup.io-57461", - "more_info_path": "/vulnerabilities/CVE-2021-29615/57461", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix fixes a heap buffer overflow caused by rounding. See CVE-2021-29529.", + "cve": "CVE-2021-29529", + "id": "pyup.io-57449", + "more_info_path": "/vulnerabilities/CVE-2021-29529/57449", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69212,10 +69590,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29513: Calling TF operations with tensors of non-numeric types when the operations expect numeric tensors result in null pointer dereferences. The conversion from Python array to C++ array (https://github.com/tensorflow/tensorflow/blob/ff70c47a396ef1e3cb73c90513da4f5cb71bebba/tensorflow/python/lib/core/ndarray_tensor.cc#L113-L169) is vulnerable to a type confusion.", - "cve": "CVE-2021-29513", - "id": "pyup.io-57455", - "more_info_path": "/vulnerabilities/CVE-2021-29513/57455", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29539: TensorFlow is an end-to-end open source platform for machine learning. Calling `tf.raw_ops.ImmutableConst`(https://www.tensorflow.org/api_docs/python/tf/raw_ops/ImmutableConst) with a `dtype` of `tf.resource` or `tf.variant` results in a segfault in the implementation as code assumes that the tensor contents are pure scalars. We have patched the issue in 4f663d4b8f0bec1b48da6fa091a7d29609980fa4 and will release TensorFlow 2.5.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. If using `tf.raw_ops.ImmutableConst` in code, you can prevent the segfault by inserting a filter for the `dtype` argument.", + "cve": "CVE-2021-29539", + "id": "pyup.io-57462", + "more_info_path": "/vulnerabilities/CVE-2021-29539/57462", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69226,10 +69604,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a division by 0 in 'Conv2DBackpropInput'. See CVE-2021-29525.", - "cve": "CVE-2021-29525", - "id": "pyup.io-57442", - "more_info_path": "/vulnerabilities/CVE-2021-29525/57442", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a 'CHECK'-fail in 'AddManySparseToTensorsMap'. See CVE-2021-29523.", + "cve": "CVE-2021-29523", + "id": "pyup.io-57448", + "more_info_path": "/vulnerabilities/CVE-2021-29523/57448", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69240,10 +69618,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix fixes a heap buffer overflow caused by rounding. See CVE-2021-29529.", - "cve": "CVE-2021-29529", - "id": "pyup.io-57449", - "more_info_path": "/vulnerabilities/CVE-2021-29529/57449", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a division by 0 in 'Conv3DBackprop*'. See CVE-2021-29522.", + "cve": "CVE-2021-29522", + "id": "pyup.io-57444", + "more_info_path": "/vulnerabilities/CVE-2021-29522/57444", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69254,10 +69632,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 includes a fix for CVE-2021-29533: An attacker can trigger a denial of service via a 'CHECK' failure by passing an empty image to 'tf.raw_ops.DrawBoundingBoxes'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses 'CHECK_*' assertions instead of 'OP_REQUIRES' to validate user controlled inputs. Whereas 'OP_REQUIRES' allows returning an error condition back to the user, the 'CHECK_*' macros result in a crash if the condition is false, similar to 'assert'. In this case, 'height' is 0 from the 'images' input. This results in 'max_box_row_clamp' being negative and the assertion being falsified, followed by aborting program execution.", - "cve": "CVE-2021-29533", - "id": "pyup.io-57452", - "more_info_path": "/vulnerabilities/CVE-2021-29533/57452", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a segfault in 'SparseCountSparseOutput'. See CVE-2021-29521.", + "cve": "CVE-2021-29521", + "id": "pyup.io-57457", + "more_info_path": "/vulnerabilities/CVE-2021-29521/57457", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69268,10 +69646,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by invalid validation in 'SparseMatrixSparseCholesky'. See CVE-2021-29530.", - "cve": "CVE-2021-29530", - "id": "pyup.io-57441", - "more_info_path": "/vulnerabilities/CVE-2021-29530/57441", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a 'CHECK'-fail in 'SparseCross' caused by type confusion. See CVE-2021-29519.", + "cve": "CVE-2021-29519", + "id": "pyup.io-57443", + "more_info_path": "/vulnerabilities/CVE-2021-29519/57443", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69282,10 +69660,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29534: An attacker can trigger a denial of service via a 'CHECK'-fail in 'tf.raw_ops.SparseConcat'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/b432a38fe0e1b4b904a6c222cbce794c39703e87/tensorflow/core/kernels/sparse_concat_op.cc#L76) takes the values specified in 'shapes[0]' as dimensions for the output shape. The 'TensorShape' constructor (https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a 'CHECK' operation which triggers when 'InitDims' (https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use 'BuildTensorShapeBase' or 'AddDimWithStatus' to prevent 'CHECK'-failures in the presence of overflows.", - "cve": "CVE-2021-29534", - "id": "pyup.io-57454", - "more_info_path": "/vulnerabilities/CVE-2021-29534/57454", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29513: Calling TF operations with tensors of non-numeric types when the operations expect numeric tensors result in null pointer dereferences. The conversion from Python array to C++ array (https://github.com/tensorflow/tensorflow/blob/ff70c47a396ef1e3cb73c90513da4f5cb71bebba/tensorflow/python/lib/core/ndarray_tensor.cc#L113-L169) is vulnerable to a type confusion.", + "cve": "CVE-2021-29513", + "id": "pyup.io-57455", + "more_info_path": "/vulnerabilities/CVE-2021-29513/57455", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69296,10 +69674,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29538: An attacker can cause a division by zero to occur in 'Conv2DBackpropFilter'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then 'work_unit_size' is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service.", - "cve": "CVE-2021-29538", - "id": "pyup.io-57458", - "more_info_path": "/vulnerabilities/CVE-2021-29538/57458", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by invalid validation in 'SparseMatrixSparseCholesky'. See CVE-2021-29530.", + "cve": "CVE-2021-29530", + "id": "pyup.io-57441", + "more_info_path": "/vulnerabilities/CVE-2021-29530/57441", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69324,10 +69702,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29532: An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to `tf.raw_ops.RaggedCross`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/efea03b38fb8d3b81762237dc85e579cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments. Each of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads.", - "cve": "CVE-2021-29532", - "id": "pyup.io-57445", - "more_info_path": "/vulnerabilities/CVE-2021-29532/57445", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29534: An attacker can trigger a denial of service via a 'CHECK'-fail in 'tf.raw_ops.SparseConcat'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/b432a38fe0e1b4b904a6c222cbce794c39703e87/tensorflow/core/kernels/sparse_concat_op.cc#L76) takes the values specified in 'shapes[0]' as dimensions for the output shape. The 'TensorShape' constructor (https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a 'CHECK' operation which triggers when 'InitDims' (https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use 'BuildTensorShapeBase' or 'AddDimWithStatus' to prevent 'CHECK'-failures in the presence of overflows.", + "cve": "CVE-2021-29534", + "id": "pyup.io-57454", + "more_info_path": "/vulnerabilities/CVE-2021-29534/57454", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69338,10 +69716,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.5.0, 2.4.2, 2.3.3, 2.2.3 and 2.1.4 include a fix for CVE-2021-29548: An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc) does not validate all constraints specified in the op's contract (https://www.tensorflow.org/api_docs/python/tf/raw_ops/QuantizedBatchNormWithGlobalNormalization).", - "cve": "CVE-2021-29548", - "id": "pyup.io-57451", - "more_info_path": "/vulnerabilities/CVE-2021-29548/57451", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a division by 0 in 'Conv2DBackpropInput'. See CVE-2021-29525.", + "cve": "CVE-2021-29525", + "id": "pyup.io-57442", + "more_info_path": "/vulnerabilities/CVE-2021-29525/57442", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69352,10 +69730,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29552: An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar. Since the tensor is empty the `CHECK` involved in `.scalar()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination.", - "cve": "CVE-2021-29552", - "id": "pyup.io-57450", - "more_info_path": "/vulnerabilities/CVE-2021-29552/57450", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 includes a fix for CVE-2021-29533: An attacker can trigger a denial of service via a 'CHECK' failure by passing an empty image to 'tf.raw_ops.DrawBoundingBoxes'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses 'CHECK_*' assertions instead of 'OP_REQUIRES' to validate user controlled inputs. Whereas 'OP_REQUIRES' allows returning an error condition back to the user, the 'CHECK_*' macros result in a crash if the condition is false, similar to 'assert'. In this case, 'height' is 0 from the 'images' input. This results in 'max_box_row_clamp' being negative and the assertion being falsified, followed by aborting program execution.", + "cve": "CVE-2021-29533", + "id": "pyup.io-57452", + "more_info_path": "/vulnerabilities/CVE-2021-29533/57452", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69366,10 +69744,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29539: TensorFlow is an end-to-end open source platform for machine learning. Calling `tf.raw_ops.ImmutableConst`(https://www.tensorflow.org/api_docs/python/tf/raw_ops/ImmutableConst) with a `dtype` of `tf.resource` or `tf.variant` results in a segfault in the implementation as code assumes that the tensor contents are pure scalars. We have patched the issue in 4f663d4b8f0bec1b48da6fa091a7d29609980fa4 and will release TensorFlow 2.5.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. If using `tf.raw_ops.ImmutableConst` in code, you can prevent the segfault by inserting a filter for the `dtype` argument.", - "cve": "CVE-2021-29539", - "id": "pyup.io-57462", - "more_info_path": "/vulnerabilities/CVE-2021-29539/57462", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29538: An attacker can cause a division by zero to occur in 'Conv2DBackpropFilter'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then 'work_unit_size' is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service.", + "cve": "CVE-2021-29538", + "id": "pyup.io-57458", + "more_info_path": "/vulnerabilities/CVE-2021-29538/57458", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69380,10 +69758,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 fix a division by 0 in 'Conv3DBackprop*'. See CVE-2021-29522.", - "cve": "CVE-2021-29522", - "id": "pyup.io-57444", - "more_info_path": "/vulnerabilities/CVE-2021-29522/57444", + "advisory": "Intel-tensorflow-avx512 2.5.0, 2.4.2, 2.3.3, 2.2.3 and 2.1.4 include a fix for CVE-2021-29548: An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc) does not validate all constraints specified in the op's contract (https://www.tensorflow.org/api_docs/python/tf/raw_ops/QuantizedBatchNormWithGlobalNormalization).", + "cve": "CVE-2021-29548", + "id": "pyup.io-57451", + "more_info_path": "/vulnerabilities/CVE-2021-29548/57451", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69464,10 +69842,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2,>=2.2.0rc0,<2.2.3" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29543: An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.CTCGreedyDecoder`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/1615440b17b364b875eb06f43d087381f1460a65/tensorflow/core/kernels/ctc_decoder_ops.cc#L37-L50) has a `CHECK_LT` inserted to validate some invariants. When this condition is false, the program aborts, instead of returning a valid error to the user. This abnormal termination can be weaponized in denial of service attacks.", - "cve": "CVE-2021-29543", - "id": "pyup.io-57435", - "more_info_path": "/vulnerabilities/CVE-2021-29543/57435", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29544: An attacker can trigger a denial of service via a 'CHECK'-fail in 'tf.raw_ops.QuantizeAndDequantizeV4Grad'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163) does not validate the rank of the 'input_*' tensors. In turn, this results in the tensors being passes as they are to 'QuantizeAndDequantizePerChannelGradientImpl' (https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306). However, the 'vec' method, requires the rank to 1 and triggers a 'CHECK' failure otherwise.", + "cve": "CVE-2021-29544", + "id": "pyup.io-57436", + "more_info_path": "/vulnerabilities/CVE-2021-29544/57436", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69478,10 +69856,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.1.0rc0,<2.1.4,>=2.4.0rc0,<2.4.2,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29544: An attacker can trigger a denial of service via a 'CHECK'-fail in 'tf.raw_ops.QuantizeAndDequantizeV4Grad'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163) does not validate the rank of the 'input_*' tensors. In turn, this results in the tensors being passes as they are to 'QuantizeAndDequantizePerChannelGradientImpl' (https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306). However, the 'vec' method, requires the rank to 1 and triggers a 'CHECK' failure otherwise.", - "cve": "CVE-2021-29544", - "id": "pyup.io-57436", - "more_info_path": "/vulnerabilities/CVE-2021-29544/57436", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29543: An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.CTCGreedyDecoder`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/1615440b17b364b875eb06f43d087381f1460a65/tensorflow/core/kernels/ctc_decoder_ops.cc#L37-L50) has a `CHECK_LT` inserted to validate some invariants. When this condition is false, the program aborts, instead of returning a valid error to the user. This abnormal termination can be weaponized in denial of service attacks.", + "cve": "CVE-2021-29543", + "id": "pyup.io-57435", + "more_info_path": "/vulnerabilities/CVE-2021-29543/57435", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.1.0rc0,<2.1.4", @@ -69646,10 +70024,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29617: An attacker can cause a denial of service via `CHECK`-fail in `tf.strings.substr` with invalid arguments.", - "cve": "CVE-2021-29617", - "id": "pyup.io-57421", - "more_info_path": "/vulnerabilities/CVE-2021-29617/57421", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a segfault in 'CTCBeamSearchDecoder'. See CVE-2021-29581.", + "cve": "CVE-2021-29581", + "id": "pyup.io-57420", + "more_info_path": "/vulnerabilities/CVE-2021-29581/57420", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.3.0rc0,<2.3.3", @@ -69660,10 +70038,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix an undefined behavior in 'MaxPool3DGradGrad'. See CVE-2021-29574.", - "cve": "CVE-2021-29574", - "id": "pyup.io-57423", - "more_info_path": "/vulnerabilities/CVE-2021-29574/57423", + "advisory": "Intel-tensorflow-avx512 versions 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 include a fix for CVE-2021-29512: If the 'splits' argument of 'RaggedBincount' does not specify a valid 'SparseTensor' (https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the 'splits' tensor buffer in the implementation of the 'RaggedBincount' op (https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433). Before the 'for' loop, 'batch_idx' is set to 0. The user controls the 'splits' array, making it contain only one element, 0. Thus, the code in the 'while' loop would increment 'batch_idx' and then try to read 'splits(1)', which is outside of bounds.", + "cve": "CVE-2021-29512", + "id": "pyup.io-57422", + "more_info_path": "/vulnerabilities/CVE-2021-29512/57422", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.3.0rc0,<2.3.3", @@ -69674,10 +70052,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 include a fix for CVE-2021-29512: If the 'splits' argument of 'RaggedBincount' does not specify a valid 'SparseTensor' (https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the 'splits' tensor buffer in the implementation of the 'RaggedBincount' op (https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433). Before the 'for' loop, 'batch_idx' is set to 0. The user controls the 'splits' array, making it contain only one element, 0. Thus, the code in the 'while' loop would increment 'batch_idx' and then try to read 'splits(1)', which is outside of bounds.", - "cve": "CVE-2021-29512", - "id": "pyup.io-57422", - "more_info_path": "/vulnerabilities/CVE-2021-29512/57422", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix an undefined behavior in 'MaxPool3DGradGrad'. See CVE-2021-29574.", + "cve": "CVE-2021-29574", + "id": "pyup.io-57423", + "more_info_path": "/vulnerabilities/CVE-2021-29574/57423", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.3.0rc0,<2.3.3", @@ -69688,10 +70066,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.3.0rc0,<2.3.3,>=2.4.0rc0,<2.4.2,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a segfault in 'CTCBeamSearchDecoder'. See CVE-2021-29581.", - "cve": "CVE-2021-29581", - "id": "pyup.io-57420", - "more_info_path": "/vulnerabilities/CVE-2021-29581/57420", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29617: An attacker can cause a denial of service via `CHECK`-fail in `tf.strings.substr` with invalid arguments.", + "cve": "CVE-2021-29617", + "id": "pyup.io-57421", + "more_info_path": "/vulnerabilities/CVE-2021-29617/57421", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.3.0rc0,<2.3.3", @@ -69744,10 +70122,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.1.0rc0,<2.1.4,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail in 'LoadAndRemapMatrix'. See CVE-2021-29561.", - "cve": "CVE-2021-29561", - "id": "pyup.io-57414", - "more_info_path": "/vulnerabilities/CVE-2021-29561/57414", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'QuantizedConv2D'. See CVE-2021-29527.", + "cve": "CVE-2021-29527", + "id": "pyup.io-57415", + "more_info_path": "/vulnerabilities/CVE-2021-29527/57415", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -69758,10 +70136,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4,>=2.3.0rc0,<2.3.3" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29595: The implementation of the `DepthToSpace` TFLite operator is vulnerable to a division by zero error (https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/depth_to_space.cc#L63-L69). An attacker can craft a model such that `params->block_size` is 0.", - "cve": "CVE-2021-29595", - "id": "pyup.io-57416", - "more_info_path": "/vulnerabilities/CVE-2021-29595/57416", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail in 'LoadAndRemapMatrix'. See CVE-2021-29561.", + "cve": "CVE-2021-29561", + "id": "pyup.io-57414", + "more_info_path": "/vulnerabilities/CVE-2021-29561/57414", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -69772,10 +70150,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4,>=2.3.0rc0,<2.3.3" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'QuantizedConv2D'. See CVE-2021-29527.", - "cve": "CVE-2021-29527", - "id": "pyup.io-57415", - "more_info_path": "/vulnerabilities/CVE-2021-29527/57415", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29595: The implementation of the `DepthToSpace` TFLite operator is vulnerable to a division by zero error (https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/depth_to_space.cc#L63-L69). An attacker can craft a model such that `params->block_size` is 0.", + "cve": "CVE-2021-29595", + "id": "pyup.io-57416", + "more_info_path": "/vulnerabilities/CVE-2021-29595/57416", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -69800,10 +70178,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.2.0rc0,<2.2.3,>=2.3.0rc0,<2.3.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29589: The reference implementation of the `GatherNd` TFLite operator is vulnerable to a division by zero error (https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/reference_ops.h#L966). An attacker can craft a model such that `params` input would be an empty tensor. In turn, `params_shape.Dims(.)` would be zero, in at least one dimension.", - "cve": "CVE-2021-29589", - "id": "pyup.io-57409", - "more_info_path": "/vulnerabilities/CVE-2021-29589/57409", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'TransposeConv'. See CVE-2021-29588.", + "cve": "CVE-2021-29588", + "id": "pyup.io-57405", + "more_info_path": "/vulnerabilities/CVE-2021-29588/57405", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -69814,10 +70192,24 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'TransposeConv'. See CVE-2021-29588.", - "cve": "CVE-2021-29588", - "id": "pyup.io-57405", - "more_info_path": "/vulnerabilities/CVE-2021-29588/57405", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'BandedTriangularSolve'. See CVE-2021-29612.", + "cve": "CVE-2021-29612", + "id": "pyup.io-57407", + "more_info_path": "/vulnerabilities/CVE-2021-29612/57407", + "specs": [ + ">=2.5.0rc0,<2.5.0", + ">=2.4.0rc0,<2.4.2", + ">=2.3.0rc0,<2.3.3", + ">=2.1.0rc0,<2.1.4", + ">=2.2.0rc0,<2.2.3" + ], + "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3" + }, + { + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29589: The reference implementation of the `GatherNd` TFLite operator is vulnerable to a division by zero error (https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/reference_ops.h#L966). An attacker can craft a model such that `params` input would be an empty tensor. In turn, `params_shape.Dims(.)` would be zero, in at least one dimension.", + "cve": "CVE-2021-29589", + "id": "pyup.io-57409", + "more_info_path": "/vulnerabilities/CVE-2021-29589/57409", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -69856,10 +70248,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'FusedBatchNorm'. See CVE-2021-29555.", - "cve": "CVE-2021-29555", - "id": "pyup.io-57411", - "more_info_path": "/vulnerabilities/CVE-2021-29555/57411", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'SparseMatMul'. See CVE-2021-29557.", + "cve": "CVE-2021-29557", + "id": "pyup.io-57406", + "more_info_path": "/vulnerabilities/CVE-2021-29557/57406", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -69870,10 +70262,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'BandedTriangularSolve'. See CVE-2021-29612.", - "cve": "CVE-2021-29612", - "id": "pyup.io-57407", - "more_info_path": "/vulnerabilities/CVE-2021-29612/57407", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'FusedBatchNorm'. See CVE-2021-29555.", + "cve": "CVE-2021-29555", + "id": "pyup.io-57411", + "more_info_path": "/vulnerabilities/CVE-2021-29555/57411", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -69898,24 +70290,24 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'SparseMatMul'. See CVE-2021-29557.", - "cve": "CVE-2021-29557", - "id": "pyup.io-57406", - "more_info_path": "/vulnerabilities/CVE-2021-29557/57406", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB read in 'tf.raw_ops.Dequantize'. See CVE-2021-29582.", + "cve": "CVE-2021-29582", + "id": "pyup.io-57354", + "more_info_path": "/vulnerabilities/CVE-2021-29582/57354", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", ">=2.3.0rc0,<2.3.3", - ">=2.1.0rc0,<2.1.4", - ">=2.2.0rc0,<2.2.3" + ">=2.2.0rc0,<2.2.3", + ">=2.1.0rc0,<2.1.4" ], - "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.1.0rc0,<2.1.4,>=2.2.0rc0,<2.2.3" + "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'MaxPoolGrad'. See CVE-2021-29579.", - "cve": "CVE-2021-29579", - "id": "pyup.io-57375", - "more_info_path": "/vulnerabilities/CVE-2021-29579/57375", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a integer overflow in TFLite concatentation. See CVE-2021-29601.", + "cve": "CVE-2021-29601", + "id": "pyup.io-57390", + "more_info_path": "/vulnerabilities/CVE-2021-29601/57390", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -69926,10 +70318,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'QuantizedMul'. See CVE-2021-29528.", - "cve": "CVE-2021-29528", - "id": "pyup.io-57396", - "more_info_path": "/vulnerabilities/CVE-2021-29528/57396", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'FractionalAvgPoolGrad'. See CVE-2021-29578.", + "cve": "CVE-2021-29578", + "id": "pyup.io-57366", + "more_info_path": "/vulnerabilities/CVE-2021-29578/57366", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -69940,10 +70332,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB in 'QuantizeAndDequantizeV3'. See CVE-2021-29553.", - "cve": "CVE-2021-29553", - "id": "pyup.io-57397", - "more_info_path": "/vulnerabilities/CVE-2021-29553/57397", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29616: The implementation of TrySimplify (https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc#L390-L401) has undefined behavior due to dereferencing a null pointer in corner cases that result in optimizing a node with no inputs.", + "cve": "CVE-2021-29616", + "id": "pyup.io-57361", + "more_info_path": "/vulnerabilities/CVE-2021-29616/57361", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -69954,10 +70346,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29546: TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger an integer division by zero undefined behavior in `tf.raw_ops.QuantizedBiasAdd`. This is because the implementation of the Eigen kernel (https://github.com/tensorflow/tensorflow/blob/61bca8bd5ba8a68b2d97435ddfafcdf2b85672cd/tensorflow/core/kernels/quantization_utils.h#L812-L849) does a division by the number of elements of the smaller input (based on shape) without checking that this is not zero.", - "cve": "CVE-2021-29546", - "id": "pyup.io-57401", - "more_info_path": "/vulnerabilities/CVE-2021-29546/57401", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a null pointer dereference in 'EditDistance'. See CVE-2021-29564.", + "cve": "CVE-2021-29564", + "id": "pyup.io-57379", + "more_info_path": "/vulnerabilities/CVE-2021-29564/57379", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -69968,10 +70360,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB read in TFLite's implementation of 'Minimum' or 'Maximum'. See CVE-2021-29590.", - "cve": "CVE-2021-29590", - "id": "pyup.io-57393", - "more_info_path": "/vulnerabilities/CVE-2021-29590/57393", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of hashtable lookup. See CVE-2021-29604.", + "cve": "CVE-2021-29604", + "id": "pyup.io-57382", + "more_info_path": "/vulnerabilities/CVE-2021-29604/57382", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -69982,10 +70374,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'SVDF'. See CVE-2021-29598.", - "cve": "CVE-2021-29598", - "id": "pyup.io-57394", - "more_info_path": "/vulnerabilities/CVE-2021-29598/57394", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29571: The implementation of 'tf.raw_ops.MaxPoolGradWithArgmax' can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation (https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of 'boxes' input is 4, as required by the op (https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in 'boxes' is less than 4, accesses similar to 'tboxes(b, bb, 3)' will access data outside of bounds. Further during code execution there are also writes to these indices.", + "cve": "CVE-2021-29571", + "id": "pyup.io-57386", + "more_info_path": "/vulnerabilities/CVE-2021-29571/57386", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -69996,10 +70388,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29618: Passing a complex argument to `tf.transpose` at the same time as passing 'conjugate=True' argument results in a crash.", - "cve": "CVE-2021-29618", - "id": "pyup.io-57402", - "more_info_path": "/vulnerabilities/CVE-2021-29618/57402", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29587: TensorFlow is an end-to-end open source platform for machine learning. The `Prepare` step of the `SpaceToDepth` TFLite operator does not check for 0 before division (https://github.com/tensorflow/tensorflow/blob/5f7975d09eac0f10ed8a17dbb6f5964977725adc/tensorflow/lite/kernels/space_to_depth.cc#L63-L67). An attacker can craft a model such that `params->block_size` would be zero.", + "cve": "CVE-2021-29587", + "id": "pyup.io-57384", + "more_info_path": "/vulnerabilities/CVE-2021-29587/57384", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70010,10 +70402,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail due to integer overflow. See CVE-2021-29584.", - "cve": "CVE-2021-29584", - "id": "pyup.io-57351", - "more_info_path": "/vulnerabilities/CVE-2021-29584/57351", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29608: TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in 'tf.raw_ops.RaggedTensorToTensor', an attacker can exploit an undefined behavior if input arguments are empty. The implementation (https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones. There are multiple 'DCHECK' validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything.", + "cve": "CVE-2021-29608", + "id": "pyup.io-57363", + "more_info_path": "/vulnerabilities/CVE-2021-29608/57363", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70024,10 +70416,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix an overflow and a denial of service in 'tf.raw_ops.ReverseSequence'. See CVE-2021-29575.", - "cve": "CVE-2021-29575", - "id": "pyup.io-57353", - "more_info_path": "/vulnerabilities/CVE-2021-29575/57353", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by incomplete validation in 'SparseSparseMinimum'. See CVE-2021-29607.", + "cve": "CVE-2021-29607", + "id": "pyup.io-57359", + "more_info_path": "/vulnerabilities/CVE-2021-29607/57359", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70038,10 +70430,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB read in 'tf.raw_ops.Dequantize'. See CVE-2021-29582.", - "cve": "CVE-2021-29582", - "id": "pyup.io-57354", - "more_info_path": "/vulnerabilities/CVE-2021-29582/57354", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB write in TFLite. See CVE-2021-29603.", + "cve": "CVE-2021-29603", + "id": "pyup.io-57381", + "more_info_path": "/vulnerabilities/CVE-2021-29603/57381", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70052,10 +70444,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix fixes a heap OOB read in TFLite. See CVE-2021-29606.", - "cve": "CVE-2021-29606", - "id": "pyup.io-57355", - "more_info_path": "/vulnerabilities/CVE-2021-29606/57355", + "advisory": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in 'tf.raw_ops.ParameterizedTruncatedNormal'. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of 'shape'. If 'shape' argument is empty, then 'shape_tensor.flat()' is an empty array. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.", + "cve": "CVE-2021-29568", + "id": "pyup.io-57385", + "more_info_path": "/vulnerabilities/CVE-2021-29568/57385", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70066,10 +70458,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'SpaceToBatchNd'. See CVE-2021-29597.", - "cve": "CVE-2021-29597", - "id": "pyup.io-57357", - "more_info_path": "/vulnerabilities/CVE-2021-29597/57357", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29550: An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments (https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to `GeneratePoolingSequence`(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since `output_length` can be 0, this results in runtime crashing.", + "cve": "CVE-2021-29550", + "id": "pyup.io-57400", + "more_info_path": "/vulnerabilities/CVE-2021-29550/57400", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70080,10 +70472,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail in 'tf.raw_ops.IRFFT'. See CVE-2021-29562.", - "cve": "CVE-2021-29562", - "id": "pyup.io-57356", - "more_info_path": "/vulnerabilities/CVE-2021-29562/57356", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'BatchToSpaceNd'. See CVE-2021-29593.", + "cve": "CVE-2021-29593", + "id": "pyup.io-57371", + "more_info_path": "/vulnerabilities/CVE-2021-29593/57371", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70108,24 +70500,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by incomplete validation in 'SparseSparseMinimum'. See CVE-2021-29607.", - "cve": "CVE-2021-29607", - "id": "pyup.io-57359", - "more_info_path": "/vulnerabilities/CVE-2021-29607/57359", - "specs": [ - ">=2.5.0rc0,<2.5.0", - ">=2.4.0rc0,<2.4.2", - ">=2.3.0rc0,<2.3.3", - ">=2.2.0rc0,<2.2.3", - ">=2.1.0rc0,<2.1.4" - ], - "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" - }, - { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a null pointer dereference in 'SparseFillEmptyRows'. See CVE-2021-29565.", - "cve": "CVE-2021-29565", - "id": "pyup.io-57360", - "more_info_path": "/vulnerabilities/CVE-2021-29565/57360", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by incomplete validation in 'SparseAdd'. See CVE-2021-29609.", + "cve": "CVE-2021-29609", + "id": "pyup.io-57364", + "more_info_path": "/vulnerabilities/CVE-2021-29609/57364", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70136,10 +70514,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a segfault in 'tf.raw_ops.SparseCountSparseOutput'. See CVE-2021-29619.", - "cve": "CVE-2021-29619", - "id": "pyup.io-57365", - "more_info_path": "/vulnerabilities/CVE-2021-29619/57365", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'SpaceToBatchNd'. See CVE-2021-29597.", + "cve": "CVE-2021-29597", + "id": "pyup.io-57357", + "more_info_path": "/vulnerabilities/CVE-2021-29597/57357", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70150,10 +70528,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'FractionalAvgPoolGrad'. See CVE-2021-29578.", - "cve": "CVE-2021-29578", - "id": "pyup.io-57366", - "more_info_path": "/vulnerabilities/CVE-2021-29578/57366", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB read in TFLite's implementation of 'Minimum' or 'Maximum'. See CVE-2021-29590.", + "cve": "CVE-2021-29590", + "id": "pyup.io-57393", + "more_info_path": "/vulnerabilities/CVE-2021-29590/57393", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70164,10 +70542,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'DepthwiseConv'. See CVE-2021-29602.", - "cve": "CVE-2021-29602", - "id": "pyup.io-57367", - "more_info_path": "/vulnerabilities/CVE-2021-29602/57367", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in optimized pooling implementations in TFLite. See CVE-2021-29586.", + "cve": "CVE-2021-29586", + "id": "pyup.io-57392", + "more_info_path": "/vulnerabilities/CVE-2021-29586/57392", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70178,10 +70556,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 update 'curl' to '7.76.0' to handle CVE-2020-8177.", - "cve": "CVE-2020-8177", - "id": "pyup.io-57369", - "more_info_path": "/vulnerabilities/CVE-2020-8177/57369", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail due to integer overflow. See CVE-2021-29584.", + "cve": "CVE-2021-29584", + "id": "pyup.io-57351", + "more_info_path": "/vulnerabilities/CVE-2021-29584/57351", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70192,10 +70570,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'AvgPool3DGrad'. See CVE-2021-29577.", - "cve": "CVE-2021-29577", - "id": "pyup.io-57373", - "more_info_path": "/vulnerabilities/CVE-2021-29577/57373", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix an undefined behavior and a 'CHECK'-fail in 'FractionalMaxPoolGrad'. See CVE-2021-29580.", + "cve": "CVE-2021-29580", + "id": "pyup.io-57399", + "more_info_path": "/vulnerabilities/CVE-2021-29580/57399", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70206,10 +70584,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'DenseCountSparseOutput'. See CVE-2021-29554.", - "cve": "CVE-2021-29554", - "id": "pyup.io-57372", - "more_info_path": "/vulnerabilities/CVE-2021-29554/57372", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'MaxPoolGradWithArgmax'. See CVE-2021-29573.", + "cve": "CVE-2021-29573", + "id": "pyup.io-57391", + "more_info_path": "/vulnerabilities/CVE-2021-29573/57391", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70220,10 +70598,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a null pointer dereference in 'EditDistance'. See CVE-2021-29564.", - "cve": "CVE-2021-29564", - "id": "pyup.io-57379", - "more_info_path": "/vulnerabilities/CVE-2021-29564/57379", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'MaxPoolGrad'. See CVE-2021-29579.", + "cve": "CVE-2021-29579", + "id": "pyup.io-57375", + "more_info_path": "/vulnerabilities/CVE-2021-29579/57375", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70234,10 +70612,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of hashtable lookup. See CVE-2021-29604.", - "cve": "CVE-2021-29604", - "id": "pyup.io-57382", - "more_info_path": "/vulnerabilities/CVE-2021-29604/57382", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a segfault in 'tf.raw_ops.SparseCountSparseOutput'. See CVE-2021-29619.", + "cve": "CVE-2021-29619", + "id": "pyup.io-57365", + "more_info_path": "/vulnerabilities/CVE-2021-29619/57365", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70248,10 +70626,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap out of bounds read in 'RequantizationRange'. See CVE-2021-29569.", - "cve": "CVE-2021-29569", - "id": "pyup.io-57387", - "more_info_path": "/vulnerabilities/CVE-2021-29569/57387", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'DepthwiseConv'. See CVE-2021-29602.", + "cve": "CVE-2021-29602", + "id": "pyup.io-57367", + "more_info_path": "/vulnerabilities/CVE-2021-29602/57367", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70262,10 +70640,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in optimized pooling implementations in TFLite. See CVE-2021-29586.", - "cve": "CVE-2021-29586", - "id": "pyup.io-57392", - "more_info_path": "/vulnerabilities/CVE-2021-29586/57392", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 update 'curl' to '7.76.0' to handle CVE-2020-8177.", + "cve": "CVE-2020-8177", + "id": "pyup.io-57369", + "more_info_path": "/vulnerabilities/CVE-2020-8177/57369", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70276,10 +70654,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a stack overflow due to looping TFLite subgraph. See CVE-2021-29591.", - "cve": "CVE-2021-29591", - "id": "pyup.io-57352", - "more_info_path": "/vulnerabilities/CVE-2021-29591/57352", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'MaxPool3DGradGrad'. See CVE-2021-29576.", + "cve": "CVE-2021-29576", + "id": "pyup.io-57377", + "more_info_path": "/vulnerabilities/CVE-2021-29576/57377", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70290,10 +70668,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'BatchToSpaceNd'. See CVE-2021-29593.", - "cve": "CVE-2021-29593", - "id": "pyup.io-57371", - "more_info_path": "/vulnerabilities/CVE-2021-29593/57371", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29618: Passing a complex argument to `tf.transpose` at the same time as passing 'conjugate=True' argument results in a crash.", + "cve": "CVE-2021-29618", + "id": "pyup.io-57402", + "more_info_path": "/vulnerabilities/CVE-2021-29618/57402", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70304,10 +70682,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB write in TFLite. See CVE-2021-29603.", - "cve": "CVE-2021-29603", - "id": "pyup.io-57381", - "more_info_path": "/vulnerabilities/CVE-2021-29603/57381", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix an overflow and a denial of service in 'tf.raw_ops.ReverseSequence'. See CVE-2021-29575.", + "cve": "CVE-2021-29575", + "id": "pyup.io-57353", + "more_info_path": "/vulnerabilities/CVE-2021-29575/57353", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70318,10 +70696,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a integer overflow in TFLite concatentation. See CVE-2021-29601.", - "cve": "CVE-2021-29601", - "id": "pyup.io-57390", - "more_info_path": "/vulnerabilities/CVE-2021-29601/57390", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a 'CHECK'-fail in 'tf.raw_ops.IRFFT'. See CVE-2021-29562.", + "cve": "CVE-2021-29562", + "id": "pyup.io-57356", + "more_info_path": "/vulnerabilities/CVE-2021-29562/57356", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70332,10 +70710,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29563: An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from the implementation of `tf.raw_ops.RFFT`. Eigen code operating on an empty matrix can trigger on an assertion and will cause program termination.", - "cve": "CVE-2021-29563", - "id": "pyup.io-57395", - "more_info_path": "/vulnerabilities/CVE-2021-29563/57395", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a stack overflow due to looping TFLite subgraph. See CVE-2021-29591.", + "cve": "CVE-2021-29591", + "id": "pyup.io-57352", + "more_info_path": "/vulnerabilities/CVE-2021-29591/57352", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70346,10 +70724,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'OneHot'. See CVE-2021-29600.", - "cve": "CVE-2021-29600", - "id": "pyup.io-57378", - "more_info_path": "/vulnerabilities/CVE-2021-29600/57378", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap out of bounds read in 'RequantizationRange'. See CVE-2021-29569.", + "cve": "CVE-2021-29569", + "id": "pyup.io-57387", + "more_info_path": "/vulnerabilities/CVE-2021-29569/57387", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70360,10 +70738,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29587: TensorFlow is an end-to-end open source platform for machine learning. The `Prepare` step of the `SpaceToDepth` TFLite operator does not check for 0 before division (https://github.com/tensorflow/tensorflow/blob/5f7975d09eac0f10ed8a17dbb6f5964977725adc/tensorflow/lite/kernels/space_to_depth.cc#L63-L67). An attacker can craft a model such that `params->block_size` would be zero.", - "cve": "CVE-2021-29587", - "id": "pyup.io-57384", - "more_info_path": "/vulnerabilities/CVE-2021-29587/57384", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a null pointer dereference in 'SparseFillEmptyRows'. See CVE-2021-29565.", + "cve": "CVE-2021-29565", + "id": "pyup.io-57360", + "more_info_path": "/vulnerabilities/CVE-2021-29565/57360", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70374,10 +70752,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by incomplete validation in 'SparseReshape'. See CVE-2021-29611.", - "cve": "CVE-2021-29611", - "id": "pyup.io-57398", - "more_info_path": "/vulnerabilities/CVE-2021-29611/57398", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29563: An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from the implementation of `tf.raw_ops.RFFT`. Eigen code operating on an empty matrix can trigger on an assertion and will cause program termination.", + "cve": "CVE-2021-29563", + "id": "pyup.io-57395", + "more_info_path": "/vulnerabilities/CVE-2021-29563/57395", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70388,10 +70766,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix an undefined behavior and a 'CHECK'-fail in 'FractionalMaxPoolGrad'. See CVE-2021-29580.", - "cve": "CVE-2021-29580", - "id": "pyup.io-57399", - "more_info_path": "/vulnerabilities/CVE-2021-29580/57399", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29560: An attacker can cause a heap buffer overflow in `tf.raw_ops.RaggedTensorToTensor`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) uses the same index to access two arrays in parallel. Since the user controls the shape of the input arguments, an attacker could trigger a heap OOB access when 'parent_output_index' is shorter than 'row_split'.", + "cve": "CVE-2021-29560", + "id": "pyup.io-57403", + "more_info_path": "/vulnerabilities/CVE-2021-29560/57403", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70402,10 +70780,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29550: An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments (https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to `GeneratePoolingSequence`(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since `output_length` can be 0, this results in runtime crashing.", - "cve": "CVE-2021-29550", - "id": "pyup.io-57400", - "more_info_path": "/vulnerabilities/CVE-2021-29550/57400", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'AvgPool3DGrad'. See CVE-2021-29577.", + "cve": "CVE-2021-29577", + "id": "pyup.io-57373", + "more_info_path": "/vulnerabilities/CVE-2021-29577/57373", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70430,10 +70808,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 updates 'curl' to '7.76.0' to handle CVE-2020-8286: curl 7.41.0 through 7.73.0 is vulnerable to an improper check for certificate revocation due to insufficient verification of the OCSP response.", - "cve": "CVE-2020-8286", - "id": "pyup.io-57370", - "more_info_path": "/vulnerabilities/CVE-2020-8286/57370", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29546: TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger an integer division by zero undefined behavior in `tf.raw_ops.QuantizedBiasAdd`. This is because the implementation of the Eigen kernel (https://github.com/tensorflow/tensorflow/blob/61bca8bd5ba8a68b2d97435ddfafcdf2b85672cd/tensorflow/core/kernels/quantization_utils.h#L812-L849) does a division by the number of elements of the smaller input (based on shape) without checking that this is not zero.", + "cve": "CVE-2021-29546", + "id": "pyup.io-57401", + "more_info_path": "/vulnerabilities/CVE-2021-29546/57401", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70444,10 +70822,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 update its dependency \"curl\" to handle CVE-2020-8284.", - "cve": "CVE-2020-8284", - "id": "pyup.io-57374", - "more_info_path": "/vulnerabilities/CVE-2020-8284/57374", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'OneHot'. See CVE-2021-29600.", + "cve": "CVE-2021-29600", + "id": "pyup.io-57378", + "more_info_path": "/vulnerabilities/CVE-2021-29600/57378", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70458,10 +70836,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29583: The implementation of 'tf.raw_ops.FusedBatchNorm' is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that 'scale', 'offset', 'mean' and 'variance' (the last two only when required) all have the same number of elements as the number of channels of 'x'. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior.", - "cve": "CVE-2021-29583", - "id": "pyup.io-57362", - "more_info_path": "/vulnerabilities/CVE-2021-29583/57362", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'QuantizedMul'. See CVE-2021-29528.", + "cve": "CVE-2021-29528", + "id": "pyup.io-57396", + "more_info_path": "/vulnerabilities/CVE-2021-29528/57396", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70472,10 +70850,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29566: An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to 'tf.raw_ops.Dilation2DBackpropInput'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array. The values for 'h_out' and 'w_out' are guaranteed to be in range for 'out_backprop' (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating 'h_in_max'/'w_in_max' and 'in_backprop'.", - "cve": "CVE-2021-29566", - "id": "pyup.io-57383", - "more_info_path": "/vulnerabilities/CVE-2021-29566/57383", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'DenseCountSparseOutput'. See CVE-2021-29554.", + "cve": "CVE-2021-29554", + "id": "pyup.io-57372", + "more_info_path": "/vulnerabilities/CVE-2021-29554/57372", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70486,10 +70864,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29571: The implementation of 'tf.raw_ops.MaxPoolGradWithArgmax' can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation (https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of 'boxes' input is 4, as required by the op (https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in 'boxes' is less than 4, accesses similar to 'tboxes(b, bb, 3)' will access data outside of bounds. Further during code execution there are also writes to these indices.", - "cve": "CVE-2021-29571", - "id": "pyup.io-57386", - "more_info_path": "/vulnerabilities/CVE-2021-29571/57386", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by incomplete validation in 'SparseReshape'. See CVE-2021-29611.", + "cve": "CVE-2021-29611", + "id": "pyup.io-57398", + "more_info_path": "/vulnerabilities/CVE-2021-29611/57398", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70500,10 +70878,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by 0 in 'MaxPoolGradWithArgmax'. See CVE-2021-29573.", - "cve": "CVE-2021-29573", - "id": "pyup.io-57391", - "more_info_path": "/vulnerabilities/CVE-2021-29573/57391", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29583: The implementation of 'tf.raw_ops.FusedBatchNorm' is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that 'scale', 'offset', 'mean' and 'variance' (the last two only when required) all have the same number of elements as the number of channels of 'x'. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior.", + "cve": "CVE-2021-29583", + "id": "pyup.io-57362", + "more_info_path": "/vulnerabilities/CVE-2021-29583/57362", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70528,10 +70906,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29616: The implementation of TrySimplify (https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc#L390-L401) has undefined behavior due to dereferencing a null pointer in corner cases that result in optimizing a node with no inputs.", - "cve": "CVE-2021-29616", - "id": "pyup.io-57361", - "more_info_path": "/vulnerabilities/CVE-2021-29616/57361", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix fixes a heap OOB read in TFLite. See CVE-2021-29606.", + "cve": "CVE-2021-29606", + "id": "pyup.io-57355", + "more_info_path": "/vulnerabilities/CVE-2021-29606/57355", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70542,10 +70920,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix vulnerabilities caused by incomplete validation in 'SparseAdd'. See CVE-2021-29609.", - "cve": "CVE-2021-29609", - "id": "pyup.io-57364", - "more_info_path": "/vulnerabilities/CVE-2021-29609/57364", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a integer overflow in TFLite memory allocation. See CVE-2021-29605.", + "cve": "CVE-2021-29605", + "id": "pyup.io-57376", + "more_info_path": "/vulnerabilities/CVE-2021-29605/57376", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70556,10 +70934,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a integer overflow in TFLite memory allocation. See CVE-2021-29605.", - "cve": "CVE-2021-29605", - "id": "pyup.io-57376", - "more_info_path": "/vulnerabilities/CVE-2021-29605/57376", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29610: The validation in 'tf.raw_ops.QuantizeAndDequantizeV2' allows invalid values for 'axis' argument:. The validation (https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74-L77) uses '||' to mix two different conditions. If 'axis_ < -1' the condition in 'OP_REQUIRES' will still be true, but this value of 'axis_' results in heap underflow. This allows attackers to read/write to other data on the heap.", + "cve": "CVE-2021-29610", + "id": "pyup.io-57404", + "more_info_path": "/vulnerabilities/CVE-2021-29610/57404", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70570,10 +70948,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29610: The validation in 'tf.raw_ops.QuantizeAndDequantizeV2' allows invalid values for 'axis' argument:. The validation (https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74-L77) uses '||' to mix two different conditions. If 'axis_ < -1' the condition in 'OP_REQUIRES' will still be true, but this value of 'axis_' results in heap underflow. This allows attackers to read/write to other data on the heap.", - "cve": "CVE-2021-29610", - "id": "pyup.io-57404", - "more_info_path": "/vulnerabilities/CVE-2021-29610/57404", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 updates 'curl' to '7.76.0' to handle CVE-2020-8286: curl 7.41.0 through 7.73.0 is vulnerable to an improper check for certificate revocation due to insufficient verification of the OCSP response.", + "cve": "CVE-2020-8286", + "id": "pyup.io-57370", + "more_info_path": "/vulnerabilities/CVE-2020-8286/57370", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70584,10 +70962,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.0, 2.4.2, 2.3.3, 2.2.3 and 2.1.4 updates its dependency \"curl\" to a secure version (7.76.0).", - "cve": "CVE-2020-8285", - "id": "pyup.io-57389", - "more_info_path": "/vulnerabilities/CVE-2020-8285/57389", + "advisory": "Intel-tensorflow-avx512 versions 2.1.4, 2.2.3, 2.3.3, 2.4.2 and 2.5.0 update its dependency \"curl\" to handle CVE-2020-8284.", + "cve": "CVE-2020-8284", + "id": "pyup.io-57374", + "more_info_path": "/vulnerabilities/CVE-2020-8284/57374", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70598,10 +70976,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 update its dependency \"curl\" to v7.76.0 to include security fixes.", - "cve": "CVE-2020-8231", - "id": "pyup.io-57368", - "more_info_path": "/vulnerabilities/CVE-2020-8231/57368", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap OOB in 'QuantizeAndDequantizeV3'. See CVE-2021-29553.", + "cve": "CVE-2021-29553", + "id": "pyup.io-57397", + "more_info_path": "/vulnerabilities/CVE-2021-29553/57397", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70612,10 +70990,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29608: TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in 'tf.raw_ops.RaggedTensorToTensor', an attacker can exploit an undefined behavior if input arguments are empty. The implementation (https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones. There are multiple 'DCHECK' validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything.", - "cve": "CVE-2021-29608", - "id": "pyup.io-57363", - "more_info_path": "/vulnerabilities/CVE-2021-29608/57363", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29566: An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to 'tf.raw_ops.Dilation2DBackpropInput'. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array. The values for 'h_out' and 'w_out' are guaranteed to be in range for 'out_backprop' (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating 'h_in_max'/'w_in_max' and 'in_backprop'.", + "cve": "CVE-2021-29566", + "id": "pyup.io-57383", + "more_info_path": "/vulnerabilities/CVE-2021-29566/57383", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70626,10 +71004,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a heap buffer overflow in 'MaxPool3DGradGrad'. See CVE-2021-29576.", - "cve": "CVE-2021-29576", - "id": "pyup.io-57377", - "more_info_path": "/vulnerabilities/CVE-2021-29576/57377", + "advisory": "Intel-tensorflow-avx512 versions 2.5.0, 2.4.2, 2.3.3, 2.2.3 and 2.1.4 updates its dependency \"curl\" to a secure version (7.76.0).", + "cve": "CVE-2020-8285", + "id": "pyup.io-57389", + "more_info_path": "/vulnerabilities/CVE-2020-8285/57389", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70640,10 +71018,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 include a fix for CVE-2021-29560: An attacker can cause a heap buffer overflow in `tf.raw_ops.RaggedTensorToTensor`. This is because the implementation (https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) uses the same index to access two arrays in parallel. Since the user controls the shape of the input arguments, an attacker could trigger a heap OOB access when 'parent_output_index' is shorter than 'row_split'.", - "cve": "CVE-2021-29560", - "id": "pyup.io-57403", - "more_info_path": "/vulnerabilities/CVE-2021-29560/57403", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 update its dependency \"curl\" to v7.76.0 to include security fixes.", + "cve": "CVE-2020-8231", + "id": "pyup.io-57368", + "more_info_path": "/vulnerabilities/CVE-2020-8231/57368", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70654,10 +71032,10 @@ "v": ">=2.5.0rc0,<2.5.0,>=2.4.0rc0,<2.4.2,>=2.3.0rc0,<2.3.3,>=2.2.0rc0,<2.2.3,>=2.1.0rc0,<2.1.4" }, { - "advisory": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in 'tf.raw_ops.ParameterizedTruncatedNormal'. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of 'shape'. If 'shape' argument is empty, then 'shape_tensor.flat()' is an empty array. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.", - "cve": "CVE-2021-29568", - "id": "pyup.io-57385", - "more_info_path": "/vulnerabilities/CVE-2021-29568/57385", + "advisory": "Intel-tensorflow-avx512 2.1.4, 2.2.3, 2.3.3, 2.4.2, and 2.5.0 fix a division by zero in TFLite's implementation of 'SVDF'. See CVE-2021-29598.", + "cve": "CVE-2021-29598", + "id": "pyup.io-57394", + "more_info_path": "/vulnerabilities/CVE-2021-29598/57394", "specs": [ ">=2.5.0rc0,<2.5.0", ">=2.4.0rc0,<2.4.2", @@ -70681,10 +71059,10 @@ "v": ">=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37688: In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer. The Tensorflow team has patched the issue in GitHub commit 15691e456c7dc9bd6be203b09765b063bf4a380c.", - "cve": "CVE-2021-37688", - "id": "pyup.io-57348", - "more_info_path": "/vulnerabilities/CVE-2021-37688/57348", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37645: In affected versions the implementation of `tf.raw_ops.QuantizeAndDequantizeV4Grad` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L126) uses the `axis` value as the size argument to `absl::InlinedVector` constructor. But, the constructor uses an unsigned type for the argument, so the implicit conversion transforms the negative value to a large integer. The Tensorflow team has patched the issue in GitHub commit 96f364a1ca3009f98980021c4b32be5fdcca33a1.", + "cve": "CVE-2021-37645", + "id": "pyup.io-57349", + "more_info_path": "/vulnerabilities/CVE-2021-37645/57349", "specs": [ ">=2.5.0rc0,<2.5.1", ">=2.4.0rc0,<2.4.3", @@ -70694,10 +71072,10 @@ "v": ">=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37645: In affected versions the implementation of `tf.raw_ops.QuantizeAndDequantizeV4Grad` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L126) uses the `axis` value as the size argument to `absl::InlinedVector` constructor. But, the constructor uses an unsigned type for the argument, so the implicit conversion transforms the negative value to a large integer. The Tensorflow team has patched the issue in GitHub commit 96f364a1ca3009f98980021c4b32be5fdcca33a1.", - "cve": "CVE-2021-37645", - "id": "pyup.io-57349", - "more_info_path": "/vulnerabilities/CVE-2021-37645/57349", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37688: In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer. The Tensorflow team has patched the issue in GitHub commit 15691e456c7dc9bd6be203b09765b063bf4a380c.", + "cve": "CVE-2021-37688", + "id": "pyup.io-57348", + "more_info_path": "/vulnerabilities/CVE-2021-37688/57348", "specs": [ ">=2.5.0rc0,<2.5.1", ">=2.4.0rc0,<2.4.3", @@ -70707,10 +71085,10 @@ "v": ">=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 2.5.1 and 2.6.0 include a fix for CVE-2021-37640: In affected versions the implementation of 'tf.raw_ops.SparseReshape' can be made to trigger an integral division by 0 exception. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L176-L181) calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements. The reshape functor (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L40-L78) blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0. The Tensorflow team has patched the issue in GitHub commit 4923de56ec94fff7770df259ab7f2288a74feb41.", - "cve": "CVE-2021-37640", - "id": "pyup.io-57347", - "more_info_path": "/vulnerabilities/CVE-2021-37640/57347", + "advisory": "Intel-tensorflow-avx512 versions 2.5.1 and 2.6.0 include a fix for CVE-2021-37692:\nIn affected versions under certain conditions, Go code can trigger a segfault in string deallocation. For string tensors, \"C.TF_TString_Dealloc\" is called during garbage collection within a finalizer function. However, tensor structure isn't checked until encoding to avoid a performance penalty. The current method for dealloc assumes that encoding succeeded, but segfaults when a string tensor is garbage collected whose encoding failed (e.g., due to mismatched dimensions). To fix this, the call to set the finalizer function is deferred until \"NewTensor\" returns and, if encoding failed for a string tensor, deallocs are determined based on bytes written. The Tensorflow team has patched the issue in GitHub commit:\nhttps://github.com/tensorflow/tensorflow/commit/8721ba96e5760c229217b594f6d2ba332beedf22\nhttps://github.com/tensorflow/tensorflow/pull/50508\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cmgw-8vpc-rc59", + "cve": "CVE-2021-37692", + "id": "pyup.io-57346", + "more_info_path": "/vulnerabilities/CVE-2021-37692/57346", "specs": [ ">=2.5.0rc0,<2.5.1", ">=2.6.0rc0,<2.6.0" @@ -70718,10 +71096,10 @@ "v": ">=2.5.0rc0,<2.5.1,>=2.6.0rc0,<2.6.0" }, { - "advisory": "Intel-tensorflow-avx512 versions 2.5.1 and 2.6.0 include a fix for CVE-2021-37692:\nIn affected versions under certain conditions, Go code can trigger a segfault in string deallocation. For string tensors, \"C.TF_TString_Dealloc\" is called during garbage collection within a finalizer function. However, tensor structure isn't checked until encoding to avoid a performance penalty. The current method for dealloc assumes that encoding succeeded, but segfaults when a string tensor is garbage collected whose encoding failed (e.g., due to mismatched dimensions). To fix this, the call to set the finalizer function is deferred until \"NewTensor\" returns and, if encoding failed for a string tensor, deallocs are determined based on bytes written. The Tensorflow team has patched the issue in GitHub commit:\nhttps://github.com/tensorflow/tensorflow/commit/8721ba96e5760c229217b594f6d2ba332beedf22\nhttps://github.com/tensorflow/tensorflow/pull/50508\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-cmgw-8vpc-rc59", - "cve": "CVE-2021-37692", - "id": "pyup.io-57346", - "more_info_path": "/vulnerabilities/CVE-2021-37692/57346", + "advisory": "Intel-tensorflow-avx512 2.5.1 and 2.6.0 include a fix for CVE-2021-37640: In affected versions the implementation of 'tf.raw_ops.SparseReshape' can be made to trigger an integral division by 0 exception. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L176-L181) calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements. The reshape functor (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L40-L78) blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0. The Tensorflow team has patched the issue in GitHub commit 4923de56ec94fff7770df259ab7f2288a74feb41.", + "cve": "CVE-2021-37640", + "id": "pyup.io-57347", + "more_info_path": "/vulnerabilities/CVE-2021-37640/57347", "specs": [ ">=2.5.0rc0,<2.5.1", ">=2.6.0rc0,<2.6.0" @@ -70742,10 +71120,10 @@ "v": ">=2.6.0a1,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37676: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in 'tf.raw_ops.SparseFillEmptyRows'. The shape inference implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/sparse_ops.cc#L608-L634) does not validate that the input arguments are not empty tensors. The Tensorflow team has patched the issue in GitHub commit 578e634b4f1c1c684d4b4294f9e5281b2133b3ed.", - "cve": "CVE-2021-37676", - "id": "pyup.io-57342", - "more_info_path": "/vulnerabilities/CVE-2021-37676/57342", + "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37668:\nIn affected versions, an attacker can cause denial of service in applications serving models using \"tf.raw_ops.UnravelIndex\" by triggering a division by 0. The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/unravel_index_op.cc#L36) does not check that the tensor subsumed by \"dims\" is not empty. Hence, if one element of \"dims\" is 0, the implementation does a division by 0. The Tensorflow team has patched the issue in GitHub commit a776040a5e7ebf76eeb7eb923bf1ae417dd4d233.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-2wmv-37vq-52g5\nhttps://github.com/tensorflow/tensorflow/commit/a776040a5e7ebf76eeb7eb923bf1ae417dd4d233", + "cve": "CVE-2021-37668", + "id": "pyup.io-57335", + "more_info_path": "/vulnerabilities/CVE-2021-37668/57335", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70755,10 +71133,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37657: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all operations of type 'tf.raw_ops.MatrixDiagV*'. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/linalg/matrix_diag_op.cc) has incomplete validation that the value of 'k' is a valid tensor. The Tensorflow team has checked that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong. The Tensorflow team has patched the issue in GitHub commit f2a673bd34f0d64b8e40a551ac78989d16daad09.", - "cve": "CVE-2021-37657", - "id": "pyup.io-57334", - "more_info_path": "/vulnerabilities/CVE-2021-37657/57334", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37685: In affected versions TFLite's 'expand_dims.cc' (https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/expand_dims.cc#L36-L50) contains a vulnerability which allows reading one element outside of bounds of heap allocated data. If 'axis' is a large negative value (e.g., '-100000'), then after the first 'if' it would still be negative. The check following the 'if' statement will pass and the 'for' loop would read one element before the start of 'input_dims.data' (when 'i = 0'). The Tensorflow team has patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257.", + "cve": "CVE-2021-37685", + "id": "pyup.io-57337", + "more_info_path": "/vulnerabilities/CVE-2021-37685/57337", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70768,10 +71146,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37661: In affected versions an attacker can cause a denial of service in 'boosted_trees_create_quantile_stream_resource' by using negative arguments. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantile_ops.cc#L96) does not validate that 'num_streams' only contains non-negative numbers. In turn, this results in using this value to allocate memory (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantiles/quantile_stream_resource.h#L31-L40). However, 'reserve' receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library. The Tensorflow team has patched the issue in GitHub commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992.", - "cve": "CVE-2021-37661", - "id": "pyup.io-57338", - "more_info_path": "/vulnerabilities/CVE-2021-37661/57338", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37657: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all operations of type 'tf.raw_ops.MatrixDiagV*'. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/linalg/matrix_diag_op.cc) has incomplete validation that the value of 'k' is a valid tensor. The Tensorflow team has checked that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong. The Tensorflow team has patched the issue in GitHub commit f2a673bd34f0d64b8e40a551ac78989d16daad09.", + "cve": "CVE-2021-37657", + "id": "pyup.io-57334", + "more_info_path": "/vulnerabilities/CVE-2021-37657/57334", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70794,10 +71172,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, { - "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37668:\nIn affected versions, an attacker can cause denial of service in applications serving models using \"tf.raw_ops.UnravelIndex\" by triggering a division by 0. The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/unravel_index_op.cc#L36) does not check that the tensor subsumed by \"dims\" is not empty. Hence, if one element of \"dims\" is 0, the implementation does a division by 0. The Tensorflow team has patched the issue in GitHub commit a776040a5e7ebf76eeb7eb923bf1ae417dd4d233.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-2wmv-37vq-52g5\nhttps://github.com/tensorflow/tensorflow/commit/a776040a5e7ebf76eeb7eb923bf1ae417dd4d233", - "cve": "CVE-2021-37668", - "id": "pyup.io-57335", - "more_info_path": "/vulnerabilities/CVE-2021-37668/57335", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37675: In affected versions most implementations of convolution operators in TensorFlow are affected by a division by 0 vulnerability where an attacker can trigger a denial of service via a crash. The shape inference implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/framework/common_shape_fns.cc#L577) is missing several validations before doing divisions and modulo operations. The Tensorflow team has patched the issue in GitHub commit 8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4.", + "cve": "CVE-2021-37675", + "id": "pyup.io-57340", + "more_info_path": "/vulnerabilities/CVE-2021-37675/57340", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70807,10 +71185,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37675: In affected versions most implementations of convolution operators in TensorFlow are affected by a division by 0 vulnerability where an attacker can trigger a denial of service via a crash. The shape inference implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/framework/common_shape_fns.cc#L577) is missing several validations before doing divisions and modulo operations. The Tensorflow team has patched the issue in GitHub commit 8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4.", - "cve": "CVE-2021-37675", - "id": "pyup.io-57340", - "more_info_path": "/vulnerabilities/CVE-2021-37675/57340", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37661: In affected versions an attacker can cause a denial of service in 'boosted_trees_create_quantile_stream_resource' by using negative arguments. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantile_ops.cc#L96) does not validate that 'num_streams' only contains non-negative numbers. In turn, this results in using this value to allocate memory (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantiles/quantile_stream_resource.h#L31-L40). However, 'reserve' receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library. The Tensorflow team has patched the issue in GitHub commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992.", + "cve": "CVE-2021-37661", + "id": "pyup.io-57338", + "more_info_path": "/vulnerabilities/CVE-2021-37661/57338", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70820,10 +71198,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37660: In affected versions an attacker can cause a floating point exception by calling inplace operations with crafted arguments that would result in a division by 0. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/inplace_ops.cc#L283) has a logic error: it should skip processing if 'x' and 'v' are empty but the code uses '||' instead of '&&'. The Tensorflow team has patched the issue in GitHub commit e86605c0a336c088b638da02135ea6f9f6753618.", - "cve": "CVE-2021-37660", - "id": "pyup.io-57344", - "more_info_path": "/vulnerabilities/CVE-2021-37660/57344", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37687: In affected versions TFLite's 'GatherNd' implementation (https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather_nd.cc#L124) does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in 'indices'. Similar issue exists in 'Gather' implementation (https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather.cc). The Tensorflow team has patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d.", + "cve": "CVE-2021-37687", + "id": "pyup.io-57341", + "more_info_path": "/vulnerabilities/CVE-2021-37687/57341", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70833,10 +71211,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, { - "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37663:\nIn affected versions, due to incomplete validation in \"tf.raw_ops.QuantizeV2\", an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that \"min_range\" and \"max_range\" both have the same non-zero number of elements. If \"axis\" is provided (i.e., not \"-1\"), then validation should check that it is a value in range for the rank of \"input\" tensor and then the lengths of \"min_range\" and \"max_range\" inputs match the \"axis\" dimension of the \"input\" tensor. The Tensorflow team has patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g25h-jr74-qp5j\nhttps://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708", - "cve": "CVE-2021-37663", - "id": "pyup.io-57336", - "more_info_path": "/vulnerabilities/CVE-2021-37663/57336", + "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37665:\nIn affected versions, due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc) does not validate the dimensions of the \"input\" tensor. A similar issue occurs in \"MklRequantizePerChannelOp\". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantize_per_channel_op.cc) does not perform full validation for all the input arguments. The Tensorflow team has patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-v82p-hv3v-p6qp\nhttps://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9\nhttps://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69", + "cve": "CVE-2021-37665", + "id": "pyup.io-57339", + "more_info_path": "/vulnerabilities/CVE-2021-37665/57339", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70846,10 +71224,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37685: In affected versions TFLite's 'expand_dims.cc' (https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/expand_dims.cc#L36-L50) contains a vulnerability which allows reading one element outside of bounds of heap allocated data. If 'axis' is a large negative value (e.g., '-100000'), then after the first 'if' it would still be negative. The check following the 'if' statement will pass and the 'for' loop would read one element before the start of 'input_dims.data' (when 'i = 0'). The Tensorflow team has patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257.", - "cve": "CVE-2021-37685", - "id": "pyup.io-57337", - "more_info_path": "/vulnerabilities/CVE-2021-37685/57337", + "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37663:\nIn affected versions, due to incomplete validation in \"tf.raw_ops.QuantizeV2\", an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that \"min_range\" and \"max_range\" both have the same non-zero number of elements. If \"axis\" is provided (i.e., not \"-1\"), then validation should check that it is a value in range for the rank of \"input\" tensor and then the lengths of \"min_range\" and \"max_range\" inputs match the \"axis\" dimension of the \"input\" tensor. The Tensorflow team has patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g25h-jr74-qp5j\nhttps://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708", + "cve": "CVE-2021-37663", + "id": "pyup.io-57336", + "more_info_path": "/vulnerabilities/CVE-2021-37663/57336", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70859,10 +71237,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, { - "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37665:\nIn affected versions, due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc) does not validate the dimensions of the \"input\" tensor. A similar issue occurs in \"MklRequantizePerChannelOp\". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantize_per_channel_op.cc) does not perform full validation for all the input arguments. The Tensorflow team has patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-v82p-hv3v-p6qp\nhttps://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9\nhttps://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69", - "cve": "CVE-2021-37665", - "id": "pyup.io-57339", - "more_info_path": "/vulnerabilities/CVE-2021-37665/57339", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37676: In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in 'tf.raw_ops.SparseFillEmptyRows'. The shape inference implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/sparse_ops.cc#L608-L634) does not validate that the input arguments are not empty tensors. The Tensorflow team has patched the issue in GitHub commit 578e634b4f1c1c684d4b4294f9e5281b2133b3ed.", + "cve": "CVE-2021-37676", + "id": "pyup.io-57342", + "more_info_path": "/vulnerabilities/CVE-2021-37676/57342", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70872,10 +71250,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37687: In affected versions TFLite's 'GatherNd' implementation (https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather_nd.cc#L124) does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in 'indices'. Similar issue exists in 'Gather' implementation (https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather.cc). The Tensorflow team has patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d.", - "cve": "CVE-2021-37687", - "id": "pyup.io-57341", - "more_info_path": "/vulnerabilities/CVE-2021-37687/57341", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37660: In affected versions an attacker can cause a floating point exception by calling inplace operations with crafted arguments that would result in a division by 0. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/inplace_ops.cc#L283) has a logic error: it should skip processing if 'x' and 'v' are empty but the code uses '||' instead of '&&'. The Tensorflow team has patched the issue in GitHub commit e86605c0a336c088b638da02135ea6f9f6753618.", + "cve": "CVE-2021-37660", + "id": "pyup.io-57344", + "more_info_path": "/vulnerabilities/CVE-2021-37660/57344", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70885,10 +71263,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.4.0rc0,<2.4.3,>=2.5.0rc0,<2.5.1" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37643: If a user does not provide a valid padding value to 'tf.raw_ops.MatrixDiagPartOp', then the code triggers a null pointer dereference (if input is empty) or produces invalid behavior, ignoring all values after the first. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L89) reads the first value from a tensor buffer without first checking that the tensor has values to read from. The Tensorflow team has patched the issue in GitHub commit 482da92095c4d48f8784b1f00dda4f81c28d2988.", - "cve": "CVE-2021-37643", - "id": "pyup.io-57328", - "more_info_path": "/vulnerabilities/CVE-2021-37643/57328", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37662: In affected versions an attacker can generate undefined behavior via a reference binding to nullptr in 'BoostedTreesCalculateBestGainsPerFeature' and similar attack can occur in 'BoostedTreesCalculateBestFeatureSplitV2'. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) does not validate the input values. The Tensorflow team has patched the issue in GitHub commit 9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad and in commit 429f009d2b2c09028647dd4bb7b3f6f414bbaad7.", + "cve": "CVE-2021-37662", + "id": "pyup.io-57327", + "more_info_path": "/vulnerabilities/CVE-2021-37662/57327", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70898,10 +71276,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37662: In affected versions an attacker can generate undefined behavior via a reference binding to nullptr in 'BoostedTreesCalculateBestGainsPerFeature' and similar attack can occur in 'BoostedTreesCalculateBestFeatureSplitV2'. The implementation (https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) does not validate the input values. The Tensorflow team has patched the issue in GitHub commit 9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad and in commit 429f009d2b2c09028647dd4bb7b3f6f414bbaad7.", - "cve": "CVE-2021-37662", - "id": "pyup.io-57327", - "more_info_path": "/vulnerabilities/CVE-2021-37662/57327", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37646: In affected versions the implementation of 'tf.raw_ops.StringNGrams' is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/string_ngrams_op.cc#L184) calls 'reserve' on a 'tstring' with a value that sometimes can be negative if user supplies negative 'ngram_widths'. The 'reserve' method calls 'TF_TString_Reserve' which has an 'unsigned long' argument for the size of the buffer. Hence, the implicit conversion transforms the negative value to a large integer. The Tensorflow team has patched the issue in GitHub commit c283e542a3f422420cfdb332414543b62fc4e4a5.", + "cve": "CVE-2021-37646", + "id": "pyup.io-57332", + "more_info_path": "/vulnerabilities/CVE-2021-37646/57332", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70911,10 +71289,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37644: In affected versions providing a negative element to 'num_elements' list argument of 'tf.raw_ops.TensorListReserve' causes the runtime to abort the process due to reallocating a 'std::vector' to have a negative number of elements. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/list_kernels.cc#L312) calls 'std::vector.resize()' with the new size controlled by input given by the user, without checking that this input is valid. The Tensorflow team has patched the issue in GitHub commit 8a6e874437670045e6c7dc6154c7412b4a2135e2.", - "cve": "CVE-2021-37644", - "id": "pyup.io-57331", - "more_info_path": "/vulnerabilities/CVE-2021-37644/57331", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37681: In affected versions the implementation of SVDF in TFLite is vulnerable to a null pointer error (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/svdf.cc#L300-L313). The 'GetVariableInput' function (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L115-L119) can return a null pointer but 'GetTensorData' assumes that the argument is always a valid tensor. Furthermore, because 'GetVariableInput' calls 'GetMutableInput' (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L82-L90) which might return 'nullptr', the 'tensor->is_variable' expression can also trigger a null pointer exception. The Tensorflow team has patched the issue in GitHub commit 5b048e87e4e55990dae6b547add4dae59f4e1c76.", + "cve": "CVE-2021-37681", + "id": "pyup.io-57330", + "more_info_path": "/vulnerabilities/CVE-2021-37681/57330", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70924,10 +71302,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37646: In affected versions the implementation of 'tf.raw_ops.StringNGrams' is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/string_ngrams_op.cc#L184) calls 'reserve' on a 'tstring' with a value that sometimes can be negative if user supplies negative 'ngram_widths'. The 'reserve' method calls 'TF_TString_Reserve' which has an 'unsigned long' argument for the size of the buffer. Hence, the implicit conversion transforms the negative value to a large integer. The Tensorflow team has patched the issue in GitHub commit c283e542a3f422420cfdb332414543b62fc4e4a5.", - "cve": "CVE-2021-37646", - "id": "pyup.io-57332", - "more_info_path": "/vulnerabilities/CVE-2021-37646/57332", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37641: In affected versions if the arguments to 'tf.raw_ops.RaggedGather' don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by 'params_nested_splits' is not an empty list of tensors. The Tensorflow team has patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373.", + "cve": "CVE-2021-37641", + "id": "pyup.io-57329", + "more_info_path": "/vulnerabilities/CVE-2021-37641/57329", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70937,10 +71315,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37684: In affected versions the implementations of pooling in TFLite are vulnerable to division by 0 errors as there are no checks for divisors not being 0. The Tensorflow team has patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695 (https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695).", - "cve": "CVE-2021-37684", - "id": "pyup.io-57326", - "more_info_path": "/vulnerabilities/CVE-2021-37684/57326", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37644: In affected versions providing a negative element to 'num_elements' list argument of 'tf.raw_ops.TensorListReserve' causes the runtime to abort the process due to reallocating a 'std::vector' to have a negative number of elements. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/list_kernels.cc#L312) calls 'std::vector.resize()' with the new size controlled by input given by the user, without checking that this input is valid. The Tensorflow team has patched the issue in GitHub commit 8a6e874437670045e6c7dc6154c7412b4a2135e2.", + "cve": "CVE-2021-37644", + "id": "pyup.io-57331", + "more_info_path": "/vulnerabilities/CVE-2021-37644/57331", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70950,10 +71328,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37641: In affected versions if the arguments to 'tf.raw_ops.RaggedGather' don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by 'params_nested_splits' is not an empty list of tensors. The Tensorflow team has patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373.", - "cve": "CVE-2021-37641", - "id": "pyup.io-57329", - "more_info_path": "/vulnerabilities/CVE-2021-37641/57329", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37684: In affected versions the implementations of pooling in TFLite are vulnerable to division by 0 errors as there are no checks for divisors not being 0. The Tensorflow team has patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695 (https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695).", + "cve": "CVE-2021-37684", + "id": "pyup.io-57326", + "more_info_path": "/vulnerabilities/CVE-2021-37684/57326", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -70963,10 +71341,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37681: In affected versions the implementation of SVDF in TFLite is vulnerable to a null pointer error (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/svdf.cc#L300-L313). The 'GetVariableInput' function (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L115-L119) can return a null pointer but 'GetTensorData' assumes that the argument is always a valid tensor. Furthermore, because 'GetVariableInput' calls 'GetMutableInput' (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L82-L90) which might return 'nullptr', the 'tensor->is_variable' expression can also trigger a null pointer exception. The Tensorflow team has patched the issue in GitHub commit 5b048e87e4e55990dae6b547add4dae59f4e1c76.", - "cve": "CVE-2021-37681", - "id": "pyup.io-57330", - "more_info_path": "/vulnerabilities/CVE-2021-37681/57330", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37643: If a user does not provide a valid padding value to 'tf.raw_ops.MatrixDiagPartOp', then the code triggers a null pointer dereference (if input is empty) or produces invalid behavior, ignoring all values after the first. The implementation (https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L89) reads the first value from a tensor buffer without first checking that the tensor has values to read from. The Tensorflow team has patched the issue in GitHub commit 482da92095c4d48f8784b1f00dda4f81c28d2988.", + "cve": "CVE-2021-37643", + "id": "pyup.io-57328", + "more_info_path": "/vulnerabilities/CVE-2021-37643/57328", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.3.0rc0,<2.3.4", @@ -71015,10 +71393,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4,>=2.5.0rc0,<2.5.1" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1, and 2.6.0 updates its dependency 'curl' to v7.77.0 to include security fixes.", - "cve": "CVE-2021-22901", - "id": "pyup.io-57315", - "more_info_path": "/vulnerabilities/CVE-2021-22901/57315", + "advisory": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a denial of service via a segmentation fault in 'tf.raw_ops.MaxPoolGrad' caused by missing validation. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/maxpooling_op.cc) misses some validation for the 'orig_input' and 'orig_output' tensors. The fixes for CVE-2021-29579 were incomplete. We have patched the issue in GitHub commit 136b51f10903e044308cf77117c0ed9871350475. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.", + "cve": "CVE-2021-37674", + "id": "pyup.io-57320", + "more_info_path": "/vulnerabilities/CVE-2021-37674/57320", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -71054,10 +71432,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37672:\nIn affected versions, an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to \"tf.raw_ops.SdcaOptimizerV2\". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/sdca_internal.cc#L320-L353) does not check that the length of \"example_labels\" is the same as the number of examples. The Tensorflow team has patched the issue in GitHub commit a4e138660270e7599793fa438cd7b2fc2ce215a6.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hj3-vjjf-f5m7\nhttps://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6", - "cve": "CVE-2021-37672", - "id": "pyup.io-57312", - "more_info_path": "/vulnerabilities/CVE-2021-37672/57312", + "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37670:\nIn affected versions, an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to \"tf.raw_ops.UpperBound\". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/searchsorted_op.cc#L85-L104) does not validate the rank of \"sorted_input\" argument. A similar issue occurs in \"tf.raw_ops.LowerBound\". The Tensorflow team has patched the issue in GitHub commit 42459e4273c2e47a3232cc16c4f4fff3b3a35c38.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9697-98pf-4rw7\nhttps://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38", + "cve": "CVE-2021-37670", + "id": "pyup.io-57313", + "more_info_path": "/vulnerabilities/CVE-2021-37670/57313", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -71067,10 +71445,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37670:\nIn affected versions, an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to \"tf.raw_ops.UpperBound\". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/searchsorted_op.cc#L85-L104) does not validate the rank of \"sorted_input\" argument. A similar issue occurs in \"tf.raw_ops.LowerBound\". The Tensorflow team has patched the issue in GitHub commit 42459e4273c2e47a3232cc16c4f4fff3b3a35c38.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-9697-98pf-4rw7\nhttps://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38", - "cve": "CVE-2021-37670", - "id": "pyup.io-57313", - "more_info_path": "/vulnerabilities/CVE-2021-37670/57313", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1, and 2.6.0 updates its dependency 'curl' to v7.77.0 to include security fixes.", + "cve": "CVE-2021-22876", + "id": "pyup.io-57317", + "more_info_path": "/vulnerabilities/CVE-2021-22876/57317", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -71080,10 +71458,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37679:\nIn affected versions it is possible to nest a \"tf.map_fn\" within another \"tf.map_fn\" call. However, if the input tensor is a \"RaggedTensor\" and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The \"t\" and \"z\" outputs should be identical, however this is not the case. The last row of \"t\" contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a \"Variant\" tensor to a \"RaggedTensor\". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. The Tensorflow team has patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g8wg-cjwc-xhhp\nhttps://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12", - "cve": "CVE-2021-37679", - "id": "pyup.io-57314", - "more_info_path": "/vulnerabilities/CVE-2021-37679/57314", + "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1, and 2.6.0 updates its dependency 'curl' to v7.77.0 to include security fixes.", + "cve": "CVE-2021-22897", + "id": "pyup.io-57316", + "more_info_path": "/vulnerabilities/CVE-2021-22897/57316", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -71093,10 +71471,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37673:\nIn affected versions, an attacker can trigger a denial of service via a \"CHECK\"-fail in \"tf.raw_ops.MapStage\". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L513) does not check that the \"key\" input is a valid non-empty tensor. The Tensorflow team has patched the issue in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-278g-rq84-9hmg\nhttps://github.com/tensorflow/tensorflow/commit/d7de67733925de196ec8863a33445b73f9562d1d", - "cve": "CVE-2021-37673", - "id": "pyup.io-57318", - "more_info_path": "/vulnerabilities/CVE-2021-37673/57318", + "advisory": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for 'tf.raw_ops.Dequantize' has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses 'axis' to select between two different values for 'minmax_rank' which is then used to retrieve tensor dimensions. However, code assumes that 'axis' can be either '-1' or a value greater than '-1', with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.", + "cve": "CVE-2021-37677", + "id": "pyup.io-57322", + "more_info_path": "/vulnerabilities/CVE-2021-37677/57322", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -71106,10 +71484,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1, and 2.6.0 updates its dependency 'curl' to v7.77.0 to include security fixes.", - "cve": "CVE-2021-22876", - "id": "pyup.io-57317", - "more_info_path": "/vulnerabilities/CVE-2021-22876/57317", + "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37669:\nIn affected versions, an attacker can cause denial of service in applications serving models using \"tf.raw_ops.NonMaxSuppressionV5\" by triggering a division by 0. The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a \"std::vector\". However, as \"std::vector::resize\" takes the size argument as a \"size_t\" and \"output_size\" is an \"int\", there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in \"CombinedNonMaxSuppression\". The Tensorflow team has patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vmjw-c2vp-p33c\nhttps://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d\nhttps://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58", + "cve": "CVE-2021-37669", + "id": "pyup.io-57321", + "more_info_path": "/vulnerabilities/CVE-2021-37669/57321", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -71120,9 +71498,9 @@ }, { "advisory": "Intel-tensorflow-avx512 2.3.4, 2.4.3, 2.5.1, and 2.6.0 updates its dependency 'curl' to v7.77.0 to include security fixes.", - "cve": "CVE-2021-22897", - "id": "pyup.io-57316", - "more_info_path": "/vulnerabilities/CVE-2021-22897/57316", + "cve": "CVE-2021-22901", + "id": "pyup.io-57315", + "more_info_path": "/vulnerabilities/CVE-2021-22901/57315", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -71132,10 +71510,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for 'tf.raw_ops.Dequantize' has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses 'axis' to select between two different values for 'minmax_rank' which is then used to retrieve tensor dimensions. However, code assumes that 'axis' can be either '-1' or a value greater than '-1', with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.", - "cve": "CVE-2021-37677", - "id": "pyup.io-57322", - "more_info_path": "/vulnerabilities/CVE-2021-37677/57322", + "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37672:\nIn affected versions, an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to \"tf.raw_ops.SdcaOptimizerV2\". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/sdca_internal.cc#L320-L353) does not check that the length of \"example_labels\" is the same as the number of examples. The Tensorflow team has patched the issue in GitHub commit a4e138660270e7599793fa438cd7b2fc2ce215a6.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hj3-vjjf-f5m7\nhttps://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6", + "cve": "CVE-2021-37672", + "id": "pyup.io-57312", + "more_info_path": "/vulnerabilities/CVE-2021-37672/57312", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -71145,10 +71523,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37669:\nIn affected versions, an attacker can cause denial of service in applications serving models using \"tf.raw_ops.NonMaxSuppressionV5\" by triggering a division by 0. The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a \"std::vector\". However, as \"std::vector::resize\" takes the size argument as a \"size_t\" and \"output_size\" is an \"int\", there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in \"CombinedNonMaxSuppression\". The Tensorflow team has patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vmjw-c2vp-p33c\nhttps://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d\nhttps://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58", - "cve": "CVE-2021-37669", - "id": "pyup.io-57321", - "more_info_path": "/vulnerabilities/CVE-2021-37669/57321", + "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37679:\nIn affected versions it is possible to nest a \"tf.map_fn\" within another \"tf.map_fn\" call. However, if the input tensor is a \"RaggedTensor\" and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The \"t\" and \"z\" outputs should be identical, however this is not the case. The last row of \"t\" contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a \"Variant\" tensor to a \"RaggedTensor\". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. The Tensorflow team has patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-g8wg-cjwc-xhhp\nhttps://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12", + "cve": "CVE-2021-37679", + "id": "pyup.io-57314", + "more_info_path": "/vulnerabilities/CVE-2021-37679/57314", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -71158,10 +71536,10 @@ "v": ">=2.6.0rc0,<2.6.0,>=2.5.0rc0,<2.5.1,>=2.4.0rc0,<2.4.3,>=2.3.0rc0,<2.3.4" }, { - "advisory": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a denial of service via a segmentation fault in 'tf.raw_ops.MaxPoolGrad' caused by missing validation. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/maxpooling_op.cc) misses some validation for the 'orig_input' and 'orig_output' tensors. The fixes for CVE-2021-29579 were incomplete. We have patched the issue in GitHub commit 136b51f10903e044308cf77117c0ed9871350475. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.", - "cve": "CVE-2021-37674", - "id": "pyup.io-57320", - "more_info_path": "/vulnerabilities/CVE-2021-37674/57320", + "advisory": "Intel-tensorflow-avx512 version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37673:\nIn affected versions, an attacker can trigger a denial of service via a \"CHECK\"-fail in \"tf.raw_ops.MapStage\". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L513) does not check that the \"key\" input is a valid non-empty tensor. The Tensorflow team has patched the issue in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-278g-rq84-9hmg\nhttps://github.com/tensorflow/tensorflow/commit/d7de67733925de196ec8863a33445b73f9562d1d", + "cve": "CVE-2021-37673", + "id": "pyup.io-57318", + "more_info_path": "/vulnerabilities/CVE-2021-37673/57318", "specs": [ ">=2.6.0rc0,<2.6.0", ">=2.5.0rc0,<2.5.1", @@ -71204,17 +71582,6 @@ ], "v": ">=2.6.0rc0,<2.6.1" }, - { - "advisory": "Intel-tensorflow-avx512 versions 2.7.1 and 2.8.0 include a fix for CVE-2022-23590: A 'GraphDef' from a TensorFlow 'SavedModel' can be maliciously altered to cause a TensorFlow process to crash due to encountering a 'StatusOr' value that is an error and forcibly extracting the value from it.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pqrv-8r2f-7278", - "cve": "CVE-2022-23590", - "id": "pyup.io-57218", - "more_info_path": "/vulnerabilities/CVE-2022-23590/57218", - "specs": [ - ">=2.7.0a0,<2.7.1", - ">=2.8.0a0,<2.8.0" - ], - "v": ">=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" - }, { "advisory": "Intel-tensorflow-avx512 is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming `GraphDef` before converting it to the MLIR-based dialect. If an attacker changes the `SavedModel` format on disk to invalidate these assumptions and the `GraphDef` is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.", "cve": "CVE-2022-23594", @@ -71227,14 +71594,15 @@ "v": ">=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { - "advisory": "Intel-tensorflow-avx512 version 2.8.0 includes a fix for CVE-2022-23592: TensorFlow's type inference can cause a heap out of bounds read as the bounds checking is done in a 'DCHECK' (which is a no-op during production). An attacker can control the 'input_idx' variable such that 'ix' would be larger than the number of values in 'node_t.args'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vq36-27g6-p492", - "cve": "CVE-2022-23592", - "id": "pyup.io-57216", - "more_info_path": "/vulnerabilities/CVE-2022-23592/57216", + "advisory": "Intel-tensorflow-avx512 versions 2.7.1 and 2.8.0 include a fix for CVE-2022-23590: A 'GraphDef' from a TensorFlow 'SavedModel' can be maliciously altered to cause a TensorFlow process to crash due to encountering a 'StatusOr' value that is an error and forcibly extracting the value from it.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-pqrv-8r2f-7278", + "cve": "CVE-2022-23590", + "id": "pyup.io-57218", + "more_info_path": "/vulnerabilities/CVE-2022-23590/57218", "specs": [ + ">=2.7.0a0,<2.7.1", ">=2.8.0a0,<2.8.0" ], - "v": ">=2.8.0a0,<2.8.0" + "v": ">=2.7.0a0,<2.7.1,>=2.8.0a0,<2.8.0" }, { "advisory": "Intel-tensorflow-avx512 2.8.0 includes a fix for CVE-2022-23593: The 'simplifyBroadcast' function in the MLIR-TFRT infrastructure in TensorFlow is vulnerable to a segfault (hence, denial of service), if called with scalar shapes. If all shapes are scalar, then 'maxRank' is 0, so we build an empty 'SmallVector'. \nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gwcx-jrx4-92w2", @@ -71246,6 +71614,16 @@ ], "v": ">=2.8.0a0,<2.8.0" }, + { + "advisory": "Intel-tensorflow-avx512 version 2.8.0 includes a fix for CVE-2022-23592: TensorFlow's type inference can cause a heap out of bounds read as the bounds checking is done in a 'DCHECK' (which is a no-op during production). An attacker can control the 'input_idx' variable such that 'ix' would be larger than the number of values in 'node_t.args'.\nhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-vq36-27g6-p492", + "cve": "CVE-2022-23592", + "id": "pyup.io-57216", + "more_info_path": "/vulnerabilities/CVE-2022-23592/57216", + "specs": [ + ">=2.8.0a0,<2.8.0" + ], + "v": ">=2.8.0a0,<2.8.0" + }, { "advisory": "TensorFlow is an open source platform for machine learning. In version 2.8.0, the 'TensorKey' hash function used total estimated 'AllocatedBytes()', which (a) is an estimate per tensor, and (b) is a very poor hash function for constants (e.g. 'int32_t'). It also tried to access individual tensor bytes through 'tensor.data()' of size 'AllocatedBytes()'. This led to ASAN failures because the 'AllocatedBytes()' is an estimate of total bytes allocated by a tensor, including any pointed-to constructs (e.g. strings), and does not refer to contiguous bytes in the '.data()' buffer. The discoverers could not use this byte vector anyway because types such as 'tstring' include pointers, whereas they needed to hash the string values themselves. This issue is patched in Tensorflow versions 2.9.0 and 2.8.1.", "cve": "CVE-2022-29210", @@ -71616,20 +71994,20 @@ "v": "<2.0.2" }, { - "advisory": "Invokeai 2.0.2 updates its dependency, protobuf, from version 3.19.4 to 3.19.6. This update was prompted by a vulnerability identified as CVE-2022-1941.\r\nhttps://github.com/invoke-ai/InvokeAI/commit/90d37eac034592cc3aed5a15a98971801b21988e", - "cve": "CVE-2022-1941", - "id": "pyup.io-63305", - "more_info_path": "/vulnerabilities/CVE-2022-1941/63305", + "advisory": "Invokeai 2.0.2 updates its dependency, pytorch-lightning, from version 1.4.2 to 1.7.7. This update was prompted by a vulnerability identified as CVE-2022-0845.\r\nhttps://github.com/invoke-ai/InvokeAI/commit/90d37eac034592cc3aed5a15a98971801b21988e", + "cve": "CVE-2022-0845", + "id": "pyup.io-63299", + "more_info_path": "/vulnerabilities/CVE-2022-0845/63299", "specs": [ "<2.0.2" ], "v": "<2.0.2" }, { - "advisory": "Invokeai 2.0.2 updates its dependency, pytorch-lightning, from version 1.4.2 to 1.7.7. This update was prompted by a vulnerability identified as CVE-2022-0845.\r\nhttps://github.com/invoke-ai/InvokeAI/commit/90d37eac034592cc3aed5a15a98971801b21988e", - "cve": "CVE-2022-0845", - "id": "pyup.io-63299", - "more_info_path": "/vulnerabilities/CVE-2022-0845/63299", + "advisory": "Invokeai 2.0.2 updates its dependency, protobuf, from version 3.19.4 to 3.19.6. This update was prompted by a vulnerability identified as CVE-2022-1941.\r\nhttps://github.com/invoke-ai/InvokeAI/commit/90d37eac034592cc3aed5a15a98971801b21988e", + "cve": "CVE-2022-1941", + "id": "pyup.io-63305", + "more_info_path": "/vulnerabilities/CVE-2022-1941/63305", "specs": [ "<2.0.2" ], @@ -72096,16 +72474,6 @@ ], "v": "<0.0.85" }, - { - "advisory": "Ipyflow 0.0.85 updates its NPM dependency 'url-parse' to v1.5.10 to include security fixes.", - "cve": "CVE-2022-0691", - "id": "pyup.io-51776", - "more_info_path": "/vulnerabilities/CVE-2022-0691/51776", - "specs": [ - "<0.0.85" - ], - "v": "<0.0.85" - }, { "advisory": "Ipyflow 0.0.85 updates its NPM dependency 'url-parse' to v1.5.10 to include security fixes.", "cve": "CVE-2022-0512", @@ -72135,6 +72503,16 @@ "<0.0.85" ], "v": "<0.0.85" + }, + { + "advisory": "Ipyflow 0.0.85 updates its NPM dependency 'url-parse' to v1.5.10 to include security fixes.", + "cve": "CVE-2022-0691", + "id": "pyup.io-51776", + "more_info_path": "/vulnerabilities/CVE-2022-0691/51776", + "specs": [ + "<0.0.85" + ], + "v": "<0.0.85" } ], "ipyhton": [ @@ -72246,20 +72624,30 @@ ], "ipyvue-remote-component": [ { - "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'ansi-regex' to v5.0.1 to include a security fix.", - "cve": "CVE-2021-3807", - "id": "pyup.io-45536", - "more_info_path": "/vulnerabilities/CVE-2021-3807/45536", + "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'node-fetch' to v2.6.7 to include a security fix.", + "cve": "CVE-2022-0235", + "id": "pyup.io-45538", + "more_info_path": "/vulnerabilities/CVE-2022-0235/45538", "specs": [ "<1.1.1" ], "v": "<1.1.1" }, { - "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'node-fetch' to v2.6.7 to include a security fix.", - "cve": "CVE-2022-0235", - "id": "pyup.io-45538", - "more_info_path": "/vulnerabilities/CVE-2022-0235/45538", + "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'path-parse' to v1.0.7 to include a security fix.", + "cve": "CVE-2021-23343", + "id": "pyup.io-45530", + "more_info_path": "/vulnerabilities/CVE-2021-23343/45530", + "specs": [ + "<1.1.1" + ], + "v": "<1.1.1" + }, + { + "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", + "cve": "CVE-2021-32804", + "id": "pyup.io-45535", + "more_info_path": "/vulnerabilities/CVE-2021-32804/45535", "specs": [ "<1.1.1" ], @@ -72285,16 +72673,6 @@ ], "v": "<1.1.1" }, - { - "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'nanoid' to v3.2.0 to include a security fix.", - "cve": "CVE-2021-23566", - "id": "pyup.io-45537", - "more_info_path": "/vulnerabilities/CVE-2021-23566/45537", - "specs": [ - "<1.1.1" - ], - "v": "<1.1.1" - }, { "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'glob-parent' to v5.1.2 to include a security fix.", "cve": "CVE-2020-28469", @@ -72306,20 +72684,20 @@ "v": "<1.1.1" }, { - "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", - "cve": "CVE-2021-37701", - "id": "pyup.io-45531", - "more_info_path": "/vulnerabilities/CVE-2021-37701/45531", + "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'ws' to v7.4.6 to include a security fix.", + "cve": "CVE-2021-32640", + "id": "pyup.io-45540", + "more_info_path": "/vulnerabilities/CVE-2021-32640/45540", "specs": [ "<1.1.1" ], "v": "<1.1.1" }, { - "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", - "cve": "CVE-2021-37712", - "id": "pyup.io-45532", - "more_info_path": "/vulnerabilities/CVE-2021-37712/45532", + "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'nanoid' to v3.2.0 to include a security fix.", + "cve": "CVE-2021-23566", + "id": "pyup.io-45537", + "more_info_path": "/vulnerabilities/CVE-2021-23566/45537", "specs": [ "<1.1.1" ], @@ -72337,29 +72715,29 @@ }, { "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", - "cve": "CVE-2021-32804", - "id": "pyup.io-45535", - "more_info_path": "/vulnerabilities/CVE-2021-32804/45535", + "cve": "CVE-2021-37712", + "id": "pyup.io-45532", + "more_info_path": "/vulnerabilities/CVE-2021-37712/45532", "specs": [ "<1.1.1" ], "v": "<1.1.1" }, { - "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'ws' to v7.4.6 to include a security fix.", - "cve": "CVE-2021-32640", - "id": "pyup.io-45540", - "more_info_path": "/vulnerabilities/CVE-2021-32640/45540", + "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", + "cve": "CVE-2021-37701", + "id": "pyup.io-45531", + "more_info_path": "/vulnerabilities/CVE-2021-37701/45531", "specs": [ "<1.1.1" ], "v": "<1.1.1" }, { - "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'path-parse' to v1.0.7 to include a security fix.", - "cve": "CVE-2021-23343", - "id": "pyup.io-45530", - "more_info_path": "/vulnerabilities/CVE-2021-23343/45530", + "advisory": "Ipyvue-remote-component 1.1.1 updates its NPM dependency 'ansi-regex' to v5.0.1 to include a security fix.", + "cve": "CVE-2021-3807", + "id": "pyup.io-45536", + "more_info_path": "/vulnerabilities/CVE-2021-3807/45536", "specs": [ "<1.1.1" ], @@ -72378,40 +72756,40 @@ "v": "<1.0.2" }, { - "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'glob-parent' to v5.1.2 to include a security fix.", - "cve": "CVE-2020-28469", - "id": "pyup.io-45550", - "more_info_path": "/vulnerabilities/CVE-2020-28469/45550", + "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'ws' to v7.4.6 to include a security fix.", + "cve": "CVE-2021-32640", + "id": "pyup.io-45512", + "more_info_path": "/vulnerabilities/CVE-2021-32640/45512", "specs": [ "<1.0.2" ], "v": "<1.0.2" }, { - "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'nanoid' to v3.2.0 to include a security fix.", - "cve": "CVE-2021-23566", - "id": "pyup.io-45542", - "more_info_path": "/vulnerabilities/CVE-2021-23566/45542", + "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'normalize-url' to v4.5.1 to include a security fix.", + "cve": "CVE-2021-33502", + "id": "pyup.io-45551", + "more_info_path": "/vulnerabilities/CVE-2021-33502/45551", "specs": [ "<1.0.2" ], "v": "<1.0.2" }, { - "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'path-parse' to v1.0.7 to include a security fix.", - "cve": "CVE-2021-23343", - "id": "pyup.io-45549", - "more_info_path": "/vulnerabilities/CVE-2021-23343/45549", + "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'glob-parent' to v5.1.2 to include a security fix.", + "cve": "CVE-2020-28469", + "id": "pyup.io-45550", + "more_info_path": "/vulnerabilities/CVE-2020-28469/45550", "specs": [ "<1.0.2" ], "v": "<1.0.2" }, { - "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'normalize-url' to v4.5.1 to include a security fix.", - "cve": "CVE-2021-33502", - "id": "pyup.io-45551", - "more_info_path": "/vulnerabilities/CVE-2021-33502/45551", + "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", + "cve": "CVE-2021-32804", + "id": "pyup.io-45544", + "more_info_path": "/vulnerabilities/CVE-2021-32804/45544", "specs": [ "<1.0.2" ], @@ -72419,9 +72797,9 @@ }, { "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", - "cve": "CVE-2021-37701", - "id": "pyup.io-45548", - "more_info_path": "/vulnerabilities/CVE-2021-37701/45548", + "cve": "CVE-2021-32803", + "id": "pyup.io-45545", + "more_info_path": "/vulnerabilities/CVE-2021-32803/45545", "specs": [ "<1.0.2" ], @@ -72438,50 +72816,50 @@ "v": "<1.0.2" }, { - "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", - "cve": "CVE-2021-32804", - "id": "pyup.io-45544", - "more_info_path": "/vulnerabilities/CVE-2021-32804/45544", + "advisory": "Ipyvue-time-series 1.0.2 updates more NPM dependencies to include security fixes.", + "cve": "PVE-2022-45552", + "id": "pyup.io-45552", + "more_info_path": "/vulnerabilities/PVE-2022-45552/45552", "specs": [ "<1.0.2" ], "v": "<1.0.2" }, { - "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", - "cve": "CVE-2021-32803", - "id": "pyup.io-45545", - "more_info_path": "/vulnerabilities/CVE-2021-32803/45545", + "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'ansi-regex' to v5.0.1 to include a security fix.", + "cve": "CVE-2021-3807", + "id": "pyup.io-45543", + "more_info_path": "/vulnerabilities/CVE-2021-3807/45543", "specs": [ "<1.0.2" ], "v": "<1.0.2" }, { - "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", - "cve": "CVE-2021-37713", - "id": "pyup.io-45546", - "more_info_path": "/vulnerabilities/CVE-2021-37713/45546", + "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'ws' to v7.4.6 to include a security fix.", + "cve": "CVE-2021-32640", + "id": "pyup.io-45539", + "more_info_path": "/vulnerabilities/CVE-2021-32640/45539", "specs": [ "<1.0.2" ], "v": "<1.0.2" }, { - "advisory": "Ipyvue-time-series 1.0.2 updates more NPM dependencies to include security fixes.", - "cve": "PVE-2022-45552", - "id": "pyup.io-45552", - "more_info_path": "/vulnerabilities/PVE-2022-45552/45552", + "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'path-parse' to v1.0.7 to include a security fix.", + "cve": "CVE-2021-23343", + "id": "pyup.io-45549", + "more_info_path": "/vulnerabilities/CVE-2021-23343/45549", "specs": [ "<1.0.2" ], "v": "<1.0.2" }, { - "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'ansi-regex' to v5.0.1 to include a security fix.", - "cve": "CVE-2021-3807", - "id": "pyup.io-45543", - "more_info_path": "/vulnerabilities/CVE-2021-3807/45543", + "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'nanoid' to v3.2.0 to include a security fix.", + "cve": "CVE-2021-23566", + "id": "pyup.io-45542", + "more_info_path": "/vulnerabilities/CVE-2021-23566/45542", "specs": [ "<1.0.2" ], @@ -72489,29 +72867,29 @@ }, { "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", - "cve": "CVE-2021-37712", - "id": "pyup.io-45547", - "more_info_path": "/vulnerabilities/CVE-2021-37712/45547", + "cve": "CVE-2021-37713", + "id": "pyup.io-45546", + "more_info_path": "/vulnerabilities/CVE-2021-37713/45546", "specs": [ "<1.0.2" ], "v": "<1.0.2" }, { - "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'ws' to v7.4.6 to include a security fix.", - "cve": "CVE-2021-32640", - "id": "pyup.io-45539", - "more_info_path": "/vulnerabilities/CVE-2021-32640/45539", + "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", + "cve": "CVE-2021-37712", + "id": "pyup.io-45547", + "more_info_path": "/vulnerabilities/CVE-2021-37712/45547", "specs": [ "<1.0.2" ], "v": "<1.0.2" }, { - "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'ws' to v7.4.6 to include a security fix.", - "cve": "CVE-2021-32640", - "id": "pyup.io-45512", - "more_info_path": "/vulnerabilities/CVE-2021-32640/45512", + "advisory": "Ipyvue-time-series 1.0.2 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", + "cve": "CVE-2021-37701", + "id": "pyup.io-45548", + "more_info_path": "/vulnerabilities/CVE-2021-37701/45548", "specs": [ "<1.0.2" ], @@ -72550,6 +72928,19 @@ "v": ">=5.0.0,<5.1.5" } ], + "irasim": [ + { + "advisory": "The OpenSSF Package Analysis project has identified the PyPI package 'irasim' version 99.6 as malicious because it communicates with a domain associated with malicious activity, indicating potential security risks.", + "cve": "PVE-2024-74241", + "id": "pyup.io-74241", + "more_info_path": "/vulnerabilities/PVE-2024-74241/74241", + "specs": [ + ">=0", + "<=0" + ], + "v": ">=0,<=0" + } + ], "irc3": [ { "advisory": "irc3 before 0.4.4 is vulnerable to several undisclosed security issues.", @@ -72917,20 +73308,20 @@ "v": "<0.2.70" }, { - "advisory": "Jake 3.0.2 updates the package dependency urllib3 from 2.0.2 to 2.0.6 to fix CVE-2023-45803 and CVE-2023-45804 on its dependency.\r\nhttps://github.com/sonatype-nexus-community/jake/pull/144/files\r\nhttps://nvd.nist.gov/vuln/detail/CVE-2023-45803", - "cve": "CVE-2023-45803", - "id": "pyup.io-62749", - "more_info_path": "/vulnerabilities/CVE-2023-45803/62749", + "advisory": "Jake 3.0.2 updates the package dependency urllib3 from 2.0.2 to 2.0.6 to fix CVE-2023-45804 on its dependency.\r\nhttps://github.com/sonatype-nexus-community/jake/pull/144/files\r\nhttps://nvd.nist.gov/vuln/detail/CVE-2023-43804", + "cve": "CVE-2023-45804", + "id": "pyup.io-63000", + "more_info_path": "/vulnerabilities/CVE-2023-45804/63000", "specs": [ "<3.0.2" ], "v": "<3.0.2" }, { - "advisory": "Jake 3.0.2 updates the package dependency urllib3 from 2.0.2 to 2.0.6 to fix CVE-2023-45804 on its dependency.\r\nhttps://github.com/sonatype-nexus-community/jake/pull/144/files\r\nhttps://nvd.nist.gov/vuln/detail/CVE-2023-43804", - "cve": "CVE-2023-45804", - "id": "pyup.io-63000", - "more_info_path": "/vulnerabilities/CVE-2023-45804/63000", + "advisory": "Jake 3.0.2 updates the package dependency urllib3 from 2.0.2 to 2.0.6 to fix CVE-2023-45803 and CVE-2023-45804 on its dependency.\r\nhttps://github.com/sonatype-nexus-community/jake/pull/144/files\r\nhttps://nvd.nist.gov/vuln/detail/CVE-2023-45803", + "cve": "CVE-2023-45803", + "id": "pyup.io-62749", + "more_info_path": "/vulnerabilities/CVE-2023-45803/62749", "specs": [ "<3.0.2" ], @@ -73056,20 +73447,20 @@ ], "jetson-stats": [ { - "advisory": "Jetson-stats 4.2.4 updates its dependency 'pillow' to include a security fix.", - "cve": "CVE-2023-4863", - "id": "pyup.io-64435", - "more_info_path": "/vulnerabilities/CVE-2023-4863/64435", + "advisory": "Jetson-stats 4.2.4 updates its dependency 'tornado' to include a security fix.", + "cve": "PVE-2023-99925", + "id": "pyup.io-64409", + "more_info_path": "/vulnerabilities/PVE-2023-99925/64409", "specs": [ "<4.2.4" ], "v": "<4.2.4" }, { - "advisory": "Jetson-stats 4.2.4 updates its dependency 'tornado' to include a security fix.", - "cve": "PVE-2023-99925", - "id": "pyup.io-64409", - "more_info_path": "/vulnerabilities/PVE-2023-99925/64409", + "advisory": "Jetson-stats 4.2.4 updates its dependency 'pillow' to include a security fix.", + "cve": "CVE-2023-4863", + "id": "pyup.io-64435", + "more_info_path": "/vulnerabilities/CVE-2023-4863/64435", "specs": [ "<4.2.4" ], @@ -73091,9 +73482,9 @@ "jina": [ { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29521", - "id": "pyup.io-44067", - "more_info_path": "/vulnerabilities/CVE-2021-29521/44067", + "cve": "CVE-2021-29538", + "id": "pyup.io-44093", + "more_info_path": "/vulnerabilities/CVE-2021-29538/44093", "specs": [ "<2.0.0" ], @@ -73101,9 +73492,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29595", - "id": "pyup.io-44113", - "more_info_path": "/vulnerabilities/CVE-2021-29595/44113", + "cve": "CVE-2021-29612", + "id": "pyup.io-44134", + "more_info_path": "/vulnerabilities/CVE-2021-29612/44134", "specs": [ "<2.0.0" ], @@ -73111,9 +73502,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29566", - "id": "pyup.io-44079", - "more_info_path": "/vulnerabilities/CVE-2021-29566/44079", + "cve": "CVE-2021-29585", + "id": "pyup.io-44106", + "more_info_path": "/vulnerabilities/CVE-2021-29585/44106", "specs": [ "<2.0.0" ], @@ -73121,9 +73512,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29549", - "id": "pyup.io-44136", - "more_info_path": "/vulnerabilities/CVE-2021-29549/44136", + "cve": "CVE-2021-29552", + "id": "pyup.io-44142", + "more_info_path": "/vulnerabilities/CVE-2021-29552/44142", "specs": [ "<2.0.0" ], @@ -73131,9 +73522,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29560", - "id": "pyup.io-44081", - "more_info_path": "/vulnerabilities/CVE-2021-29560/44081", + "cve": "CVE-2021-29614", + "id": "pyup.io-44173", + "more_info_path": "/vulnerabilities/CVE-2021-29614/44173", "specs": [ "<2.0.0" ], @@ -73141,9 +73532,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29529", - "id": "pyup.io-44119", - "more_info_path": "/vulnerabilities/CVE-2021-29529/44119", + "cve": "CVE-2021-29583", + "id": "pyup.io-44104", + "more_info_path": "/vulnerabilities/CVE-2021-29583/44104", "specs": [ "<2.0.0" ], @@ -73151,9 +73542,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29579", - "id": "pyup.io-44101", - "more_info_path": "/vulnerabilities/CVE-2021-29579/44101", + "cve": "CVE-2021-29617", + "id": "pyup.io-44139", + "more_info_path": "/vulnerabilities/CVE-2021-29617/44139", "specs": [ "<2.0.0" ], @@ -73161,9 +73552,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29586", - "id": "pyup.io-44107", - "more_info_path": "/vulnerabilities/CVE-2021-29586/44107", + "cve": "CVE-2021-29616", + "id": "pyup.io-44071", + "more_info_path": "/vulnerabilities/CVE-2021-29616/44071", "specs": [ "<2.0.0" ], @@ -73171,9 +73562,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29512", - "id": "pyup.io-44088", - "more_info_path": "/vulnerabilities/CVE-2021-29512/44088", + "cve": "CVE-2021-29615", + "id": "pyup.io-44138", + "more_info_path": "/vulnerabilities/CVE-2021-29615/44138", "specs": [ "<2.0.0" ], @@ -73181,9 +73572,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29543", - "id": "pyup.io-44072", - "more_info_path": "/vulnerabilities/CVE-2021-29543/44072", + "cve": "CVE-2021-29588", + "id": "pyup.io-44108", + "more_info_path": "/vulnerabilities/CVE-2021-29588/44108", "specs": [ "<2.0.0" ], @@ -73191,9 +73582,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29551", - "id": "pyup.io-44096", - "more_info_path": "/vulnerabilities/CVE-2021-29551/44096", + "cve": "CVE-2021-29533", + "id": "pyup.io-44089", + "more_info_path": "/vulnerabilities/CVE-2021-29533/44089", "specs": [ "<2.0.0" ], @@ -73201,9 +73592,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29598", - "id": "pyup.io-44122", - "more_info_path": "/vulnerabilities/CVE-2021-29598/44122", + "cve": "CVE-2021-29607", + "id": "pyup.io-44131", + "more_info_path": "/vulnerabilities/CVE-2021-29607/44131", "specs": [ "<2.0.0" ], @@ -73211,9 +73602,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29541", - "id": "pyup.io-44130", - "more_info_path": "/vulnerabilities/CVE-2021-29541/44130", + "cve": "CVE-2021-29551", + "id": "pyup.io-44096", + "more_info_path": "/vulnerabilities/CVE-2021-29551/44096", "specs": [ "<2.0.0" ], @@ -73221,9 +73612,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29553", - "id": "pyup.io-44141", - "more_info_path": "/vulnerabilities/CVE-2021-29553/44141", + "cve": "CVE-2021-29606", + "id": "pyup.io-44128", + "more_info_path": "/vulnerabilities/CVE-2021-29606/44128", "specs": [ "<2.0.0" ], @@ -73231,9 +73622,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29615", - "id": "pyup.io-44138", - "more_info_path": "/vulnerabilities/CVE-2021-29615/44138", + "cve": "CVE-2021-29580", + "id": "pyup.io-44099", + "more_info_path": "/vulnerabilities/CVE-2021-29580/44099", "specs": [ "<2.0.0" ], @@ -73241,9 +73632,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29552", - "id": "pyup.io-44144", - "more_info_path": "/vulnerabilities/CVE-2021-29552/44144", + "cve": "CVE-2021-29598", + "id": "pyup.io-44122", + "more_info_path": "/vulnerabilities/CVE-2021-29598/44122", "specs": [ "<2.0.0" ], @@ -73251,9 +73642,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29597", - "id": "pyup.io-44120", - "more_info_path": "/vulnerabilities/CVE-2021-29597/44120", + "cve": "CVE-2021-29596", + "id": "pyup.io-44115", + "more_info_path": "/vulnerabilities/CVE-2021-29596/44115", "specs": [ "<2.0.0" ], @@ -73261,9 +73652,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29592", - "id": "pyup.io-44111", - "more_info_path": "/vulnerabilities/CVE-2021-29592/44111", + "cve": "CVE-2021-29595", + "id": "pyup.io-44113", + "more_info_path": "/vulnerabilities/CVE-2021-29595/44113", "specs": [ "<2.0.0" ], @@ -73271,9 +73662,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29520", - "id": "pyup.io-44065", - "more_info_path": "/vulnerabilities/CVE-2021-29520/44065", + "cve": "CVE-2021-29586", + "id": "pyup.io-44107", + "more_info_path": "/vulnerabilities/CVE-2021-29586/44107", "specs": [ "<2.0.0" ], @@ -73281,9 +73672,19 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29558", - "id": "pyup.io-44152", - "more_info_path": "/vulnerabilities/CVE-2021-29558/44152", + "cve": "CVE-2021-29608", + "id": "pyup.io-44095", + "more_info_path": "/vulnerabilities/CVE-2021-29608/44095", + "specs": [ + "<2.0.0" + ], + "v": "<2.0.0" + }, + { + "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", + "cve": "CVE-2021-29584", + "id": "pyup.io-44105", + "more_info_path": "/vulnerabilities/CVE-2021-29584/44105", "specs": [ "<2.0.0" ], @@ -73301,9 +73702,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29606", - "id": "pyup.io-44128", - "more_info_path": "/vulnerabilities/CVE-2021-29606/44128", + "cve": "CVE-2021-29582", + "id": "pyup.io-44103", + "more_info_path": "/vulnerabilities/CVE-2021-29582/44103", "specs": [ "<2.0.0" ], @@ -73311,9 +73712,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29583", - "id": "pyup.io-44104", - "more_info_path": "/vulnerabilities/CVE-2021-29583/44104", + "cve": "CVE-2021-29603", + "id": "pyup.io-44126", + "more_info_path": "/vulnerabilities/CVE-2021-29603/44126", "specs": [ "<2.0.0" ], @@ -73321,9 +73722,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29574", - "id": "pyup.io-44168", - "more_info_path": "/vulnerabilities/CVE-2021-29574/44168", + "cve": "CVE-2021-29579", + "id": "pyup.io-44101", + "more_info_path": "/vulnerabilities/CVE-2021-29579/44101", "specs": [ "<2.0.0" ], @@ -73331,9 +73732,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29608", - "id": "pyup.io-44095", - "more_info_path": "/vulnerabilities/CVE-2021-29608/44095", + "cve": "CVE-2021-29592", + "id": "pyup.io-44111", + "more_info_path": "/vulnerabilities/CVE-2021-29592/44111", "specs": [ "<2.0.0" ], @@ -73341,9 +73742,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29523", - "id": "pyup.io-44068", - "more_info_path": "/vulnerabilities/CVE-2021-29523/44068", + "cve": "CVE-2021-29520", + "id": "pyup.io-44065", + "more_info_path": "/vulnerabilities/CVE-2021-29520/44065", "specs": [ "<2.0.0" ], @@ -73351,9 +73752,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29568", - "id": "pyup.io-44078", - "more_info_path": "/vulnerabilities/CVE-2021-29568/44078", + "cve": "CVE-2021-29573", + "id": "pyup.io-44167", + "more_info_path": "/vulnerabilities/CVE-2021-29573/44167", "specs": [ "<2.0.0" ], @@ -73361,9 +73762,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29539", - "id": "pyup.io-41713", - "more_info_path": "/vulnerabilities/CVE-2021-29539/41713", + "cve": "CVE-2021-29571", + "id": "pyup.io-44170", + "more_info_path": "/vulnerabilities/CVE-2021-29571/44170", "specs": [ "<2.0.0" ], @@ -73381,9 +73782,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29546", - "id": "pyup.io-44135", - "more_info_path": "/vulnerabilities/CVE-2021-29546/44135", + "cve": "CVE-2021-29576", + "id": "pyup.io-44169", + "more_info_path": "/vulnerabilities/CVE-2021-29576/44169", "specs": [ "<2.0.0" ], @@ -73391,9 +73792,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29594", - "id": "pyup.io-44112", - "more_info_path": "/vulnerabilities/CVE-2021-29594/44112", + "cve": "CVE-2021-29575", + "id": "pyup.io-44165", + "more_info_path": "/vulnerabilities/CVE-2021-29575/44165", "specs": [ "<2.0.0" ], @@ -73401,9 +73802,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29561", - "id": "pyup.io-44162", - "more_info_path": "/vulnerabilities/CVE-2021-29561/44162", + "cve": "CVE-2021-29568", + "id": "pyup.io-44078", + "more_info_path": "/vulnerabilities/CVE-2021-29568/44078", "specs": [ "<2.0.0" ], @@ -73411,9 +73812,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29570", - "id": "pyup.io-44164", - "more_info_path": "/vulnerabilities/CVE-2021-29570/44164", + "cve": "CVE-2021-29566", + "id": "pyup.io-44079", + "more_info_path": "/vulnerabilities/CVE-2021-29566/44079", "specs": [ "<2.0.0" ], @@ -73421,9 +73822,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29589", - "id": "pyup.io-44087", - "more_info_path": "/vulnerabilities/CVE-2021-29589/44087", + "cve": "CVE-2021-29565", + "id": "pyup.io-44154", + "more_info_path": "/vulnerabilities/CVE-2021-29565/44154", "specs": [ "<2.0.0" ], @@ -73431,9 +73832,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29542", - "id": "pyup.io-44129", - "more_info_path": "/vulnerabilities/CVE-2021-29542/44129", + "cve": "CVE-2021-29560", + "id": "pyup.io-44081", + "more_info_path": "/vulnerabilities/CVE-2021-29560/44081", "specs": [ "<2.0.0" ], @@ -73441,9 +73842,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29535", - "id": "pyup.io-44090", - "more_info_path": "/vulnerabilities/CVE-2021-29535/44090", + "cve": "CVE-2021-29562", + "id": "pyup.io-44163", + "more_info_path": "/vulnerabilities/CVE-2021-29562/44163", "specs": [ "<2.0.0" ], @@ -73451,9 +73852,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29538", - "id": "pyup.io-44093", - "more_info_path": "/vulnerabilities/CVE-2021-29538/44093", + "cve": "CVE-2021-29561", + "id": "pyup.io-44162", + "more_info_path": "/vulnerabilities/CVE-2021-29561/44162", "specs": [ "<2.0.0" ], @@ -73461,9 +73862,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29545", - "id": "pyup.io-44083", - "more_info_path": "/vulnerabilities/CVE-2021-29545/44083", + "cve": "CVE-2021-29557", + "id": "pyup.io-44149", + "more_info_path": "/vulnerabilities/CVE-2021-29557/44149", "specs": [ "<2.0.0" ], @@ -73471,9 +73872,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29534", - "id": "pyup.io-44075", - "more_info_path": "/vulnerabilities/CVE-2021-29534/44075", + "cve": "CVE-2021-29550", + "id": "pyup.io-44082", + "more_info_path": "/vulnerabilities/CVE-2021-29550/44082", "specs": [ "<2.0.0" ], @@ -73481,9 +73882,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29516", - "id": "pyup.io-44158", - "more_info_path": "/vulnerabilities/CVE-2021-29516/44158", + "cve": "CVE-2021-29556", + "id": "pyup.io-44151", + "more_info_path": "/vulnerabilities/CVE-2021-29556/44151", "specs": [ "<2.0.0" ], @@ -73491,9 +73892,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29584", - "id": "pyup.io-44105", - "more_info_path": "/vulnerabilities/CVE-2021-29584/44105", + "cve": "CVE-2021-29549", + "id": "pyup.io-44136", + "more_info_path": "/vulnerabilities/CVE-2021-29549/44136", "specs": [ "<2.0.0" ], @@ -73501,9 +73902,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29530", - "id": "pyup.io-44118", - "more_info_path": "/vulnerabilities/CVE-2021-29530/44118", + "cve": "CVE-2021-29555", + "id": "pyup.io-44147", + "more_info_path": "/vulnerabilities/CVE-2021-29555/44147", "specs": [ "<2.0.0" ], @@ -73511,9 +73912,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29518", - "id": "pyup.io-44156", - "more_info_path": "/vulnerabilities/CVE-2021-29518/44156", + "cve": "CVE-2021-29553", + "id": "pyup.io-44141", + "more_info_path": "/vulnerabilities/CVE-2021-29553/44141", "specs": [ "<2.0.0" ], @@ -73521,9 +73922,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29614", - "id": "pyup.io-44173", - "more_info_path": "/vulnerabilities/CVE-2021-29614/44173", + "cve": "CVE-2021-29545", + "id": "pyup.io-44083", + "more_info_path": "/vulnerabilities/CVE-2021-29545/44083", "specs": [ "<2.0.0" ], @@ -73531,9 +73932,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29532", - "id": "pyup.io-44070", - "more_info_path": "/vulnerabilities/CVE-2021-29532/44070", + "cve": "CVE-2021-29546", + "id": "pyup.io-44135", + "more_info_path": "/vulnerabilities/CVE-2021-29546/44135", "specs": [ "<2.0.0" ], @@ -73541,9 +73942,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29524", - "id": "pyup.io-44064", - "more_info_path": "/vulnerabilities/CVE-2021-29524/44064", + "cve": "CVE-2021-29619", + "id": "pyup.io-44143", + "more_info_path": "/vulnerabilities/CVE-2021-29619/44143", "specs": [ "<2.0.0" ], @@ -73551,9 +73952,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29552", - "id": "pyup.io-44142", - "more_info_path": "/vulnerabilities/CVE-2021-29552/44142", + "cve": "CVE-2021-29543", + "id": "pyup.io-44072", + "more_info_path": "/vulnerabilities/CVE-2021-29543/44072", "specs": [ "<2.0.0" ], @@ -73561,9 +73962,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29619", - "id": "pyup.io-44143", - "more_info_path": "/vulnerabilities/CVE-2021-29619/44143", + "cve": "CVE-2021-29542", + "id": "pyup.io-44129", + "more_info_path": "/vulnerabilities/CVE-2021-29542/44129", "specs": [ "<2.0.0" ], @@ -73571,9 +73972,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29616", - "id": "pyup.io-44071", - "more_info_path": "/vulnerabilities/CVE-2021-29616/44071", + "cve": "CVE-2021-29541", + "id": "pyup.io-44130", + "more_info_path": "/vulnerabilities/CVE-2021-29541/44130", "specs": [ "<2.0.0" ], @@ -73581,9 +73982,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29557", - "id": "pyup.io-44149", - "more_info_path": "/vulnerabilities/CVE-2021-29557/44149", + "cve": "CVE-2021-29518", + "id": "pyup.io-44156", + "more_info_path": "/vulnerabilities/CVE-2021-29518/44156", "specs": [ "<2.0.0" ], @@ -73591,9 +73992,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29554", - "id": "pyup.io-44145", - "more_info_path": "/vulnerabilities/CVE-2021-29554/44145", + "cve": "CVE-2021-29524", + "id": "pyup.io-44064", + "more_info_path": "/vulnerabilities/CVE-2021-29524/44064", "specs": [ "<2.0.0" ], @@ -73601,9 +74002,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29617", - "id": "pyup.io-44139", - "more_info_path": "/vulnerabilities/CVE-2021-29617/44139", + "cve": "CVE-2021-29593", + "id": "pyup.io-44114", + "more_info_path": "/vulnerabilities/CVE-2021-29593/44114", "specs": [ "<2.0.0" ], @@ -73611,9 +74012,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29577", - "id": "pyup.io-44171", - "more_info_path": "/vulnerabilities/CVE-2021-29577/44171", + "cve": "CVE-2021-29554", + "id": "pyup.io-44145", + "more_info_path": "/vulnerabilities/CVE-2021-29554/44145", "specs": [ "<2.0.0" ], @@ -73621,9 +74022,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29533", - "id": "pyup.io-44089", - "more_info_path": "/vulnerabilities/CVE-2021-29533/44089", + "cve": "CVE-2021-29517", + "id": "pyup.io-44160", + "more_info_path": "/vulnerabilities/CVE-2021-29517/44160", "specs": [ "<2.0.0" ], @@ -73631,9 +74032,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29612", - "id": "pyup.io-44134", - "more_info_path": "/vulnerabilities/CVE-2021-29612/44134", + "cve": "CVE-2021-29605", + "id": "pyup.io-44127", + "more_info_path": "/vulnerabilities/CVE-2021-29605/44127", "specs": [ "<2.0.0" ], @@ -73651,9 +74052,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29517", - "id": "pyup.io-44160", - "more_info_path": "/vulnerabilities/CVE-2021-29517/44160", + "cve": "CVE-2021-29528", + "id": "pyup.io-44117", + "more_info_path": "/vulnerabilities/CVE-2021-29528/44117", "specs": [ "<2.0.0" ], @@ -73661,9 +74062,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29605", - "id": "pyup.io-44127", - "more_info_path": "/vulnerabilities/CVE-2021-29605/44127", + "cve": "CVE-2021-29537", + "id": "pyup.io-44084", + "more_info_path": "/vulnerabilities/CVE-2021-29537/44084", "specs": [ "<2.0.0" ], @@ -73671,9 +74072,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29601", - "id": "pyup.io-44125", - "more_info_path": "/vulnerabilities/CVE-2021-29601/44125", + "cve": "CVE-2021-29534", + "id": "pyup.io-44075", + "more_info_path": "/vulnerabilities/CVE-2021-29534/44075", "specs": [ "<2.0.0" ], @@ -73681,9 +74082,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29593", - "id": "pyup.io-44114", - "more_info_path": "/vulnerabilities/CVE-2021-29593/44114", + "cve": "CVE-2021-29540", + "id": "pyup.io-44094", + "more_info_path": "/vulnerabilities/CVE-2021-29540/44094", "specs": [ "<2.0.0" ], @@ -73691,9 +74092,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29576", - "id": "pyup.io-44169", - "more_info_path": "/vulnerabilities/CVE-2021-29576/44169", + "cve": "CVE-2021-29539", + "id": "pyup.io-41713", + "more_info_path": "/vulnerabilities/CVE-2021-29539/41713", "specs": [ "<2.0.0" ], @@ -73701,9 +74102,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29528", - "id": "pyup.io-44117", - "more_info_path": "/vulnerabilities/CVE-2021-29528/44117", + "cve": "CVE-2021-29577", + "id": "pyup.io-44171", + "more_info_path": "/vulnerabilities/CVE-2021-29577/44171", "specs": [ "<2.0.0" ], @@ -73711,9 +74112,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29522", - "id": "pyup.io-44063", - "more_info_path": "/vulnerabilities/CVE-2021-29522/44063", + "cve": "CVE-2021-29601", + "id": "pyup.io-44125", + "more_info_path": "/vulnerabilities/CVE-2021-29601/44125", "specs": [ "<2.0.0" ], @@ -73721,9 +74122,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29596", - "id": "pyup.io-44115", - "more_info_path": "/vulnerabilities/CVE-2021-29596/44115", + "cve": "CVE-2021-29535", + "id": "pyup.io-44090", + "more_info_path": "/vulnerabilities/CVE-2021-29535/44090", "specs": [ "<2.0.0" ], @@ -73731,9 +74132,39 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29573", - "id": "pyup.io-44167", - "more_info_path": "/vulnerabilities/CVE-2021-29573/44167", + "cve": "CVE-2021-29611", + "id": "pyup.io-44133", + "more_info_path": "/vulnerabilities/CVE-2021-29611/44133", + "specs": [ + "<2.0.0" + ], + "v": "<2.0.0" + }, + { + "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", + "cve": "CVE-2021-29548", + "id": "pyup.io-44091", + "more_info_path": "/vulnerabilities/CVE-2021-29548/44091", + "specs": [ + "<2.0.0" + ], + "v": "<2.0.0" + }, + { + "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", + "cve": "CVE-2021-29522", + "id": "pyup.io-44063", + "more_info_path": "/vulnerabilities/CVE-2021-29522/44063", + "specs": [ + "<2.0.0" + ], + "v": "<2.0.0" + }, + { + "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", + "cve": "CVE-2021-29532", + "id": "pyup.io-44070", + "more_info_path": "/vulnerabilities/CVE-2021-29532/44070", "specs": [ "<2.0.0" ], @@ -73771,9 +74202,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29555", - "id": "pyup.io-44147", - "more_info_path": "/vulnerabilities/CVE-2021-29555/44147", + "cve": "CVE-2021-29530", + "id": "pyup.io-44118", + "more_info_path": "/vulnerabilities/CVE-2021-29530/44118", "specs": [ "<2.0.0" ], @@ -73781,9 +74212,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2020-8286", - "id": "pyup.io-44155", - "more_info_path": "/vulnerabilities/CVE-2020-8286/44155", + "cve": "CVE-2021-29529", + "id": "pyup.io-44119", + "more_info_path": "/vulnerabilities/CVE-2021-29529/44119", "specs": [ "<2.0.0" ], @@ -73791,9 +74222,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29611", - "id": "pyup.io-44133", - "more_info_path": "/vulnerabilities/CVE-2021-29611/44133", + "cve": "CVE-2021-29516", + "id": "pyup.io-44159", + "more_info_path": "/vulnerabilities/CVE-2021-29516/44159", "specs": [ "<2.0.0" ], @@ -73801,9 +74232,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29599", - "id": "pyup.io-44123", - "more_info_path": "/vulnerabilities/CVE-2021-29599/44123", + "cve": "CVE-2021-29516", + "id": "pyup.io-44158", + "more_info_path": "/vulnerabilities/CVE-2021-29516/44158", "specs": [ "<2.0.0" ], @@ -73811,9 +74242,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29590", - "id": "pyup.io-44109", - "more_info_path": "/vulnerabilities/CVE-2021-29590/44109", + "cve": "CVE-2021-29515", + "id": "pyup.io-44166", + "more_info_path": "/vulnerabilities/CVE-2021-29515/44166", "specs": [ "<2.0.0" ], @@ -73821,9 +74252,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29548", - "id": "pyup.io-44091", - "more_info_path": "/vulnerabilities/CVE-2021-29548/44091", + "cve": "CVE-2021-29513", + "id": "pyup.io-44097", + "more_info_path": "/vulnerabilities/CVE-2021-29513/44097", "specs": [ "<2.0.0" ], @@ -73831,9 +74262,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29607", - "id": "pyup.io-44131", - "more_info_path": "/vulnerabilities/CVE-2021-29607/44131", + "cve": "CVE-2021-29512", + "id": "pyup.io-44088", + "more_info_path": "/vulnerabilities/CVE-2021-29512/44088", "specs": [ "<2.0.0" ], @@ -73851,9 +74282,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29536", - "id": "pyup.io-44092", - "more_info_path": "/vulnerabilities/CVE-2021-29536/44092", + "cve": "CVE-2020-8286", + "id": "pyup.io-44155", + "more_info_path": "/vulnerabilities/CVE-2020-8286/44155", "specs": [ "<2.0.0" ], @@ -73861,9 +74292,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29531", - "id": "pyup.io-44121", - "more_info_path": "/vulnerabilities/CVE-2021-29531/44121", + "cve": "CVE-2021-29523", + "id": "pyup.io-44068", + "more_info_path": "/vulnerabilities/CVE-2021-29523/44068", "specs": [ "<2.0.0" ], @@ -73871,9 +74302,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29585", - "id": "pyup.io-44106", - "more_info_path": "/vulnerabilities/CVE-2021-29585/44106", + "cve": "CVE-2021-29590", + "id": "pyup.io-44109", + "more_info_path": "/vulnerabilities/CVE-2021-29590/44109", "specs": [ "<2.0.0" ], @@ -73881,9 +74312,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29571", - "id": "pyup.io-44170", - "more_info_path": "/vulnerabilities/CVE-2021-29571/44170", + "cve": "CVE-2021-29531", + "id": "pyup.io-44121", + "more_info_path": "/vulnerabilities/CVE-2021-29531/44121", "specs": [ "<2.0.0" ], @@ -73901,19 +74332,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2020-8285", - "id": "pyup.io-44172", - "more_info_path": "/vulnerabilities/CVE-2020-8285/44172", - "specs": [ - "<2.0.0" - ], - "v": "<2.0.0" - }, - { - "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29575", - "id": "pyup.io-44165", - "more_info_path": "/vulnerabilities/CVE-2021-29575/44165", + "cve": "CVE-2021-29521", + "id": "pyup.io-44067", + "more_info_path": "/vulnerabilities/CVE-2021-29521/44067", "specs": [ "<2.0.0" ], @@ -73931,19 +74352,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2020-8177", - "id": "pyup.io-44146", - "more_info_path": "/vulnerabilities/CVE-2020-8177/44146", - "specs": [ - "<2.0.0" - ], - "v": "<2.0.0" - }, - { - "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29580", - "id": "pyup.io-44099", - "more_info_path": "/vulnerabilities/CVE-2021-29580/44099", + "cve": "CVE-2021-29574", + "id": "pyup.io-44168", + "more_info_path": "/vulnerabilities/CVE-2021-29574/44168", "specs": [ "<2.0.0" ], @@ -73951,9 +74362,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29547", - "id": "pyup.io-44073", - "more_info_path": "/vulnerabilities/CVE-2021-29547/44073", + "cve": "CVE-2020-8285", + "id": "pyup.io-44172", + "more_info_path": "/vulnerabilities/CVE-2020-8285/44172", "specs": [ "<2.0.0" ], @@ -73971,19 +74382,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29515", - "id": "pyup.io-44166", - "more_info_path": "/vulnerabilities/CVE-2021-29515/44166", - "specs": [ - "<2.0.0" - ], - "v": "<2.0.0" - }, - { - "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2020-8231", - "id": "pyup.io-44148", - "more_info_path": "/vulnerabilities/CVE-2020-8231/44148", + "cve": "CVE-2021-29569", + "id": "pyup.io-44161", + "more_info_path": "/vulnerabilities/CVE-2021-29569/44161", "specs": [ "<2.0.0" ], @@ -73991,9 +74392,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29569", - "id": "pyup.io-44161", - "more_info_path": "/vulnerabilities/CVE-2021-29569/44161", + "cve": "CVE-2021-29519", + "id": "pyup.io-44062", + "more_info_path": "/vulnerabilities/CVE-2021-29519/44062", "specs": [ "<2.0.0" ], @@ -74001,9 +74402,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29540", - "id": "pyup.io-44094", - "more_info_path": "/vulnerabilities/CVE-2021-29540/44094", + "cve": "CVE-2021-29610", + "id": "pyup.io-44076", + "more_info_path": "/vulnerabilities/CVE-2021-29610/44076", "specs": [ "<2.0.0" ], @@ -74011,9 +74412,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29519", - "id": "pyup.io-44062", - "more_info_path": "/vulnerabilities/CVE-2021-29519/44062", + "cve": "CVE-2021-29599", + "id": "pyup.io-44123", + "more_info_path": "/vulnerabilities/CVE-2021-29599/44123", "specs": [ "<2.0.0" ], @@ -74021,9 +74422,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29610", - "id": "pyup.io-44076", - "more_info_path": "/vulnerabilities/CVE-2021-29610/44076", + "cve": "CVE-2020-8231", + "id": "pyup.io-44148", + "more_info_path": "/vulnerabilities/CVE-2020-8231/44148", "specs": [ "<2.0.0" ], @@ -74031,9 +74432,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2020-8284", - "id": "pyup.io-44150", - "more_info_path": "/vulnerabilities/CVE-2020-8284/44150", + "cve": "CVE-2021-29597", + "id": "pyup.io-44120", + "more_info_path": "/vulnerabilities/CVE-2021-29597/44120", "specs": [ "<2.0.0" ], @@ -74041,9 +74442,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29556", - "id": "pyup.io-44151", - "more_info_path": "/vulnerabilities/CVE-2021-29556/44151", + "cve": "CVE-2021-29570", + "id": "pyup.io-44164", + "more_info_path": "/vulnerabilities/CVE-2021-29570/44164", "specs": [ "<2.0.0" ], @@ -74061,9 +74462,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29564", - "id": "pyup.io-44066", - "more_info_path": "/vulnerabilities/CVE-2021-29564/44066", + "cve": "CVE-2020-8284", + "id": "pyup.io-44150", + "more_info_path": "/vulnerabilities/CVE-2020-8284/44150", "specs": [ "<2.0.0" ], @@ -74071,9 +74472,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29516", - "id": "pyup.io-44159", - "more_info_path": "/vulnerabilities/CVE-2021-29516/44159", + "cve": "CVE-2021-29600", + "id": "pyup.io-44124", + "more_info_path": "/vulnerabilities/CVE-2021-29600/44124", "specs": [ "<2.0.0" ], @@ -74081,9 +74482,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29588", - "id": "pyup.io-44108", - "more_info_path": "/vulnerabilities/CVE-2021-29588/44108", + "cve": "CVE-2021-29552", + "id": "pyup.io-44144", + "more_info_path": "/vulnerabilities/CVE-2021-29552/44144", "specs": [ "<2.0.0" ], @@ -74091,9 +74492,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29537", - "id": "pyup.io-44084", - "more_info_path": "/vulnerabilities/CVE-2021-29537/44084", + "cve": "CVE-2021-29547", + "id": "pyup.io-44073", + "more_info_path": "/vulnerabilities/CVE-2021-29547/44073", "specs": [ "<2.0.0" ], @@ -74101,9 +74502,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29600", - "id": "pyup.io-44124", - "more_info_path": "/vulnerabilities/CVE-2021-29600/44124", + "cve": "CVE-2021-29594", + "id": "pyup.io-44112", + "more_info_path": "/vulnerabilities/CVE-2021-29594/44112", "specs": [ "<2.0.0" ], @@ -74111,9 +74512,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29582", - "id": "pyup.io-44103", - "more_info_path": "/vulnerabilities/CVE-2021-29582/44103", + "cve": "CVE-2021-29572", + "id": "pyup.io-44098", + "more_info_path": "/vulnerabilities/CVE-2021-29572/44098", "specs": [ "<2.0.0" ], @@ -74131,9 +74532,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2020-8169", - "id": "pyup.io-44069", - "more_info_path": "/vulnerabilities/CVE-2020-8169/44069", + "cve": "CVE-2021-29563", + "id": "pyup.io-44080", + "more_info_path": "/vulnerabilities/CVE-2021-29563/44080", "specs": [ "<2.0.0" ], @@ -74141,9 +74542,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29572", - "id": "pyup.io-44098", - "more_info_path": "/vulnerabilities/CVE-2021-29572/44098", + "cve": "CVE-2021-29558", + "id": "pyup.io-44152", + "more_info_path": "/vulnerabilities/CVE-2021-29558/44152", "specs": [ "<2.0.0" ], @@ -74151,9 +74552,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29565", - "id": "pyup.io-44154", - "more_info_path": "/vulnerabilities/CVE-2021-29565/44154", + "cve": "CVE-2021-29564", + "id": "pyup.io-44066", + "more_info_path": "/vulnerabilities/CVE-2021-29564/44066", "specs": [ "<2.0.0" ], @@ -74161,9 +74562,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29562", - "id": "pyup.io-44163", - "more_info_path": "/vulnerabilities/CVE-2021-29562/44163", + "cve": "CVE-2021-29544", + "id": "pyup.io-44074", + "more_info_path": "/vulnerabilities/CVE-2021-29544/44074", "specs": [ "<2.0.0" ], @@ -74171,9 +74572,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29563", - "id": "pyup.io-44080", - "more_info_path": "/vulnerabilities/CVE-2021-29563/44080", + "cve": "CVE-2021-29536", + "id": "pyup.io-44092", + "more_info_path": "/vulnerabilities/CVE-2021-29536/44092", "specs": [ "<2.0.0" ], @@ -74181,9 +74582,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29513", - "id": "pyup.io-44097", - "more_info_path": "/vulnerabilities/CVE-2021-29513/44097", + "cve": "CVE-2020-8169", + "id": "pyup.io-44069", + "more_info_path": "/vulnerabilities/CVE-2020-8169/44069", "specs": [ "<2.0.0" ], @@ -74191,9 +74592,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29603", - "id": "pyup.io-44126", - "more_info_path": "/vulnerabilities/CVE-2021-29603/44126", + "cve": "CVE-2021-29589", + "id": "pyup.io-44087", + "more_info_path": "/vulnerabilities/CVE-2021-29589/44087", "specs": [ "<2.0.0" ], @@ -74211,19 +74612,9 @@ }, { "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29550", - "id": "pyup.io-44082", - "more_info_path": "/vulnerabilities/CVE-2021-29550/44082", - "specs": [ - "<2.0.0" - ], - "v": "<2.0.0" - }, - { - "advisory": "Jina version 2.0.0 updates its dependency \"Tensorflow\" to v2.4.2 to include security fixes.", - "cve": "CVE-2021-29544", - "id": "pyup.io-44074", - "more_info_path": "/vulnerabilities/CVE-2021-29544/44074", + "cve": "CVE-2020-8177", + "id": "pyup.io-44146", + "more_info_path": "/vulnerabilities/CVE-2020-8177/44146", "specs": [ "<2.0.0" ], @@ -74301,16 +74692,6 @@ ], "v": "<3.1.4" }, - { - "advisory": "In Jinja2, the from_string function is prone to Server Side Template Injection (SSTI) where it takes the source parameter as a template object, renders it, and then returns it. The attacker can exploit it with INJECTION COMMANDS in a URI. \r\nNOTE: The maintainer and multiple third parties believe that this vulnerability isn't valid because users shouldn't use untrusted templates without sandboxing.", - "cve": "CVE-2019-8341", - "id": "pyup.io-70612", - "more_info_path": "/vulnerabilities/CVE-2019-8341/70612", - "specs": [ - ">=0" - ], - "v": ">=0" - }, { "advisory": "Jinja2 2.10.1 adds 'SandboxedEnvironment' to handle 'str.format_map' in order to prevent code execution through untrusted format strings.\r\nhttps://github.com/pallets/jinja/commit/a2a6c930bcca591a25d2b316fcfd2d6793897b26", "cve": "CVE-2019-10906", @@ -74437,26 +74818,6 @@ ], "v": "<3.3.1" }, - { - "advisory": "Jnitrace 3.3.1 updates its NPM dependency 'cached-path-relative' to v1.1.0 to include a security fix.", - "cve": "CVE-2021-23518", - "id": "pyup.io-54818", - "more_info_path": "/vulnerabilities/CVE-2021-23518/54818", - "specs": [ - "<3.3.1" - ], - "v": "<3.3.1" - }, - { - "advisory": "Jnitrace 3.3.1 updates its NPM dependency 'ansi-regex' to v4.1.1 to include a security fix.", - "cve": "CVE-2021-3807", - "id": "pyup.io-54825", - "more_info_path": "/vulnerabilities/CVE-2021-3807/54825", - "specs": [ - "<3.3.1" - ], - "v": "<3.3.1" - }, { "advisory": "Jnitrace 3.3.1 updates its NPM dependency 'shell-quote' to v1.7.3 to include a security fix.", "cve": "CVE-2021-42740", @@ -74477,6 +74838,16 @@ ], "v": "<3.3.1" }, + { + "advisory": "Jnitrace 3.3.1 updates its NPM dependency 'ansi-regex' to v4.1.1 to include a security fix.", + "cve": "CVE-2021-3807", + "id": "pyup.io-54825", + "more_info_path": "/vulnerabilities/CVE-2021-3807/54825", + "specs": [ + "<3.3.1" + ], + "v": "<3.3.1" + }, { "advisory": "Jnitrace 3.3.1 updates its NPM dependency 'minimist' to v1.2.8 to include a security fix.", "cve": "CVE-2021-44906", @@ -74486,6 +74857,16 @@ "<3.3.1" ], "v": "<3.3.1" + }, + { + "advisory": "Jnitrace 3.3.1 updates its NPM dependency 'cached-path-relative' to v1.1.0 to include a security fix.", + "cve": "CVE-2021-23518", + "id": "pyup.io-54818", + "more_info_path": "/vulnerabilities/CVE-2021-23518/54818", + "specs": [ + "<3.3.1" + ], + "v": "<3.3.1" } ], "jno": [ @@ -74501,9 +74882,9 @@ }, { "advisory": "Jno 0.5.0 and prior potentially uses a version of Arduino IDE that depends on a version of 'log4j' containing severe and critical vulnerabilities.", - "cve": "CVE-2021-45105", - "id": "pyup.io-43590", - "more_info_path": "/vulnerabilities/CVE-2021-45105/43590", + "cve": "CVE-2021-44228", + "id": "pyup.io-43588", + "more_info_path": "/vulnerabilities/CVE-2021-44228/43588", "specs": [ "<=0.5.0" ], @@ -74511,9 +74892,9 @@ }, { "advisory": "Jno 0.5.0 and prior potentially uses a version of Arduino IDE that depends on a version of 'log4j' containing severe and critical vulnerabilities.", - "cve": "CVE-2021-44228", - "id": "pyup.io-43588", - "more_info_path": "/vulnerabilities/CVE-2021-44228/43588", + "cve": "CVE-2021-45046", + "id": "pyup.io-43589", + "more_info_path": "/vulnerabilities/CVE-2021-45046/43589", "specs": [ "<=0.5.0" ], @@ -74521,9 +74902,9 @@ }, { "advisory": "Jno 0.5.0 and prior potentially uses a version of Arduino IDE that depends on a version of 'log4j' containing severe and critical vulnerabilities.", - "cve": "CVE-2021-45046", - "id": "pyup.io-43589", - "more_info_path": "/vulnerabilities/CVE-2021-45046/43589", + "cve": "CVE-2021-45105", + "id": "pyup.io-43590", + "more_info_path": "/vulnerabilities/CVE-2021-45105/43590", "specs": [ "<=0.5.0" ], @@ -74830,9 +75211,9 @@ "juntagrico": [ { "advisory": "Juntagrico 1.5.5 updates its dependency 'django' requirement to \"~=4.0.8\" to include security fixes.", - "cve": "CVE-2022-34265", - "id": "pyup.io-51982", - "more_info_path": "/vulnerabilities/CVE-2022-34265/51982", + "cve": "CVE-2022-41323", + "id": "pyup.io-51984", + "more_info_path": "/vulnerabilities/CVE-2022-41323/51984", "specs": [ "<1.5.5" ], @@ -74860,9 +75241,9 @@ }, { "advisory": "Juntagrico 1.5.5 updates its dependency 'django' requirement to \"~=4.0.8\" to include security fixes.", - "cve": "CVE-2022-41323", - "id": "pyup.io-51984", - "more_info_path": "/vulnerabilities/CVE-2022-41323/51984", + "cve": "CVE-2022-28346", + "id": "pyup.io-51981", + "more_info_path": "/vulnerabilities/CVE-2022-28346/51981", "specs": [ "<1.5.5" ], @@ -74870,9 +75251,9 @@ }, { "advisory": "Juntagrico 1.5.5 updates its dependency 'django' requirement to \"~=4.0.8\" to include security fixes.", - "cve": "CVE-2022-28346", - "id": "pyup.io-51981", - "more_info_path": "/vulnerabilities/CVE-2022-28346/51981", + "cve": "CVE-2022-34265", + "id": "pyup.io-51982", + "more_info_path": "/vulnerabilities/CVE-2022-34265/51982", "specs": [ "<1.5.5" ], @@ -74963,10 +75344,10 @@ ], "jupyter-jsmol": [ { - "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", - "cve": "CVE-2021-37701", - "id": "pyup.io-45569", - "more_info_path": "/vulnerabilities/CVE-2021-37701/45569", + "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'url-parse' to v1.5.3 to include a security fix.", + "cve": "CVE-2021-3664", + "id": "pyup.io-45574", + "more_info_path": "/vulnerabilities/CVE-2021-3664/45574", "specs": [ "<2021.9.0" ], @@ -74983,20 +75364,20 @@ "v": "<2021.9.0" }, { - "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", - "cve": "CVE-2021-37712", - "id": "pyup.io-45570", - "more_info_path": "/vulnerabilities/CVE-2021-37712/45570", + "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'lodash' to v4.17.21 to include security fixes.", + "cve": "CVE-2020-28500", + "id": "pyup.io-45563", + "more_info_path": "/vulnerabilities/CVE-2020-28500/45563", "specs": [ "<2021.9.0" ], "v": "<2021.9.0" }, { - "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'lodash' to v4.17.21 to include security fixes.", - "cve": "CVE-2020-28500", - "id": "pyup.io-45563", - "more_info_path": "/vulnerabilities/CVE-2020-28500/45563", + "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", + "cve": "CVE-2021-32804", + "id": "pyup.io-45572", + "more_info_path": "/vulnerabilities/CVE-2021-32804/45572", "specs": [ "<2021.9.0" ], @@ -75022,16 +75403,6 @@ ], "v": "<2021.9.0" }, - { - "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'ssri' to v6.0.2 to include a security fix.", - "cve": "CVE-2021-27290", - "id": "pyup.io-45561", - "more_info_path": "/vulnerabilities/CVE-2021-27290/45561", - "specs": [ - "<2021.9.0" - ], - "v": "<2021.9.0" - }, { "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'lodash' to v4.17.21 to include security fixes.", "cve": "CVE-2021-23337", @@ -75063,20 +75434,20 @@ "v": "<2021.9.0" }, { - "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", - "cve": "CVE-2021-32804", - "id": "pyup.io-45572", - "more_info_path": "/vulnerabilities/CVE-2021-32804/45572", + "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'ssri' to v6.0.2 to include a security fix.", + "cve": "CVE-2021-27290", + "id": "pyup.io-45561", + "more_info_path": "/vulnerabilities/CVE-2021-27290/45561", "specs": [ "<2021.9.0" ], "v": "<2021.9.0" }, { - "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'url-parse' to v1.5.3 to include a security fix.", - "cve": "CVE-2021-3664", - "id": "pyup.io-45574", - "more_info_path": "/vulnerabilities/CVE-2021-3664/45574", + "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'underscore' to v1.13.1 to include a security fix.", + "cve": "CVE-2021-23358", + "id": "pyup.io-45562", + "more_info_path": "/vulnerabilities/CVE-2021-23358/45562", "specs": [ "<2021.9.0" ], @@ -75093,20 +75464,30 @@ "v": "<2021.9.0" }, { - "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'underscore' to v1.13.1 to include a security fix.", - "cve": "CVE-2021-23358", - "id": "pyup.io-45562", - "more_info_path": "/vulnerabilities/CVE-2021-23358/45562", + "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'path-parse' to v1.0.7 to include a security fix.", + "cve": "CVE-2021-23343", + "id": "pyup.io-45568", + "more_info_path": "/vulnerabilities/CVE-2021-23343/45568", "specs": [ "<2021.9.0" ], "v": "<2021.9.0" }, { - "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'path-parse' to v1.0.7 to include a security fix.", - "cve": "CVE-2021-23343", - "id": "pyup.io-45568", - "more_info_path": "/vulnerabilities/CVE-2021-23343/45568", + "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", + "cve": "CVE-2021-37712", + "id": "pyup.io-45570", + "more_info_path": "/vulnerabilities/CVE-2021-37712/45570", + "specs": [ + "<2021.9.0" + ], + "v": "<2021.9.0" + }, + { + "advisory": "Jupyter-jsmol 2021.9.0 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", + "cve": "CVE-2021-37701", + "id": "pyup.io-45569", + "more_info_path": "/vulnerabilities/CVE-2021-37701/45569", "specs": [ "<2021.9.0" ], @@ -75312,6 +75693,16 @@ ], "v": "<1.17.1,==2.0.0a0" }, + { + "advisory": "Affected versions of jupyter_server are vulnerable to information exposure through logs. The log_request function recorded sensitive query parameters (token, auth, key, code, state, xsrf) in logs without sanitization. Attackers could exploit this vulnerability by accessing leaked or improperly secured log files, gaining unauthorized access to sensitive data such as authentication tokens or API keys.", + "cve": "PVE-2024-63562", + "id": "pyup.io-63562", + "more_info_path": "/vulnerabilities/PVE-2024-63562/63562", + "specs": [ + "<1.23.6" + ], + "v": "<1.23.6" + }, { "advisory": "Jupyter-server version 1.6.2 improves xsrf checks.\r\nhttps://github.com/jupyter-server/jupyter_server/pull/478", "cve": "PVE-2021-41836", @@ -75611,30 +76002,30 @@ ], "jupyterlab-link-share": [ { - "advisory": "Jupyterlab-link-share 0.2.1 updates its NPM dependency 'hosted-git-info' to v2.8.9 to include a security fix.", - "cve": "CVE-2021-23362", - "id": "pyup.io-52189", - "more_info_path": "/vulnerabilities/CVE-2021-23362/52189", + "advisory": "Jupyterlab-link-share 0.2.1 updates its NPM dependency 'ssri' to v8.0.1 to include a security fix.", + "cve": "CVE-2021-27290", + "id": "pyup.io-52299", + "more_info_path": "/vulnerabilities/CVE-2021-27290/52299", "specs": [ "<0.2.1" ], "v": "<0.2.1" }, { - "advisory": "Jupyterlab-link-share 0.2.1 updates its NPM dependency 'ssri' to v8.0.1 to include a security fix.", - "cve": "CVE-2021-27290", - "id": "pyup.io-52299", - "more_info_path": "/vulnerabilities/CVE-2021-27290/52299", + "advisory": "Jupyterlab-link-share 0.2.1 updates its NPM dependency 'lodash' to v4.17.21 to include security fixes.", + "cve": "CVE-2020-28500", + "id": "pyup.io-52301", + "more_info_path": "/vulnerabilities/CVE-2020-28500/52301", "specs": [ "<0.2.1" ], "v": "<0.2.1" }, { - "advisory": "Jupyterlab-link-share 0.2.1 updates its NPM dependency 'lodash' to v4.17.21 to include security fixes.", - "cve": "CVE-2021-23337", - "id": "pyup.io-52300", - "more_info_path": "/vulnerabilities/CVE-2021-23337/52300", + "advisory": "Jupyterlab-link-share 0.2.1 updates its NPM dependency 'hosted-git-info' to v2.8.9 to include a security fix.", + "cve": "CVE-2021-23362", + "id": "pyup.io-52189", + "more_info_path": "/vulnerabilities/CVE-2021-23362/52189", "specs": [ "<0.2.1" ], @@ -75642,9 +76033,9 @@ }, { "advisory": "Jupyterlab-link-share 0.2.1 updates its NPM dependency 'lodash' to v4.17.21 to include security fixes.", - "cve": "CVE-2020-28500", - "id": "pyup.io-52301", - "more_info_path": "/vulnerabilities/CVE-2020-28500/52301", + "cve": "CVE-2021-23337", + "id": "pyup.io-52300", + "more_info_path": "/vulnerabilities/CVE-2021-23337/52300", "specs": [ "<0.2.1" ], @@ -75772,6 +76163,16 @@ ], "v": "<1.11.3" }, + { + "advisory": "Jupytext 1.11.5 updates its NPM dependency 'url-parse' to v1.5.3 to include a security fix.", + "cve": "CVE-2021-3664", + "id": "pyup.io-49040", + "more_info_path": "/vulnerabilities/CVE-2021-3664/49040", + "specs": [ + "<1.11.5" + ], + "v": "<1.11.5" + }, { "advisory": "Jupytext 1.11.5 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", "cve": "CVE-2021-37713", @@ -75804,19 +76205,9 @@ }, { "advisory": "Jupytext 1.11.5 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", - "cve": "CVE-2021-32804", - "id": "pyup.io-49038", - "more_info_path": "/vulnerabilities/CVE-2021-32804/49038", - "specs": [ - "<1.11.5" - ], - "v": "<1.11.5" - }, - { - "advisory": "Jupytext 1.11.5 updates its NPM dependency 'url-parse' to v1.5.3 to include a security fix.", - "cve": "CVE-2021-3664", - "id": "pyup.io-49040", - "more_info_path": "/vulnerabilities/CVE-2021-3664/49040", + "cve": "CVE-2021-37701", + "id": "pyup.io-41249", + "more_info_path": "/vulnerabilities/CVE-2021-37701/41249", "specs": [ "<1.11.5" ], @@ -75824,9 +76215,9 @@ }, { "advisory": "Jupytext 1.11.5 updates its NPM dependency 'tar' to v6.1.11 to include security fixes.", - "cve": "CVE-2021-37701", - "id": "pyup.io-41249", - "more_info_path": "/vulnerabilities/CVE-2021-37701/41249", + "cve": "CVE-2021-32804", + "id": "pyup.io-49038", + "more_info_path": "/vulnerabilities/CVE-2021-32804/49038", "specs": [ "<1.11.5" ], @@ -75843,10 +76234,10 @@ "v": "<1.13.0" }, { - "advisory": "Jupytext 1.13.8 updates its NPM dependency 'url-parse' to v1.5.10 to include security fixes.", - "cve": "CVE-2022-0691", - "id": "pyup.io-47984", - "more_info_path": "/vulnerabilities/CVE-2022-0691/47984", + "advisory": "Jupytext 1.13.8 updates its NPM dependency 'nanoid' to v3.3.1 to include a security fix.", + "cve": "CVE-2021-23566", + "id": "pyup.io-47972", + "more_info_path": "/vulnerabilities/CVE-2021-23566/47972", "specs": [ "<1.13.8" ], @@ -75863,10 +76254,10 @@ "v": "<1.13.8" }, { - "advisory": "Jupytext 1.13.8 updates its NPM dependency 'nanoid' to v3.3.1 to include a security fix.", - "cve": "CVE-2021-23566", - "id": "pyup.io-47972", - "more_info_path": "/vulnerabilities/CVE-2021-23566/47972", + "advisory": "Jupytext 1.13.8 updates its NPM dependency 'url-parse' to v1.5.10 to include security fixes.", + "cve": "CVE-2022-0691", + "id": "pyup.io-47984", + "more_info_path": "/vulnerabilities/CVE-2022-0691/47984", "specs": [ "<1.13.8" ], @@ -75884,9 +76275,9 @@ }, { "advisory": "Jupytext 1.13.8 updates its NPM dependency 'url-parse' to v1.5.10 to include a security fixes.", - "cve": "CVE-2022-0512", - "id": "pyup.io-47985", - "more_info_path": "/vulnerabilities/CVE-2022-0512/47985", + "cve": "CVE-2022-0639", + "id": "pyup.io-47982", + "more_info_path": "/vulnerabilities/CVE-2022-0639/47982", "specs": [ "<1.13.8" ], @@ -75894,9 +76285,9 @@ }, { "advisory": "Jupytext 1.13.8 updates its NPM dependency 'url-parse' to v1.5.10 to include a security fixes.", - "cve": "CVE-2022-0639", - "id": "pyup.io-47982", - "more_info_path": "/vulnerabilities/CVE-2022-0639/47982", + "cve": "CVE-2022-0512", + "id": "pyup.io-47985", + "more_info_path": "/vulnerabilities/CVE-2022-0512/47985", "specs": [ "<1.13.8" ], @@ -75912,16 +76303,6 @@ ], "v": "<1.14.2" }, - { - "advisory": "Jupytext 1.14.2 updates its NPM dependency 'moment' to v2.29.4 to include a security fix.", - "cve": "CVE-2022-31129", - "id": "pyup.io-52330", - "more_info_path": "/vulnerabilities/CVE-2022-31129/52330", - "specs": [ - "<1.14.2" - ], - "v": "<1.14.2" - }, { "advisory": "Jupytext 1.14.2 updates its NPM dependency 'terser' to v5.14.2 to include a security fix.", "cve": "CVE-2022-25858", @@ -75933,14 +76314,14 @@ "v": "<1.14.2" }, { - "advisory": "Jupytext 1.14.5 updates its NPM dependency 'json5' to v1.0.2 to include a security fix.", - "cve": "CVE-2022-46175", - "id": "pyup.io-53467", - "more_info_path": "/vulnerabilities/CVE-2022-46175/53467", + "advisory": "Jupytext 1.14.2 updates its NPM dependency 'moment' to v2.29.4 to include a security fix.", + "cve": "CVE-2022-31129", + "id": "pyup.io-52330", + "more_info_path": "/vulnerabilities/CVE-2022-31129/52330", "specs": [ - "<1.14.5" + "<1.14.2" ], - "v": "<1.14.5" + "v": "<1.14.2" }, { "advisory": "Jupytext 1.14.5 updates its NPM dependency 'http-cache-semantics' to v4.1.1 to include a security fix.", @@ -75951,6 +76332,16 @@ "<1.14.5" ], "v": "<1.14.5" + }, + { + "advisory": "Jupytext 1.14.5 updates its NPM dependency 'json5' to v1.0.2 to include a security fix.", + "cve": "CVE-2022-46175", + "id": "pyup.io-53467", + "more_info_path": "/vulnerabilities/CVE-2022-46175/53467", + "specs": [ + "<1.14.5" + ], + "v": "<1.14.5" } ], "just-distribute": [ @@ -76557,20 +76948,20 @@ "v": "<6.3.0" }, { - "advisory": "In Keylime before 6.3.0, quote responses from the agent can contain possibly untrusted ZIP data which can lead to zip bombs.", - "cve": "CVE-2022-23951", - "id": "pyup.io-62594", - "more_info_path": "/vulnerabilities/CVE-2022-23951/62594", + "advisory": "In Keylime before 6.3.0, current keylime installer installs the keylime.conf file, which can contain sensitive data, as world-readable.", + "cve": "CVE-2022-23952", + "id": "pyup.io-62595", + "more_info_path": "/vulnerabilities/CVE-2022-23952/62595", "specs": [ "<6.3.0" ], "v": "<6.3.0" }, { - "advisory": "In Keylime before 6.3.0, current keylime installer installs the keylime.conf file, which can contain sensitive data, as world-readable.", - "cve": "CVE-2022-23952", - "id": "pyup.io-62595", - "more_info_path": "/vulnerabilities/CVE-2022-23952/62595", + "advisory": "In Keylime before 6.3.0, quote responses from the agent can contain possibly untrusted ZIP data which can lead to zip bombs.", + "cve": "CVE-2022-23951", + "id": "pyup.io-62594", + "more_info_path": "/vulnerabilities/CVE-2022-23951/62594", "specs": [ "<6.3.0" ], @@ -77435,9 +77826,9 @@ }, { "advisory": "Khorosjx 3.0.0 updates its dependency \"urllib3\" to v1.26.6 to include security fixes.", - "cve": "CVE-2020-7212", - "id": "pyup.io-41754", - "more_info_path": "/vulnerabilities/CVE-2020-7212/41754", + "cve": "CVE-2020-26137", + "id": "pyup.io-49043", + "more_info_path": "/vulnerabilities/CVE-2020-26137/49043", "specs": [ "<3.0.0" ], @@ -77445,9 +77836,9 @@ }, { "advisory": "Khorosjx 3.0.0 updates its dependency \"urllib3\" to v1.26.6 to include security fixes.", - "cve": "CVE-2020-26137", - "id": "pyup.io-49043", - "more_info_path": "/vulnerabilities/CVE-2020-26137/49043", + "cve": "CVE-2020-7212", + "id": "pyup.io-41754", + "more_info_path": "/vulnerabilities/CVE-2020-7212/41754", "specs": [ "<3.0.0" ], @@ -77691,26 +78082,6 @@ } ], "kiwitcms": [ - { - "advisory": "Kiwitcms 11.1 updates its dependency 'Django' to v4.0.2 to include security fixes.", - "cve": "CVE-2021-45115", - "id": "pyup.io-48455", - "more_info_path": "/vulnerabilities/CVE-2021-45115/48455", - "specs": [ - "<11.1" - ], - "v": "<11.1" - }, - { - "advisory": "Kiwitcms 11.1 updates its dependency 'Django' to v4.0.2 to include security fixes.", - "cve": "CVE-2022-23833", - "id": "pyup.io-48309", - "more_info_path": "/vulnerabilities/CVE-2022-23833/48309", - "specs": [ - "<11.1" - ], - "v": "<11.1" - }, { "advisory": "Kiwitcms 11.1 updates its dependency 'Django' to v4.0.2 to include security fixes.", "cve": "CVE-2022-22818", @@ -77741,6 +78112,26 @@ ], "v": "<11.1" }, + { + "advisory": "Kiwitcms 11.1 updates its dependency 'Django' to v4.0.2 to include security fixes.", + "cve": "CVE-2021-45115", + "id": "pyup.io-48455", + "more_info_path": "/vulnerabilities/CVE-2021-45115/48455", + "specs": [ + "<11.1" + ], + "v": "<11.1" + }, + { + "advisory": "Kiwitcms 11.1 updates its dependency 'Django' to v4.0.2 to include security fixes.", + "cve": "CVE-2022-23833", + "id": "pyup.io-48309", + "more_info_path": "/vulnerabilities/CVE-2022-23833/48309", + "specs": [ + "<11.1" + ], + "v": "<11.1" + }, { "advisory": "Kiwitcms 11.6 cleans HTML input when generating history diff to prevent XSS attacks.\r\nhttps://github.com/kiwitcms/Kiwi/commit/a2b169ffdef1d7c1755bade8138578423b35011b", "cve": "PVE-2022-51779", @@ -78042,20 +78433,20 @@ "v": "<8.1" }, { - "advisory": "Kiwi TCMS is an open source test management system. In kiwitcms/Kiwi v12.2 and prior and kiwitcms/enterprise v12.2 and prior, the `changelog.yml` workflow is vulnerable to command injection attacks because of using an untrusted `github.head_ref` field. The `github.head_ref` value is an attacker-controlled value. Assigning the value to `zzz\";echo${IFS}\"hello\";#` can lead to command injection. Since the permission is not restricted, the attacker has a write-access to the repository. Commit 834c86dfd1b2492ccad7ebbfd6304bfec895fed2 of the kiwitcms/Kiwi repository and commit e39f7e156fdaf6fec09a15ea6f4e8fec8cdbf751 of the kiwitcms/enterprise repository contain a fix for this issue.", - "cve": "CVE-2023-30628", - "id": "pyup.io-64183", - "more_info_path": "/vulnerabilities/CVE-2023-30628/64183", + "advisory": "Kiwi TCMS is an open source test management system for both manual and automated testing. Kiwi TCMS allows users to upload attachments to test plans, test cases, etc. Earlier versions of Kiwi TCMS had introduced upload validators in order to prevent potentially dangerous files from being uploaded. The upload validation checks were not robust enough which left the possibility of an attacker to circumvent them and upload a potentially dangerous file. Exploiting this flaw, a combination of files could be uploaded so that they work together to circumvent the existing Content-Security-Policy and allow execution of arbitrary JavaScript in the browser. This issue has been patched in version 12.3.\r\n\r\nAlias:\r\nGHSA-x7c2-7wvg-jpx7", + "cve": "CVE-2023-32686", + "id": "pyup.io-59493", + "more_info_path": "/vulnerabilities/CVE-2023-32686/59493", "specs": [ "<=12.2" ], "v": "<=12.2" }, { - "advisory": "Kiwi TCMS is an open source test management system for both manual and automated testing. Kiwi TCMS allows users to upload attachments to test plans, test cases, etc. Earlier versions of Kiwi TCMS had introduced upload validators in order to prevent potentially dangerous files from being uploaded. The upload validation checks were not robust enough which left the possibility of an attacker to circumvent them and upload a potentially dangerous file. Exploiting this flaw, a combination of files could be uploaded so that they work together to circumvent the existing Content-Security-Policy and allow execution of arbitrary JavaScript in the browser. This issue has been patched in version 12.3.\r\n\r\nAlias:\r\nGHSA-x7c2-7wvg-jpx7", - "cve": "CVE-2023-32686", - "id": "pyup.io-59493", - "more_info_path": "/vulnerabilities/CVE-2023-32686/59493", + "advisory": "Kiwi TCMS is an open source test management system. In kiwitcms/Kiwi v12.2 and prior and kiwitcms/enterprise v12.2 and prior, the `changelog.yml` workflow is vulnerable to command injection attacks because of using an untrusted `github.head_ref` field. The `github.head_ref` value is an attacker-controlled value. Assigning the value to `zzz\";echo${IFS}\"hello\";#` can lead to command injection. Since the permission is not restricted, the attacker has a write-access to the repository. Commit 834c86dfd1b2492ccad7ebbfd6304bfec895fed2 of the kiwitcms/Kiwi repository and commit e39f7e156fdaf6fec09a15ea6f4e8fec8cdbf751 of the kiwitcms/enterprise repository contain a fix for this issue.", + "cve": "CVE-2023-30628", + "id": "pyup.io-64183", + "more_info_path": "/vulnerabilities/CVE-2023-30628/64183", "specs": [ "<=12.2" ], @@ -78678,10 +79069,10 @@ ], "label-sleuth": [ { - "advisory": "Label-sleuth 0.11.6 updates its dependency 'GitPython' to v3.1.31 to include a security fix.", - "cve": "CVE-2022-24439", - "id": "pyup.io-58754", - "more_info_path": "/vulnerabilities/CVE-2022-24439/58754", + "advisory": "Label-sleuth 0.11.6 updates its dependency 'flask' to v2.3.2 to include a security fix.", + "cve": "CVE-2023-30861", + "id": "pyup.io-58764", + "more_info_path": "/vulnerabilities/CVE-2023-30861/58764", "specs": [ "<0.11.6" ], @@ -78698,10 +79089,20 @@ "v": "<0.11.6" }, { - "advisory": "Label-sleuth 0.11.6 updates its dependency 'flask' to v2.3.2 to include a security fix.", - "cve": "CVE-2023-30861", - "id": "pyup.io-58764", - "more_info_path": "/vulnerabilities/CVE-2023-30861/58764", + "advisory": "Label-sleuth 0.11.6 updates its dependency 'waitress' to v2.1.2 to include a security fix.", + "cve": "CVE-2022-31015", + "id": "pyup.io-58767", + "more_info_path": "/vulnerabilities/CVE-2022-31015/58767", + "specs": [ + "<0.11.6" + ], + "v": "<0.11.6" + }, + { + "advisory": "Label-sleuth 0.11.6 updates its dependency 'GitPython' to v3.1.31 to include a security fix.", + "cve": "CVE-2022-24439", + "id": "pyup.io-58754", + "more_info_path": "/vulnerabilities/CVE-2022-24439/58754", "specs": [ "<0.11.6" ], @@ -78727,16 +79128,6 @@ ], "v": "<0.11.6" }, - { - "advisory": "Label-sleuth 0.11.6 updates its dependency 'waitress' to v2.1.2 to include a security fix.", - "cve": "CVE-2022-31015", - "id": "pyup.io-58767", - "more_info_path": "/vulnerabilities/CVE-2022-31015/58767", - "specs": [ - "<0.11.6" - ], - "v": "<0.11.6" - }, { "advisory": "Label-sleuth 0.11.6 updates its NPM dependency 'webpack' to v5.82.1 to include a security fix.", "cve": "CVE-2023-28154", @@ -78780,20 +79171,20 @@ "v": "<1.10.1" }, { - "advisory": "Label-studio 1.11.0 addresses the CVE-2023-47116 by introducing more exhaustive IP validation for Server Side Request Forgery (SSRF) defenses. This includes banning all IPs within reserved blocks, for both IPv4 and IPv6, by default. The system also allows users to ban additional blocks using USER_ADDITIONAL_BANNED_SUBNETS, or to specify their full list of banned IP blocks themselves using USE_DEFAULT_BANNED_SUBNETS. By default, USE_DEFAULT_BANNED_SUBNETS is set to True. Additionally, the error message has been made more informative when SSRF protection blocks an upload.\r\nhttps://github.com/HumanSignal/label-studio/pull/5316", - "cve": "CVE-2023-47116", - "id": "pyup.io-64822", - "more_info_path": "/vulnerabilities/CVE-2023-47116/64822", + "advisory": "Label Studio before 1.11.0 is vulnerable to cross-site scripting (XSS) because it fails to properly sanitize data uploaded via the file upload feature before it is rendered within Choices or Labels tags. This vulnerability allows attackers to inject malicious scripts that could execute within the user's browser session. However, exploitation is contingent upon the attacker having permission to use the \"data import\" function.", + "cve": "CVE-2024-26152", + "id": "pyup.io-66696", + "more_info_path": "/vulnerabilities/CVE-2024-26152/66696", "specs": [ "<1.11.0" ], "v": "<1.11.0" }, { - "advisory": "Label Studio before 1.11.0 is vulnerable to cross-site scripting (XSS) because it fails to properly sanitize data uploaded via the file upload feature before it is rendered within Choices or Labels tags. This vulnerability allows attackers to inject malicious scripts that could execute within the user's browser session. However, exploitation is contingent upon the attacker having permission to use the \"data import\" function.", - "cve": "CVE-2024-26152", - "id": "pyup.io-66696", - "more_info_path": "/vulnerabilities/CVE-2024-26152/66696", + "advisory": "Label-studio 1.11.0 addresses the CVE-2023-47116 by introducing more exhaustive IP validation for Server Side Request Forgery (SSRF) defenses. This includes banning all IPs within reserved blocks, for both IPv4 and IPv6, by default. The system also allows users to ban additional blocks using USER_ADDITIONAL_BANNED_SUBNETS, or to specify their full list of banned IP blocks themselves using USE_DEFAULT_BANNED_SUBNETS. By default, USE_DEFAULT_BANNED_SUBNETS is set to True. Additionally, the error message has been made more informative when SSRF protection blocks an upload.\r\nhttps://github.com/HumanSignal/label-studio/pull/5316", + "cve": "CVE-2023-47116", + "id": "pyup.io-64822", + "more_info_path": "/vulnerabilities/CVE-2023-47116/64822", "specs": [ "<1.11.0" ], @@ -78894,9 +79285,9 @@ "label-studio-converter": [ { "advisory": "Label-studio-converter 0.0.43 updates its dependency 'pillow' to v8.3.1 to include security fixes.", - "cve": "CVE-2021-25291", - "id": "pyup.io-50650", - "more_info_path": "/vulnerabilities/CVE-2021-25291/50650", + "cve": "CVE-2021-25293", + "id": "pyup.io-50652", + "more_info_path": "/vulnerabilities/CVE-2021-25293/50652", "specs": [ "<0.0.43" ], @@ -78904,9 +79295,9 @@ }, { "advisory": "Label-studio-converter 0.0.43 updates its dependency 'pillow' to v8.3.1 to include security fixes.", - "cve": "CVE-2021-25292", - "id": "pyup.io-50651", - "more_info_path": "/vulnerabilities/CVE-2021-25292/50651", + "cve": "CVE-2021-25288", + "id": "pyup.io-50647", + "more_info_path": "/vulnerabilities/CVE-2021-25288/50647", "specs": [ "<0.0.43" ], @@ -78914,9 +79305,9 @@ }, { "advisory": "Label-studio-converter 0.0.43 updates its dependency 'pillow' to v8.3.1 to include security fixes.", - "cve": "CVE-2021-25289", - "id": "pyup.io-50648", - "more_info_path": "/vulnerabilities/CVE-2021-25289/50648", + "cve": "CVE-2021-28677", + "id": "pyup.io-50645", + "more_info_path": "/vulnerabilities/CVE-2021-28677/50645", "specs": [ "<0.0.43" ], @@ -78924,9 +79315,9 @@ }, { "advisory": "Label-studio-converter 0.0.43 updates its dependency 'pillow' to v8.3.1 to include security fixes.", - "cve": "CVE-2021-25290", - "id": "pyup.io-50649", - "more_info_path": "/vulnerabilities/CVE-2021-25290/50649", + "cve": "CVE-2021-25289", + "id": "pyup.io-50648", + "more_info_path": "/vulnerabilities/CVE-2021-25289/50648", "specs": [ "<0.0.43" ], @@ -78934,9 +79325,9 @@ }, { "advisory": "Label-studio-converter 0.0.43 updates its dependency 'pillow' to v8.3.1 to include security fixes.", - "cve": "CVE-2021-25288", - "id": "pyup.io-50647", - "more_info_path": "/vulnerabilities/CVE-2021-25288/50647", + "cve": "CVE-2021-25291", + "id": "pyup.io-50650", + "more_info_path": "/vulnerabilities/CVE-2021-25291/50650", "specs": [ "<0.0.43" ], @@ -78944,9 +79335,9 @@ }, { "advisory": "Label-studio-converter 0.0.43 updates its dependency 'pillow' to v8.3.1 to include security fixes.", - "cve": "CVE-2021-28677", - "id": "pyup.io-50645", - "more_info_path": "/vulnerabilities/CVE-2021-28677/50645", + "cve": "CVE-2021-25292", + "id": "pyup.io-50651", + "more_info_path": "/vulnerabilities/CVE-2021-25292/50651", "specs": [ "<0.0.43" ], @@ -78964,9 +79355,9 @@ }, { "advisory": "Label-studio-converter 0.0.43 updates its dependency 'pillow' to v8.3.1 to include security fixes.", - "cve": "CVE-2021-34552", - "id": "pyup.io-50641", - "more_info_path": "/vulnerabilities/CVE-2021-34552/50641", + "cve": "CVE-2021-28676", + "id": "pyup.io-50644", + "more_info_path": "/vulnerabilities/CVE-2021-28676/50644", "specs": [ "<0.0.43" ], @@ -78974,9 +79365,9 @@ }, { "advisory": "Label-studio-converter 0.0.43 updates its dependency 'pillow' to v8.3.1 to include security fixes.", - "cve": "CVE-2021-28676", - "id": "pyup.io-50644", - "more_info_path": "/vulnerabilities/CVE-2021-28676/50644", + "cve": "CVE-2021-25290", + "id": "pyup.io-50649", + "more_info_path": "/vulnerabilities/CVE-2021-25290/50649", "specs": [ "<0.0.43" ], @@ -78984,9 +79375,9 @@ }, { "advisory": "Label-studio-converter 0.0.43 updates its dependency 'pillow' to v8.3.1 to include security fixes.", - "cve": "CVE-2021-25293", - "id": "pyup.io-50652", - "more_info_path": "/vulnerabilities/CVE-2021-25293/50652", + "cve": "CVE-2021-34552", + "id": "pyup.io-50641", + "more_info_path": "/vulnerabilities/CVE-2021-34552/50641", "specs": [ "<0.0.43" ], @@ -79038,26 +79429,6 @@ } ], "ladok3": [ - { - "advisory": "Ladok3 4.2 updates its dependency 'pygments' to version '2.15.1' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/dbosk/ladok3/commit/685f2fca6d38960a650060b1bdd13ca88f30b13f", - "cve": "CVE-2022-40896", - "id": "pyup.io-60161", - "more_info_path": "/vulnerabilities/CVE-2022-40896/60161", - "specs": [ - "<4.2" - ], - "v": "<4.2" - }, - { - "advisory": "Ladok3 4.2 updates its dependency 'cryptography' to version '38.0.4' to include a fix for a Buffer Overflow vulnerability.\r\nhttps://github.com/dbosk/ladok3/commit/685f2fca6d38960a650060b1bdd13ca88f30b13f", - "cve": "CVE-2022-3786", - "id": "pyup.io-60137", - "more_info_path": "/vulnerabilities/CVE-2022-3786/60137", - "specs": [ - "<4.2" - ], - "v": "<4.2" - }, { "advisory": "Ladok3 4.2 updates its dependency 'requests' to version '2.31.0' to include a fix for an Information Exposure vulnerability.\r\nhttps://github.com/dbosk/ladok3/commit/685f2fca6d38960a650060b1bdd13ca88f30b13f", "cve": "CVE-2023-32681", @@ -79079,10 +79450,10 @@ "v": "<4.2" }, { - "advisory": "Ladok3 4.2 updates its dependency 'certifi' to version '2023.7.22' to include a fix for a vulnerability.\r\nhttps://github.com/dbosk/ladok3/commit/685f2fca6d38960a650060b1bdd13ca88f30b13f", - "cve": "CVE-2023-37920", - "id": "pyup.io-60159", - "more_info_path": "/vulnerabilities/CVE-2023-37920/60159", + "advisory": "Ladok3 4.2 updates its dependency 'cryptography' to version '38.0.4' to include a fix for a Buffer Overflow vulnerability.\r\nhttps://github.com/dbosk/ladok3/commit/685f2fca6d38960a650060b1bdd13ca88f30b13f", + "cve": "CVE-2022-3786", + "id": "pyup.io-60137", + "more_info_path": "/vulnerabilities/CVE-2022-3786/60137", "specs": [ "<4.2" ], @@ -79099,30 +79470,30 @@ "v": "<4.2" }, { - "advisory": "Ladok3 4.3 updates its dependency 'cryptography' to version '39.0.1' to include a fix for an Expected Behavior Violation vulnerability.\r\nhttps://github.com/dbosk/ladok3/commit/33acab13bd48e3efdcf65493364eb06bf63decd6", - "cve": "CVE-2023-23931", - "id": "pyup.io-60566", - "more_info_path": "/vulnerabilities/CVE-2023-23931/60566", + "advisory": "Ladok3 4.2 updates its dependency 'certifi' to version '2023.7.22' to include a fix for a vulnerability.\r\nhttps://github.com/dbosk/ladok3/commit/685f2fca6d38960a650060b1bdd13ca88f30b13f", + "cve": "CVE-2023-37920", + "id": "pyup.io-60159", + "more_info_path": "/vulnerabilities/CVE-2023-37920/60159", "specs": [ - "<4.3" + "<4.2" ], - "v": "<4.3" + "v": "<4.2" }, { - "advisory": "Ladok3 4.3 updates its dependency 'cryptography' to version '39.0.1' to include a fix for a Timing Attack vulnerability.\r\nhttps://github.com/dbosk/ladok3/commit/33acab13bd48e3efdcf65493364eb06bf63decd6", - "cve": "CVE-2022-4304", - "id": "pyup.io-60562", - "more_info_path": "/vulnerabilities/CVE-2022-4304/60562", + "advisory": "Ladok3 4.2 updates its dependency 'pygments' to version '2.15.1' to include a fix for a ReDoS vulnerability.\r\nhttps://github.com/dbosk/ladok3/commit/685f2fca6d38960a650060b1bdd13ca88f30b13f", + "cve": "CVE-2022-40896", + "id": "pyup.io-60161", + "more_info_path": "/vulnerabilities/CVE-2022-40896/60161", "specs": [ - "<4.3" + "<4.2" ], - "v": "<4.3" + "v": "<4.2" }, { "advisory": "Ladok3 4.3 updates its dependency 'cryptography' to version '39.0.1' to include a fix for a DoS vulnerability.\r\nhttps://github.com/dbosk/ladok3/commit/33acab13bd48e3efdcf65493364eb06bf63decd6", - "cve": "CVE-2023-0217", - "id": "pyup.io-60559", - "more_info_path": "/vulnerabilities/CVE-2023-0217/60559", + "cve": "CVE-2022-4203", + "id": "pyup.io-60564", + "more_info_path": "/vulnerabilities/CVE-2022-4203/60564", "specs": [ "<4.3" ], @@ -79150,9 +79521,9 @@ }, { "advisory": "Ladok3 4.3 updates its dependency 'cryptography' to version '39.0.1' to include a fix for a DoS vulnerability.\r\nhttps://github.com/dbosk/ladok3/commit/33acab13bd48e3efdcf65493364eb06bf63decd6", - "cve": "CVE-2023-0216", - "id": "pyup.io-60563", - "more_info_path": "/vulnerabilities/CVE-2023-0216/60563", + "cve": "CVE-2023-0217", + "id": "pyup.io-60559", + "more_info_path": "/vulnerabilities/CVE-2023-0217/60559", "specs": [ "<4.3" ], @@ -79160,9 +79531,9 @@ }, { "advisory": "Ladok3 4.3 updates its dependency 'cryptography' to version '39.0.1' to include a fix for a DoS vulnerability.\r\nhttps://github.com/dbosk/ladok3/commit/33acab13bd48e3efdcf65493364eb06bf63decd6", - "cve": "CVE-2022-4203", - "id": "pyup.io-60564", - "more_info_path": "/vulnerabilities/CVE-2022-4203/60564", + "cve": "CVE-2023-0216", + "id": "pyup.io-60563", + "more_info_path": "/vulnerabilities/CVE-2023-0216/60563", "specs": [ "<4.3" ], @@ -79188,6 +79559,26 @@ ], "v": "<4.3" }, + { + "advisory": "Ladok3 4.3 updates its dependency 'cryptography' to version '39.0.1' to include a fix for a Timing Attack vulnerability.\r\nhttps://github.com/dbosk/ladok3/commit/33acab13bd48e3efdcf65493364eb06bf63decd6", + "cve": "CVE-2022-4304", + "id": "pyup.io-60562", + "more_info_path": "/vulnerabilities/CVE-2022-4304/60562", + "specs": [ + "<4.3" + ], + "v": "<4.3" + }, + { + "advisory": "Ladok3 4.3 updates its dependency 'cryptography' to version '39.0.1' to include a fix for an Expected Behavior Violation vulnerability.\r\nhttps://github.com/dbosk/ladok3/commit/33acab13bd48e3efdcf65493364eb06bf63decd6", + "cve": "CVE-2023-23931", + "id": "pyup.io-60566", + "more_info_path": "/vulnerabilities/CVE-2023-23931/60566", + "specs": [ + "<4.3" + ], + "v": "<4.3" + }, { "advisory": "Ladok3 4.3 updates its dependency 'cryptography' to version '39.0.1' to include a fix for a DoS vulnerability.\r\nhttps://github.com/dbosk/ladok3/commit/33acab13bd48e3efdcf65493364eb06bf63decd6", "cve": "CVE-2022-3996", @@ -79347,20 +79738,20 @@ "v": "<0.0.236" }, { - "advisory": "Affected versions of Langchain allow an attacker to execute arbitrary code via the PALChain in the python exec method. The PALChain class requires unique security considerations so it was moved langchain-experimental package and removed from langchain on version 0.0.247. The issue was attempted to be resolved several times in langchain-experimental but the fixes were found incomplete. See CVE-2023-44467, CVE-2024-27444, and CVE-2024-38459.", - "cve": "CVE-2023-36258", - "id": "pyup.io-59294", - "more_info_path": "/vulnerabilities/CVE-2023-36258/59294", + "advisory": "Langchain 0.0.247 includes a fix for CVE-2023-36189: SQL injection vulnerability allows a remote attacker to obtain sensitive information via the SQLDatabaseChain component.\r\nhttps://github.com/langchain-ai/langchain/issues/5923", + "cve": "CVE-2023-36189", + "id": "pyup.io-60080", + "more_info_path": "/vulnerabilities/CVE-2023-36189/60080", "specs": [ "<0.0.247" ], "v": "<0.0.247" }, { - "advisory": "Langchain 0.0.247 includes a fix for CVE-2023-36189: SQL injection vulnerability allows a remote attacker to obtain sensitive information via the SQLDatabaseChain component.\r\nhttps://github.com/langchain-ai/langchain/issues/5923", - "cve": "CVE-2023-36189", - "id": "pyup.io-60080", - "more_info_path": "/vulnerabilities/CVE-2023-36189/60080", + "advisory": "Affected versions of Langchain allow an attacker to execute arbitrary code via the PALChain in the python exec method. The PALChain class requires unique security considerations so it was moved langchain-experimental package and removed from langchain on version 0.0.247. The issue was attempted to be resolved several times in langchain-experimental but the fixes were found incomplete. See CVE-2023-44467, CVE-2024-27444, and CVE-2024-38459.", + "cve": "CVE-2023-36258", + "id": "pyup.io-59294", + "more_info_path": "/vulnerabilities/CVE-2023-36258/59294", "specs": [ "<0.0.247" ], @@ -79559,7 +79950,7 @@ "v": "<0.2.4" }, { - "advisory": "Denial of service in SitemapLoader Document Loader in the langchain-community package, affecting versions below 0.2.5. The parse_sitemap method, responsible for parsing sitemaps and extracting URLs, lacks a mechanism to prevent infinite recursion when a sitemap URL refers to the current sitemap itself. This oversight allows for the possibility of an infinite loop, leading to a crash by exceeding the maximum recursion depth in Python. This vulnerability can be exploited to occupy server socket/port resources and crash the Python process, impacting the availability of services relying on this functionality.", + "advisory": "Affected versions of Langchain-community are vulnerable to Denial of service in SitemapLoader Document Loader. The parse_sitemap method, responsible for parsing sitemaps and extracting URLs, lacks a mechanism to prevent infinite recursion when a sitemap URL refers to the current sitemap itself. This oversight allows for the possibility of an infinite loop, leading to a crash by exceeding the maximum recursion depth in Python. This vulnerability can be exploited to occupy server socket/port resources and crash the Python process, impacting the availability of services relying on this functionality.", "cve": "CVE-2024-2965", "id": "pyup.io-71614", "more_info_path": "/vulnerabilities/CVE-2024-2965/71614", @@ -79579,7 +79970,7 @@ "v": "<0.2.9" }, { - "advisory": "Affected versions of langchain-ai/langchain are vulnerable to SQL injection through GraphCypherQAChain class. This vulnerability allows attackers to manipulate database queries via malicious input in prompts, potentially leading to unauthorized data access, manipulation, and cross-tenant data breaches. The vulnerability exists in the query processing logic of GraphCypherQAChain where user input is not properly sanitized. Successfully exploiting this requires access to the API endpoint. Users should upgrade to version 0.2.6 or later which includes input sanitization fixes.", + "advisory": "Affected versions of langchain-ai/langchain are vulnerable to SQL injection through GraphCypherQAChain class. This vulnerability allows attackers to manipulate database queries via malicious input in prompts, potentially leading to unauthorized data access, manipulation, and cross-tenant data breaches. The vulnerability exists in the query processing logic of GraphCypherQAChain where user input is not properly sanitized. Successfully exploiting this requires access to the API endpoint.", "cve": "CVE-2024-8309", "id": "pyup.io-73959", "more_info_path": "/vulnerabilities/CVE-2024-8309/73959", @@ -79773,6 +80164,19 @@ "v": "<0.18.7" } ], + "lapnet": [ + { + "advisory": "The OpenSSF Package Analysis project has identified the PyPI package 'lapnet' version 99.6 as malicious because it communicates with a domain associated with malicious activity, indicating potential security risks.", + "cve": "PVE-2024-74245", + "id": "pyup.io-74245", + "more_info_path": "/vulnerabilities/PVE-2024-74245/74245", + "specs": [ + ">=0", + "<=0" + ], + "v": ">=0,<=0" + } + ], "laporte-mqtt": [ { "advisory": "Laporte-Mqtt 0.2.2 includes a security patch for the function 'load_config' in 'laporte_mqtt/config.py'. It used the unsafe yaml.load(), that allows instantiation of arbitrary objects. Consider yaml.safe_load().\r\nhttps://github.com/vinklat/laporte-mqtt/commit/db24ded1c1ed0232c42bb826b9c1dc30fb925f2e", @@ -79875,30 +80279,30 @@ "v": "<2021.2.1" }, { - "advisory": "Layeredimage 2021.2.1 updates its dependency 'pillow' to a version >= 8.1.1 to include security fixes.", - "cve": "CVE-2021-27923", - "id": "pyup.io-40327", - "more_info_path": "/vulnerabilities/CVE-2021-27923/40327", + "advisory": "Layeredimage 2021.2.1 updates the 'Pillow' dependency >= 8.1.1 due to high severity security vulnerabilities (CVE-2020-35655).", + "cve": "CVE-2020-35655", + "id": "pyup.io-40332", + "more_info_path": "/vulnerabilities/CVE-2020-35655/40332", "specs": [ "<2021.2.1" ], "v": "<2021.2.1" }, { - "advisory": "Layeredimage 2021.2.1 updates its dependency 'pillow' to a version >= 8.1.1 to include security fixes.", - "cve": "CVE-2021-27921", - "id": "pyup.io-40330", - "more_info_path": "/vulnerabilities/CVE-2021-27921/40330", + "advisory": "Layeredimage 2021.2.1 updates the 'Pillow' dependency >= 8.1.1 due to high severity security vulnerabilities (CVE-2020-35654).", + "cve": "CVE-2020-35654", + "id": "pyup.io-40328", + "more_info_path": "/vulnerabilities/CVE-2020-35654/40328", "specs": [ "<2021.2.1" ], "v": "<2021.2.1" }, { - "advisory": "Layeredimage 2021.2.1 updates the 'Pillow' dependency >= 8.1.1 due to high severity security vulnerabilities (CVE-2020-35655).", - "cve": "CVE-2020-35655", - "id": "pyup.io-40332", - "more_info_path": "/vulnerabilities/CVE-2020-35655/40332", + "advisory": "Layeredimage 2021.2.1 updates its dependency 'pillow' to a version >= 8.1.1 to include security fixes.", + "cve": "CVE-2021-27923", + "id": "pyup.io-40327", + "more_info_path": "/vulnerabilities/CVE-2021-27923/40327", "specs": [ "<2021.2.1" ], @@ -79906,19 +80310,19 @@ }, { "advisory": "Layeredimage 2021.2.1 updates its dependency 'pillow' to a version >= 8.1.1 to include security fixes.", - "cve": "CVE-2021-27922", - "id": "pyup.io-40331", - "more_info_path": "/vulnerabilities/CVE-2021-27922/40331", + "cve": "CVE-2021-27921", + "id": "pyup.io-40330", + "more_info_path": "/vulnerabilities/CVE-2021-27921/40330", "specs": [ "<2021.2.1" ], "v": "<2021.2.1" }, { - "advisory": "Layeredimage 2021.2.1 updates the 'Pillow' dependency >= 8.1.1 due to high severity security vulnerabilities (CVE-2020-35654).", - "cve": "CVE-2020-35654", - "id": "pyup.io-40328", - "more_info_path": "/vulnerabilities/CVE-2020-35654/40328", + "advisory": "Layeredimage 2021.2.1 updates its dependency 'pillow' to a version >= 8.1.1 to include security fixes.", + "cve": "CVE-2021-27922", + "id": "pyup.io-40331", + "more_info_path": "/vulnerabilities/CVE-2021-27922/40331", "specs": [ "<2021.2.1" ], @@ -79937,70 +80341,70 @@ "v": "<2.2.2" }, { - "advisory": "Lazuli 2.2.3 updates its dependency 'nltk' to v3.7 to include security fixes.", - "cve": "CVE-2021-43854", - "id": "pyup.io-50484", - "more_info_path": "/vulnerabilities/CVE-2021-43854/50484", + "advisory": "Lazuli 2.2.3 updates its dependency 'urllib3' to v1.26.11 to include a security fix.", + "cve": "CVE-2021-33503", + "id": "pyup.io-50479", + "more_info_path": "/vulnerabilities/CVE-2021-33503/50479", "specs": [ "<2.2.3" ], "v": "<2.2.3" }, { - "advisory": "Lazuli 2.2.3 updates its dependency 'nltk' to v3.7 to include security fixes.", - "cve": "CVE-2021-3842", - "id": "pyup.io-50483", - "more_info_path": "/vulnerabilities/CVE-2021-3842/50483", + "advisory": "Lazuli 2.2.3 updates its dependency 'pygments' to v2.12.0 to include a security fix.", + "cve": "CVE-2021-20270", + "id": "pyup.io-50480", + "more_info_path": "/vulnerabilities/CVE-2021-20270/50480", "specs": [ "<2.2.3" ], "v": "<2.2.3" }, { - "advisory": "Lazuli 2.2.3 updates its dependency 'nltk' to v3.7 to include security fixes.", - "cve": "CVE-2021-3828", - "id": "pyup.io-50482", - "more_info_path": "/vulnerabilities/CVE-2021-3828/50482", + "advisory": "Lazuli 2.2.3 updates its dependency 'urllib3' to v1.26.11 to include a security fix.", + "cve": "CVE-2021-28363", + "id": "pyup.io-50478", + "more_info_path": "/vulnerabilities/CVE-2021-28363/50478", "specs": [ "<2.2.3" ], "v": "<2.2.3" }, { - "advisory": "Lazuli 2.2.3 updates its dependency 'pygments' to v2.12.0 to include a security fix.", - "cve": "CVE-2021-20270", - "id": "pyup.io-50480", - "more_info_path": "/vulnerabilities/CVE-2021-20270/50480", + "advisory": "Lazuli 2.2.3 updates its dependency 'jinja2' to v2.11.3 to include a security fix.", + "cve": "CVE-2020-28493", + "id": "pyup.io-50477", + "more_info_path": "/vulnerabilities/CVE-2020-28493/50477", "specs": [ "<2.2.3" ], "v": "<2.2.3" }, { - "advisory": "Lazuli 2.2.3 updates its dependency 'mkdocs' to v1.3.1 to include a security fix.", - "cve": "CVE-2021-40978", - "id": "pyup.io-50476", - "more_info_path": "/vulnerabilities/CVE-2021-40978/50476", + "advisory": "Lazuli 2.2.3 updates its dependency 'pygments' to v2.12.0 to include a security fix.", + "cve": "CVE-2021-27291", + "id": "pyup.io-50481", + "more_info_path": "/vulnerabilities/CVE-2021-27291/50481", "specs": [ "<2.2.3" ], "v": "<2.2.3" }, { - "advisory": "Lazuli 2.2.3 updates its dependency 'pygments' to v2.12.0 to include a security fix.", - "cve": "CVE-2021-27291", - "id": "pyup.io-50481", - "more_info_path": "/vulnerabilities/CVE-2021-27291/50481", + "advisory": "Lazuli 2.2.3 updates its dependency 'mkdocs' to v1.3.1 to include a security fix.", + "cve": "CVE-2021-40978", + "id": "pyup.io-50476", + "more_info_path": "/vulnerabilities/CVE-2021-40978/50476", "specs": [ "<2.2.3" ], "v": "<2.2.3" }, { - "advisory": "Lazuli 2.2.3 updates its dependency 'jinja2' to v2.11.3 to include a security fix.", - "cve": "CVE-2020-28493", - "id": "pyup.io-50477", - "more_info_path": "/vulnerabilities/CVE-2020-28493/50477", + "advisory": "Lazuli 2.2.3 updates its dependency 'nltk' to v3.7 to include security fixes.", + "cve": "CVE-2021-3828", + "id": "pyup.io-50482", + "more_info_path": "/vulnerabilities/CVE-2021-3828/50482", "specs": [ "<2.2.3" ], @@ -80017,20 +80421,20 @@ "v": "<2.2.3" }, { - "advisory": "Lazuli 2.2.3 updates its dependency 'urllib3' to v1.26.11 to include a security fix.", - "cve": "CVE-2021-33503", - "id": "pyup.io-50479", - "more_info_path": "/vulnerabilities/CVE-2021-33503/50479", + "advisory": "Lazuli 2.2.3 updates its dependency 'nltk' to v3.7 to include security fixes.", + "cve": "CVE-2021-3842", + "id": "pyup.io-50483", + "more_info_path": "/vulnerabilities/CVE-2021-3842/50483", "specs": [ "<2.2.3" ], "v": "<2.2.3" }, { - "advisory": "Lazuli 2.2.3 updates its dependency 'urllib3' to v1.26.11 to include a security fix.", - "cve": "CVE-2021-28363", - "id": "pyup.io-50478", - "more_info_path": "/vulnerabilities/CVE-2021-28363/50478", + "advisory": "Lazuli 2.2.3 updates its dependency 'nltk' to v3.7 to include security fixes.", + "cve": "CVE-2021-43854", + "id": "pyup.io-50484", + "more_info_path": "/vulnerabilities/CVE-2021-43854/50484", "specs": [ "<2.2.3" ], @@ -80312,9 +80716,9 @@ "lg-rez": [ { "advisory": "Lg-rez version 2.1.4 updates its dependency 'pillow' to v8.2.0 to include security fixes.", - "cve": "CVE-2021-28676", - "id": "pyup.io-42104", - "more_info_path": "/vulnerabilities/CVE-2021-28676/42104", + "cve": "CVE-2021-28675", + "id": "pyup.io-42103", + "more_info_path": "/vulnerabilities/CVE-2021-28675/42103", "specs": [ "<2.1.4" ], @@ -80331,10 +80735,10 @@ "v": "<2.1.4" }, { - "advisory": "Lg-rez 2.1.4 updates its dependency 'rsa' to v4.7 to include a security fix.", - "cve": "CVE-2020-25658", - "id": "pyup.io-42106", - "more_info_path": "/vulnerabilities/CVE-2020-25658/42106", + "advisory": "Lg-rez version 2.1.4 updates its dependency 'pillow' to v8.2.0 to include security fixes.", + "cve": "CVE-2021-28676", + "id": "pyup.io-42104", + "more_info_path": "/vulnerabilities/CVE-2021-28676/42104", "specs": [ "<2.1.4" ], @@ -80342,19 +80746,19 @@ }, { "advisory": "Lg-rez version 2.1.4 updates its dependency 'pillow' to v8.2.0 to include security fixes.", - "cve": "CVE-2021-28675", - "id": "pyup.io-42103", - "more_info_path": "/vulnerabilities/CVE-2021-28675/42103", + "cve": "CVE-2021-28678", + "id": "pyup.io-42049", + "more_info_path": "/vulnerabilities/CVE-2021-28678/42049", "specs": [ "<2.1.4" ], "v": "<2.1.4" }, { - "advisory": "Lg-rez version 2.1.4 updates its dependency 'pillow' to v8.2.0 to include security fixes.", - "cve": "CVE-2021-28678", - "id": "pyup.io-42049", - "more_info_path": "/vulnerabilities/CVE-2021-28678/42049", + "advisory": "Lg-rez 2.1.4 updates its dependency 'rsa' to v4.7 to include a security fix.", + "cve": "CVE-2020-25658", + "id": "pyup.io-42106", + "more_info_path": "/vulnerabilities/CVE-2020-25658/42106", "specs": [ "<2.1.4" ], @@ -80413,20 +80817,20 @@ ], "libgenesis": [ { - "advisory": "Libgenesis 0.1.8 updates its dependency 'lxml' minimum version to v4.6.5 to include security fixes.", - "cve": "CVE-2020-27783", - "id": "pyup.io-43386", - "more_info_path": "/vulnerabilities/CVE-2020-27783/43386", + "advisory": "Libgenesis 0.1.8 updates its dependency 'lxml' to v4.6.5 to include a security fix.", + "cve": "CVE-2021-43818", + "id": "pyup.io-43379", + "more_info_path": "/vulnerabilities/CVE-2021-43818/43379", "specs": [ "<0.1.8" ], "v": "<0.1.8" }, { - "advisory": "Libgenesis 0.1.8 updates its dependency 'lxml' to v4.6.5 to include a security fix.", - "cve": "CVE-2021-43818", - "id": "pyup.io-43379", - "more_info_path": "/vulnerabilities/CVE-2021-43818/43379", + "advisory": "Libgenesis 0.1.8 updates its dependency 'lxml' minimum version to v4.6.5 to include security fixes.", + "cve": "CVE-2020-27783", + "id": "pyup.io-43386", + "more_info_path": "/vulnerabilities/CVE-2020-27783/43386", "specs": [ "<0.1.8" ], @@ -80542,10 +80946,10 @@ ], "libretranslate": [ { - "advisory": "Libretranslate 1.5.4 updates its Requests dependency from 2.28.1 to 2.31.0. This upgrade addresses the vulnerability identified as CVE-2023-32681.\r\nhttps://github.com/LibreTranslate/LibreTranslate/pull/570/commits/51341d92ade55e47d94ac2f6dd095fcc226e62d0", - "cve": "CVE-2023-32681", - "id": "pyup.io-64073", - "more_info_path": "/vulnerabilities/CVE-2023-32681/64073", + "advisory": "Libretranslate 1.5.4 updates its Flask dependency from 2.2.2 to 2.2.5. This upgrade addresses the vulnerability identified as CVE-2023-30861.\r\nhttps://github.com/LibreTranslate/LibreTranslate/pull/570/commits/51341d92ade55e47d94ac2f6dd095fcc226e62d0", + "cve": "CVE-2023-30861", + "id": "pyup.io-63742", + "more_info_path": "/vulnerabilities/CVE-2023-30861/63742", "specs": [ "<1.5.4" ], @@ -80562,10 +80966,10 @@ "v": "<1.5.4" }, { - "advisory": "Libretranslate 1.5.4 updates its Flask dependency from 2.2.2 to 2.2.5. This upgrade addresses the vulnerability identified as CVE-2023-30861.\r\nhttps://github.com/LibreTranslate/LibreTranslate/pull/570/commits/51341d92ade55e47d94ac2f6dd095fcc226e62d0", - "cve": "CVE-2023-30861", - "id": "pyup.io-63742", - "more_info_path": "/vulnerabilities/CVE-2023-30861/63742", + "advisory": "Libretranslate 1.5.4 updates its Requests dependency from 2.28.1 to 2.31.0. This upgrade addresses the vulnerability identified as CVE-2023-32681.\r\nhttps://github.com/LibreTranslate/LibreTranslate/pull/570/commits/51341d92ade55e47d94ac2f6dd095fcc226e62d0", + "cve": "CVE-2023-32681", + "id": "pyup.io-64073", + "more_info_path": "/vulnerabilities/CVE-2023-32681/64073", "specs": [ "<1.5.4" ], @@ -80632,37 +81036,40 @@ ], "libyang": [ { - "advisory": "In function read_yin_container() in libyang <= v1.0.225 or possibly <= 1.0.240 doesn't check whether the value of retval->ext[r] is NULL. In some cases, it can be NULL, which leads to the operation of retval->ext[r]->flags that results in a crash.", - "cve": "CVE-2021-28902", - "id": "pyup.io-62213", - "more_info_path": "/vulnerabilities/CVE-2021-28902/62213", + "advisory": "A stack overflow in libyang <= v1.0.225 or possibly <= 1.0.240 can cause a denial of service through function lyxml_parse_mem(). lyxml_parse_elem() function will be called recursively, which will consume stack space and lead to crash.", + "cve": "CVE-2021-28903", + "id": "pyup.io-62212", + "more_info_path": "/vulnerabilities/CVE-2021-28903/62212", "specs": [ "<1.0.240" ], "v": "<1.0.240" }, { - "advisory": "A stack overflow in libyang <= v1.0.225 or possibly <= 1.0.240 can cause a denial of service through function lyxml_parse_mem(). lyxml_parse_elem() function will be called recursively, which will consume stack space and lead to crash.", - "cve": "CVE-2021-28903", - "id": "pyup.io-62212", - "more_info_path": "/vulnerabilities/CVE-2021-28903/62212", + "advisory": "In function read_yin_container() in libyang <= v1.0.225 or possibly <= 1.0.240 doesn't check whether the value of retval->ext[r] is NULL. In some cases, it can be NULL, which leads to the operation of retval->ext[r]->flags that results in a crash.", + "cve": "CVE-2021-28902", + "id": "pyup.io-62213", + "more_info_path": "/vulnerabilities/CVE-2021-28902/62213", "specs": [ "<1.0.240" ], "v": "<1.0.240" } ], - "licenseware": [ + "licensemonitor": [ { - "advisory": "Licenseware 2.0.0 updates its dependency 'libexpat1' in the Dockerfile to include security fixes.\r\nhttps://github.com/licenseware/licenseware-sdk-v2/pull/59", - "cve": "CVE-2022-25236", - "id": "pyup.io-51853", - "more_info_path": "/vulnerabilities/CVE-2022-25236/51853", + "advisory": "The OpenSSF Package Analysis project has identified the PyPI package 'licensemonitor' version 99.6 as malicious because it communicates with a domain associated with malicious activity, indicating potential security risks.", + "cve": "PVE-2024-74249", + "id": "pyup.io-74249", + "more_info_path": "/vulnerabilities/PVE-2024-74249/74249", "specs": [ - "<2.0.0" + ">=0", + "<=0" ], - "v": "<2.0.0" - }, + "v": ">=0,<=0" + } + ], + "licenseware": [ { "advisory": "Licenseware 2.0.0 updates its dependency 'libexpat1' in the Dockerfile to include security fixes.\r\nhttps://github.com/licenseware/licenseware-sdk-v2/pull/59", "cve": "CVE-2022-25314", @@ -80695,9 +81102,9 @@ }, { "advisory": "Licenseware 2.0.0 updates its dependency 'libexpat1' in the Dockerfile to include security fixes.\r\nhttps://github.com/licenseware/licenseware-sdk-v2/pull/59", - "cve": "CVE-2022-25315", - "id": "pyup.io-51857", - "more_info_path": "/vulnerabilities/CVE-2022-25315/51857", + "cve": "CVE-2022-25235", + "id": "pyup.io-51854", + "more_info_path": "/vulnerabilities/CVE-2022-25235/51854", "specs": [ "<2.0.0" ], @@ -80705,9 +81112,9 @@ }, { "advisory": "Licenseware 2.0.0 updates its dependency 'libexpat1' in the Dockerfile to include security fixes.\r\nhttps://github.com/licenseware/licenseware-sdk-v2/pull/59", - "cve": "CVE-2022-25235", - "id": "pyup.io-51854", - "more_info_path": "/vulnerabilities/CVE-2022-25235/51854", + "cve": "CVE-2022-25315", + "id": "pyup.io-51857", + "more_info_path": "/vulnerabilities/CVE-2022-25315/51857", "specs": [ "<2.0.0" ], @@ -80723,6 +81130,16 @@ ], "v": "<2.0.0" }, + { + "advisory": "Licenseware 2.0.0 updates its dependency 'libexpat1' in the Dockerfile to include security fixes.\r\nhttps://github.com/licenseware/licenseware-sdk-v2/pull/59", + "cve": "CVE-2022-25236", + "id": "pyup.io-51853", + "more_info_path": "/vulnerabilities/CVE-2022-25236/51853", + "specs": [ + "<2.0.0" + ], + "v": "<2.0.0" + }, { "advisory": "Licenseware 2.4.7 adds XSS/HTTPS security headers.\r\nhttps://github.com/licenseware/licenseware-sdk-v2/pull/310", "cve": "PVE-2022-52374", @@ -80745,16 +81162,6 @@ ], "v": "<0.11.0" }, - { - "advisory": "A vulnerability in the LIEF::MachO::BinaryParser::init_and_parse function allows attackers to cause a denial of service (DOS) through a segmentation fault via a crafted MachO file. A [patch](https://github.com/lief-project/LIEF/commit/fde2c48986739fabd2cf9b40b9af149a89c57850) for this issue is available at commit fde2c48986739fabd2cf9b40b9af149a89c57850.", - "cve": "CVE-2022-40922", - "id": "pyup.io-54501", - "more_info_path": "/vulnerabilities/CVE-2022-40922/54501", - "specs": [ - "<0.12.3" - ], - "v": "<0.12.3" - }, { "advisory": "A bad macho file can lead LIEF::MachO::Parser::parse() to segmentation fault. That may open up for Denial of Service (Dos) attacks.\r\nhttps://github.com/lief-project/LIEF/issues/806", "cve": "PVE-2024-64373", @@ -80775,6 +81182,16 @@ ], "v": "<0.12.3" }, + { + "advisory": "A vulnerability in the LIEF::MachO::BinaryParser::init_and_parse function allows attackers to cause a denial of service (DOS) through a segmentation fault via a crafted MachO file. A [patch](https://github.com/lief-project/LIEF/commit/fde2c48986739fabd2cf9b40b9af149a89c57850) for this issue is available at commit fde2c48986739fabd2cf9b40b9af149a89c57850.", + "cve": "CVE-2022-40922", + "id": "pyup.io-54501", + "more_info_path": "/vulnerabilities/CVE-2022-40922/54501", + "specs": [ + "<0.12.3" + ], + "v": "<0.12.3" + }, { "advisory": "LIEF commit 365a16a was discovered to contain a segmentation violation via the component CoreFile.tcc:69.", "cve": "CVE-2022-38497", @@ -80924,20 +81341,20 @@ ], "lightning": [ { - "advisory": "Lightning 2.0.4 updates its dependency 'redis' to version '4.5.5' to include a security fix.\r\nhttps://github.com/Lightning-AI/lightning/commit/0831e138c02e492bdd384be99079d949c93b1e8e", - "cve": "CVE-2023-28858", - "id": "pyup.io-59186", - "more_info_path": "/vulnerabilities/CVE-2023-28858/59186", + "advisory": "Lightning 2.0.4 updates its dependency 'ipython' to version '8.14.0' to include a security fix.\r\nhttps://github.com/Lightning-AI/lightning/commit/98e1aabd0c711e508d33f599265de011ca5dfba8", + "cve": "CVE-2023-24816", + "id": "pyup.io-59170", + "more_info_path": "/vulnerabilities/CVE-2023-24816/59170", "specs": [ "<2.0.4" ], "v": "<2.0.4" }, { - "advisory": "Lightning 2.0.4 updates its dependency 'ipython' to version '8.14.0' to include a security fix.\r\nhttps://github.com/Lightning-AI/lightning/commit/98e1aabd0c711e508d33f599265de011ca5dfba8", - "cve": "CVE-2023-24816", - "id": "pyup.io-59170", - "more_info_path": "/vulnerabilities/CVE-2023-24816/59170", + "advisory": "Lightning 2.0.4 updates its dependency 'vite' to version '2.9.16' to include a security fix.\r\nhttps://github.com/Lightning-AI/lightning/commit/7d2a46efa9834c3bb0bc1069df0a2e3a6e855d01", + "cve": "CVE-2023-34092", + "id": "pyup.io-59184", + "more_info_path": "/vulnerabilities/CVE-2023-34092/59184", "specs": [ "<2.0.4" ], @@ -80964,10 +81381,10 @@ "v": "<2.0.4" }, { - "advisory": "Lightning 2.0.4 updates its dependency 'vite' to version '2.9.16' to include a security fix.\r\nhttps://github.com/Lightning-AI/lightning/commit/7d2a46efa9834c3bb0bc1069df0a2e3a6e855d01", - "cve": "CVE-2023-34092", - "id": "pyup.io-59184", - "more_info_path": "/vulnerabilities/CVE-2023-34092/59184", + "advisory": "Lightning 2.0.4 updates its dependency 'redis' to version '4.5.5' to include a security fix.\r\nhttps://github.com/Lightning-AI/lightning/commit/0831e138c02e492bdd384be99079d949c93b1e8e", + "cve": "CVE-2023-28858", + "id": "pyup.io-59186", + "more_info_path": "/vulnerabilities/CVE-2023-28858/59186", "specs": [ "<2.0.4" ], @@ -80996,20 +81413,20 @@ ], "lilac": [ { - "advisory": "Lilac 0.3.7 upgrades its pillow dependency to version ^10.2.0 from ^9.3.0 in response to CVE-2023-50447.\r\nhttps://github.com/lilacai/lilac/pull/1191/commits/493dd721e01019185fa62beb0c162286d24dbbbe", - "cve": "CVE-2023-50447", - "id": "pyup.io-65642", - "more_info_path": "/vulnerabilities/CVE-2023-50447/65642", + "advisory": "Lilac 0.3.7 upgrades its pyarrow dependency to version ^13.0.0 from ^14.0.1 in response to CVE-2023-47248.\r\nhttps://github.com/lilacai/lilac/pull/1191/commits/493dd721e01019185fa62beb0c162286d24dbbbe", + "cve": "CVE-2023-47248", + "id": "pyup.io-65676", + "more_info_path": "/vulnerabilities/CVE-2023-47248/65676", "specs": [ "<0.3.7" ], "v": "<0.3.7" }, { - "advisory": "Lilac 0.3.7 upgrades its pyarrow dependency to version ^13.0.0 from ^14.0.1 in response to CVE-2023-47248.\r\nhttps://github.com/lilacai/lilac/pull/1191/commits/493dd721e01019185fa62beb0c162286d24dbbbe", - "cve": "CVE-2023-47248", - "id": "pyup.io-65676", - "more_info_path": "/vulnerabilities/CVE-2023-47248/65676", + "advisory": "Lilac 0.3.7 upgrades its pillow dependency to version ^10.2.0 from ^9.3.0 in response to CVE-2023-50447.\r\nhttps://github.com/lilacai/lilac/pull/1191/commits/493dd721e01019185fa62beb0c162286d24dbbbe", + "cve": "CVE-2023-50447", + "id": "pyup.io-65642", + "more_info_path": "/vulnerabilities/CVE-2023-50447/65642", "specs": [ "<0.3.7" ], @@ -81104,20 +81521,20 @@ ], "lin-cms-flask": [ { - "advisory": "Lin-CMS-flask allows remote attackers to launch brute force login attempts without restriction via the 'login' function in the component 'app/api/cms/user.py'.\r\nhttps://github.com/TaleLin/lin-cms-flask/issues/27", - "cve": "CVE-2020-18698", - "id": "pyup.io-45611", - "more_info_path": "/vulnerabilities/CVE-2020-18698/45611", + "advisory": "Lin-CMS-flask allows remote attackers to obtain sensitive information and/or gain privileges due to the application not invalidating a user's authentication token upon logout, which allows for replaying packets.\r\nhttps://github.com/TaleLin/lin-cms-flask/issues/30", + "cve": "CVE-2020-18701", + "id": "pyup.io-45615", + "more_info_path": "/vulnerabilities/CVE-2020-18701/45615", "specs": [ ">0" ], "v": ">0" }, { - "advisory": "Lin-CMS-flask allows remote attackers to obtain sensitive information and/or gain privileges due to the application not invalidating a user's authentication token upon logout, which allows for replaying packets.\r\nhttps://github.com/TaleLin/lin-cms-flask/issues/30", - "cve": "CVE-2020-18701", - "id": "pyup.io-45615", - "more_info_path": "/vulnerabilities/CVE-2020-18701/45615", + "advisory": "Lin-CMS-flask allows remote attackers to launch brute force login attempts without restriction via the 'login' function in the component 'app/api/cms/user.py'.\r\nhttps://github.com/TaleLin/lin-cms-flask/issues/27", + "cve": "CVE-2020-18698", + "id": "pyup.io-45611", + "more_info_path": "/vulnerabilities/CVE-2020-18698/45611", "specs": [ ">0" ], @@ -81205,16 +81622,6 @@ } ], "lintegrate": [ - { - "advisory": "Lintegrate 0.1.11 updates its dependency 'numpy' to v>=1.21 to include security fixes.", - "cve": "CVE-2021-34141", - "id": "pyup.io-44757", - "more_info_path": "/vulnerabilities/CVE-2021-34141/44757", - "specs": [ - "<0.1.11" - ], - "v": "<0.1.11" - }, { "advisory": "Lintegrate 0.1.11 updates its dependency 'numpy' to v>=1.21 to include security fixes.", "cve": "CVE-2019-6446", @@ -81235,6 +81642,16 @@ ], "v": "<0.1.11" }, + { + "advisory": "Lintegrate 0.1.11 updates its dependency 'numpy' to v>=1.21 to include security fixes.", + "cve": "CVE-2021-34141", + "id": "pyup.io-44757", + "more_info_path": "/vulnerabilities/CVE-2021-34141/44757", + "specs": [ + "<0.1.11" + ], + "v": "<0.1.11" + }, { "advisory": "Lintegrate 0.1.11 may use a vulnerable version of Numpy: >=1.21.0", "cve": "CVE-2021-41496", @@ -81290,20 +81707,20 @@ "v": "<1.35.1.dev1" }, { - "advisory": "Affected versions of BerriAI's litellm are vulnerable to arbitrary file deletion due to improper input validation on the `/audio/transcriptions` endpoint. An attacker can exploit this vulnerability by sending a specially crafted request that includes a file path to the server, which then deletes the specified file without proper authorization or validation. This vulnerability is present in the code where `os.remove(file.filename)` is used to delete a file, allowing any user to delete critical files on the server such as SSH keys, SQLite databases, or configuration files.", - "cve": "CVE-2024-4888", - "id": "pyup.io-71651", - "more_info_path": "/vulnerabilities/CVE-2024-4888/71651", + "advisory": "Affected versions of Litellm are vulnerable to improper authorization. Users could remove files from litellm proxy server when calling /audio/transcriptions.", + "cve": "PVE-2024-68072", + "id": "pyup.io-68072", + "more_info_path": "/vulnerabilities/PVE-2024-68072/68072", "specs": [ "<1.35.18" ], "v": "<1.35.18" }, { - "advisory": "Affected versions of Litellm are vulnerable to improper authorization. Users could remove files from litellm proxy server when calling /audio/transcriptions.", - "cve": "PVE-2024-68072", - "id": "pyup.io-68072", - "more_info_path": "/vulnerabilities/PVE-2024-68072/68072", + "advisory": "Affected versions of BerriAI's litellm are vulnerable to arbitrary file deletion due to improper input validation on the `/audio/transcriptions` endpoint. An attacker can exploit this vulnerability by sending a specially crafted request that includes a file path to the server, which then deletes the specified file without proper authorization or validation. This vulnerability is present in the code where `os.remove(file.filename)` is used to delete a file, allowing any user to delete critical files on the server such as SSH keys, SQLite databases, or configuration files.", + "cve": "CVE-2024-4888", + "id": "pyup.io-71651", + "more_info_path": "/vulnerabilities/CVE-2024-4888/71651", "specs": [ "<1.35.18" ], @@ -81536,20 +81953,20 @@ ], "llama-index-core": [ { - "advisory": "A vulnerability was identified in the `exec_utils` class of the `llama_index` package, specifically within the `safe_eval` function, allowing for prompt injection leading to arbitrary code execution. This issue arises due to insufficient validation of input, which can be exploited to bypass method restrictions and execute unauthorized code. The vulnerability is a bypass of the previously addressed CVE-2023-39662, demonstrated through a proof of concept that creates a file on the system by exploiting the flaw.", - "cve": "CVE-2024-3098", - "id": "pyup.io-71653", - "more_info_path": "/vulnerabilities/CVE-2024-3098/71653", + "advisory": "A command injection vulnerability exists in the run-llama/llama_index repository, specifically within the safe_eval function. Attackers can bypass the intended security mechanism, which checks for the presence of underscores in code generated by LLM, to execute arbitrary code. This is achieved by crafting input that does not contain an underscore but still results in the execution of OS commands. The vulnerability allows for remote code execution (RCE) on the server hosting the application.", + "cve": "CVE-2024-3271", + "id": "pyup.io-71792", + "more_info_path": "/vulnerabilities/CVE-2024-3271/71792", "specs": [ "<0.10.24" ], "v": "<0.10.24" }, { - "advisory": "A command injection vulnerability exists in the run-llama/llama_index repository, specifically within the safe_eval function. Attackers can bypass the intended security mechanism, which checks for the presence of underscores in code generated by LLM, to execute arbitrary code. This is achieved by crafting input that does not contain an underscore but still results in the execution of OS commands. The vulnerability allows for remote code execution (RCE) on the server hosting the application.", - "cve": "CVE-2024-3271", - "id": "pyup.io-71792", - "more_info_path": "/vulnerabilities/CVE-2024-3271/71792", + "advisory": "A vulnerability was identified in the `exec_utils` class of the `llama_index` package, specifically within the `safe_eval` function, allowing for prompt injection leading to arbitrary code execution. This issue arises due to insufficient validation of input, which can be exploited to bypass method restrictions and execute unauthorized code. The vulnerability is a bypass of the previously addressed CVE-2023-39662, demonstrated through a proof of concept that creates a file on the system by exploiting the flaw.", + "cve": "CVE-2024-3098", + "id": "pyup.io-71653", + "more_info_path": "/vulnerabilities/CVE-2024-3098/71653", "specs": [ "<0.10.24" ], @@ -81578,6 +81995,18 @@ "v": "<0.1.3" } ], + "llamafactory": [ + { + "advisory": "A critical remote OS command injection vulnerability exists in Llama Factory due to improper handling of user input. The insecure use of the Popen function with `shell=True` and unsanitized input allows attackers to execute arbitrary OS commands, potentially compromising data, escalating privileges, or deploying malware. Immediate remediation involves avoiding `shell=True` in Popen and passing commands as lists to prevent malicious command execution, thereby mitigating the risk of data breaches and system disruption.", + "cve": "CVE-2024-52803", + "id": "pyup.io-74226", + "more_info_path": "/vulnerabilities/CVE-2024-52803/74226", + "specs": [ + "<=0.9.0" + ], + "v": "<=0.9.0" + } + ], "lmdb": [ { "advisory": "An issue was discovered in py-lmdb 0.97. mdb_node_del does not validate a memmove in the case of an unexpected node->mn_hi, leading to an invalid write operation. NOTE: this outcome occurs when accessing a data.mdb file supplied by an attacker.", @@ -81758,20 +82187,20 @@ "v": "==2.3.2" }, { - "advisory": "Localstack is vulnerable to CVE-2021-32091: A Cross-site scripting (XSS) vulnerability exists in StackLift LocalStack 0.12.6. After disclosure, vendor said that these threats \"are not considered a key concern since LocalStack is executed on a local machine\". There's no information about patches for these vulnerabilities.\r\nhttps://blog.sonarsource.com/hack-the-stack-with-localstack", - "cve": "CVE-2021-32091", - "id": "pyup.io-42836", - "more_info_path": "/vulnerabilities/CVE-2021-32091/42836", + "advisory": "Localstack is vulnerable to Denial of Service via regular expressions (ReDoS). After disclosure, vendor said that these threats \"are not considered a key concern since LocalStack is executed on a local machine\". There's no information about patches for these vulnerabilities.\r\nhttps://blog.sonarsource.com/hack-the-stack-with-localstack", + "cve": "PVE-2021-42837", + "id": "pyup.io-42837", + "more_info_path": "/vulnerabilities/PVE-2021-42837/42837", "specs": [ ">0" ], "v": ">0" }, { - "advisory": "Localstack is vulnerable to Denial of Service via regular expressions (ReDoS). After disclosure, vendor said that these threats \"are not considered a key concern since LocalStack is executed on a local machine\". There's no information about patches for these vulnerabilities.\r\nhttps://blog.sonarsource.com/hack-the-stack-with-localstack", - "cve": "PVE-2021-42837", - "id": "pyup.io-42837", - "more_info_path": "/vulnerabilities/PVE-2021-42837/42837", + "advisory": "Localstack is vulnerable to CVE-2021-32091: A Cross-site scripting (XSS) vulnerability exists in StackLift LocalStack 0.12.6. After disclosure, vendor said that these threats \"are not considered a key concern since LocalStack is executed on a local machine\". There's no information about patches for these vulnerabilities.\r\nhttps://blog.sonarsource.com/hack-the-stack-with-localstack", + "cve": "CVE-2021-32091", + "id": "pyup.io-42836", + "more_info_path": "/vulnerabilities/CVE-2021-32091/42836", "specs": [ ">0" ], @@ -81837,9 +82266,9 @@ "logbesselk": [ { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41196", - "id": "pyup.io-51580", - "more_info_path": "/vulnerabilities/CVE-2021-41196/51580", + "cve": "CVE-2021-22922", + "id": "pyup.io-51574", + "more_info_path": "/vulnerabilities/CVE-2021-22922/51574", "specs": [ "<0.8.5" ], @@ -81847,9 +82276,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41198", - "id": "pyup.io-51582", - "more_info_path": "/vulnerabilities/CVE-2021-41198/51582", + "cve": "CVE-2021-41219", + "id": "pyup.io-51603", + "more_info_path": "/vulnerabilities/CVE-2021-41219/51603", "specs": [ "<0.8.5" ], @@ -81857,9 +82286,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41209", - "id": "pyup.io-51593", - "more_info_path": "/vulnerabilities/CVE-2021-41209/51593", + "cve": "CVE-2021-41227", + "id": "pyup.io-51611", + "more_info_path": "/vulnerabilities/CVE-2021-41227/51611", "specs": [ "<0.8.5" ], @@ -81867,9 +82296,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41212", - "id": "pyup.io-51596", - "more_info_path": "/vulnerabilities/CVE-2021-41212/51596", + "cve": "CVE-2021-41221", + "id": "pyup.io-51605", + "more_info_path": "/vulnerabilities/CVE-2021-41221/51605", "specs": [ "<0.8.5" ], @@ -81877,9 +82306,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41215", - "id": "pyup.io-51599", - "more_info_path": "/vulnerabilities/CVE-2021-41215/51599", + "cve": "CVE-2021-41201", + "id": "pyup.io-51585", + "more_info_path": "/vulnerabilities/CVE-2021-41201/51585", "specs": [ "<0.8.5" ], @@ -81887,9 +82316,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41216", - "id": "pyup.io-51600", - "more_info_path": "/vulnerabilities/CVE-2021-41216/51600", + "cve": "CVE-2021-41209", + "id": "pyup.io-51593", + "more_info_path": "/vulnerabilities/CVE-2021-41209/51593", "specs": [ "<0.8.5" ], @@ -81897,9 +82326,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41218", - "id": "pyup.io-51602", - "more_info_path": "/vulnerabilities/CVE-2021-41218/51602", + "cve": "CVE-2021-41215", + "id": "pyup.io-51599", + "more_info_path": "/vulnerabilities/CVE-2021-41215/51599", "specs": [ "<0.8.5" ], @@ -81907,9 +82336,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41220", - "id": "pyup.io-51604", - "more_info_path": "/vulnerabilities/CVE-2021-41220/51604", + "cve": "CVE-2021-41217", + "id": "pyup.io-51601", + "more_info_path": "/vulnerabilities/CVE-2021-41217/51601", "specs": [ "<0.8.5" ], @@ -81917,9 +82346,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41224", - "id": "pyup.io-51608", - "more_info_path": "/vulnerabilities/CVE-2021-41224/51608", + "cve": "CVE-2021-41214", + "id": "pyup.io-51598", + "more_info_path": "/vulnerabilities/CVE-2021-41214/51598", "specs": [ "<0.8.5" ], @@ -81927,9 +82356,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41197", - "id": "pyup.io-51581", - "more_info_path": "/vulnerabilities/CVE-2021-41197/51581", + "cve": "CVE-2021-41211", + "id": "pyup.io-51595", + "more_info_path": "/vulnerabilities/CVE-2021-41211/51595", "specs": [ "<0.8.5" ], @@ -81937,9 +82366,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41214", - "id": "pyup.io-51598", - "more_info_path": "/vulnerabilities/CVE-2021-41214/51598", + "cve": "CVE-2021-22926", + "id": "pyup.io-51578", + "more_info_path": "/vulnerabilities/CVE-2021-22926/51578", "specs": [ "<0.8.5" ], @@ -81947,9 +82376,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41217", - "id": "pyup.io-51601", - "more_info_path": "/vulnerabilities/CVE-2021-41217/51601", + "cve": "CVE-2021-41205", + "id": "pyup.io-51589", + "more_info_path": "/vulnerabilities/CVE-2021-41205/51589", "specs": [ "<0.8.5" ], @@ -81957,9 +82386,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-22922", - "id": "pyup.io-51574", - "more_info_path": "/vulnerabilities/CVE-2021-22922/51574", + "cve": "CVE-2021-41220", + "id": "pyup.io-51604", + "more_info_path": "/vulnerabilities/CVE-2021-41220/51604", "specs": [ "<0.8.5" ], @@ -81967,9 +82396,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41206", - "id": "pyup.io-51590", - "more_info_path": "/vulnerabilities/CVE-2021-41206/51590", + "cve": "CVE-2021-41224", + "id": "pyup.io-51608", + "more_info_path": "/vulnerabilities/CVE-2021-41224/51608", "specs": [ "<0.8.5" ], @@ -81977,9 +82406,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41221", - "id": "pyup.io-51605", - "more_info_path": "/vulnerabilities/CVE-2021-41221/51605", + "cve": "CVE-2021-41197", + "id": "pyup.io-51581", + "more_info_path": "/vulnerabilities/CVE-2021-41197/51581", "specs": [ "<0.8.5" ], @@ -81987,9 +82416,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41227", - "id": "pyup.io-51611", - "more_info_path": "/vulnerabilities/CVE-2021-41227/51611", + "cve": "CVE-2021-41228", + "id": "pyup.io-51612", + "more_info_path": "/vulnerabilities/CVE-2021-41228/51612", "specs": [ "<0.8.5" ], @@ -81997,9 +82426,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41228", - "id": "pyup.io-51612", - "more_info_path": "/vulnerabilities/CVE-2021-41228/51612", + "cve": "CVE-2021-41222", + "id": "pyup.io-51606", + "more_info_path": "/vulnerabilities/CVE-2021-41222/51606", "specs": [ "<0.8.5" ], @@ -82007,9 +82436,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-22925", - "id": "pyup.io-51577", - "more_info_path": "/vulnerabilities/CVE-2021-22925/51577", + "cve": "CVE-2021-41200", + "id": "pyup.io-51584", + "more_info_path": "/vulnerabilities/CVE-2021-41200/51584", "specs": [ "<0.8.5" ], @@ -82017,9 +82446,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41204", - "id": "pyup.io-51588", - "more_info_path": "/vulnerabilities/CVE-2021-41204/51588", + "cve": "CVE-2021-41208", + "id": "pyup.io-51592", + "more_info_path": "/vulnerabilities/CVE-2021-41208/51592", "specs": [ "<0.8.5" ], @@ -82027,9 +82456,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-22926", - "id": "pyup.io-51578", - "more_info_path": "/vulnerabilities/CVE-2021-22926/51578", + "cve": "CVE-2021-22924", + "id": "pyup.io-51576", + "more_info_path": "/vulnerabilities/CVE-2021-22924/51576", "specs": [ "<0.8.5" ], @@ -82037,9 +82466,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41201", - "id": "pyup.io-51585", - "more_info_path": "/vulnerabilities/CVE-2021-41201/51585", + "cve": "CVE-2020-10531", + "id": "pyup.io-51613", + "more_info_path": "/vulnerabilities/CVE-2020-10531/51613", "specs": [ "<0.8.5" ], @@ -82047,9 +82476,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41203", - "id": "pyup.io-51587", - "more_info_path": "/vulnerabilities/CVE-2021-41203/51587", + "cve": "CVE-2021-41212", + "id": "pyup.io-51596", + "more_info_path": "/vulnerabilities/CVE-2021-41212/51596", "specs": [ "<0.8.5" ], @@ -82057,9 +82486,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41207", - "id": "pyup.io-51591", - "more_info_path": "/vulnerabilities/CVE-2021-41207/51591", + "cve": "CVE-2021-41216", + "id": "pyup.io-51600", + "more_info_path": "/vulnerabilities/CVE-2021-41216/51600", "specs": [ "<0.8.5" ], @@ -82067,9 +82496,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41219", - "id": "pyup.io-51603", - "more_info_path": "/vulnerabilities/CVE-2021-41219/51603", + "cve": "CVE-2021-41218", + "id": "pyup.io-51602", + "more_info_path": "/vulnerabilities/CVE-2021-41218/51602", "specs": [ "<0.8.5" ], @@ -82077,9 +82506,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41208", - "id": "pyup.io-51592", - "more_info_path": "/vulnerabilities/CVE-2021-41208/51592", + "cve": "CVE-2021-22923", + "id": "pyup.io-51575", + "more_info_path": "/vulnerabilities/CVE-2021-22923/51575", "specs": [ "<0.8.5" ], @@ -82087,9 +82516,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41200", - "id": "pyup.io-51584", - "more_info_path": "/vulnerabilities/CVE-2021-41200/51584", + "cve": "CVE-2021-41198", + "id": "pyup.io-51582", + "more_info_path": "/vulnerabilities/CVE-2021-41198/51582", "specs": [ "<0.8.5" ], @@ -82097,9 +82526,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41211", - "id": "pyup.io-51595", - "more_info_path": "/vulnerabilities/CVE-2021-41211/51595", + "cve": "CVE-2021-41225", + "id": "pyup.io-51609", + "more_info_path": "/vulnerabilities/CVE-2021-41225/51609", "specs": [ "<0.8.5" ], @@ -82107,9 +82536,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-22924", - "id": "pyup.io-51576", - "more_info_path": "/vulnerabilities/CVE-2021-22924/51576", + "cve": "CVE-2021-41226", + "id": "pyup.io-51610", + "more_info_path": "/vulnerabilities/CVE-2021-41226/51610", "specs": [ "<0.8.5" ], @@ -82117,19 +82546,19 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2020-10531", - "id": "pyup.io-51613", - "more_info_path": "/vulnerabilities/CVE-2020-10531/51613", + "cve": "CVE-2021-41210", + "id": "pyup.io-51594", + "more_info_path": "/vulnerabilities/CVE-2021-41210/51594", "specs": [ "<0.8.5" ], "v": "<0.8.5" }, { - "advisory": "Logbesselk 0.8.5 updates its dependency 'numpy' to v1.21.5 to include a security fix.", - "cve": "CVE-2021-33430", - "id": "pyup.io-51528", - "more_info_path": "/vulnerabilities/CVE-2021-33430/51528", + "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", + "cve": "CVE-2021-41195", + "id": "pyup.io-51579", + "more_info_path": "/vulnerabilities/CVE-2021-41195/51579", "specs": [ "<0.8.5" ], @@ -82137,9 +82566,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-22923", - "id": "pyup.io-51575", - "more_info_path": "/vulnerabilities/CVE-2021-22923/51575", + "cve": "CVE-2021-41203", + "id": "pyup.io-51587", + "more_info_path": "/vulnerabilities/CVE-2021-41203/51587", "specs": [ "<0.8.5" ], @@ -82157,9 +82586,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41199", - "id": "pyup.io-51583", - "more_info_path": "/vulnerabilities/CVE-2021-41199/51583", + "cve": "CVE-2021-41196", + "id": "pyup.io-51580", + "more_info_path": "/vulnerabilities/CVE-2021-41196/51580", "specs": [ "<0.8.5" ], @@ -82167,9 +82596,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41225", - "id": "pyup.io-51609", - "more_info_path": "/vulnerabilities/CVE-2021-41225/51609", + "cve": "CVE-2021-41223", + "id": "pyup.io-51607", + "more_info_path": "/vulnerabilities/CVE-2021-41223/51607", "specs": [ "<0.8.5" ], @@ -82177,19 +82606,19 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41226", - "id": "pyup.io-51610", - "more_info_path": "/vulnerabilities/CVE-2021-41226/51610", + "cve": "CVE-2021-41213", + "id": "pyup.io-51597", + "more_info_path": "/vulnerabilities/CVE-2021-41213/51597", "specs": [ "<0.8.5" ], "v": "<0.8.5" }, { - "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41222", - "id": "pyup.io-51606", - "more_info_path": "/vulnerabilities/CVE-2021-41222/51606", + "advisory": "Logbesselk 0.8.5 updates its dependency 'numpy' to v1.21.5 to include a security fix.", + "cve": "CVE-2021-33430", + "id": "pyup.io-51528", + "more_info_path": "/vulnerabilities/CVE-2021-33430/51528", "specs": [ "<0.8.5" ], @@ -82197,9 +82626,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41213", - "id": "pyup.io-51597", - "more_info_path": "/vulnerabilities/CVE-2021-41213/51597", + "cve": "CVE-2021-41207", + "id": "pyup.io-51591", + "more_info_path": "/vulnerabilities/CVE-2021-41207/51591", "specs": [ "<0.8.5" ], @@ -82207,9 +82636,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41210", - "id": "pyup.io-51594", - "more_info_path": "/vulnerabilities/CVE-2021-41210/51594", + "cve": "CVE-2021-41199", + "id": "pyup.io-51583", + "more_info_path": "/vulnerabilities/CVE-2021-41199/51583", "specs": [ "<0.8.5" ], @@ -82217,9 +82646,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41205", - "id": "pyup.io-51589", - "more_info_path": "/vulnerabilities/CVE-2021-41205/51589", + "cve": "CVE-2021-41206", + "id": "pyup.io-51590", + "more_info_path": "/vulnerabilities/CVE-2021-41206/51590", "specs": [ "<0.8.5" ], @@ -82227,9 +82656,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41195", - "id": "pyup.io-51579", - "more_info_path": "/vulnerabilities/CVE-2021-41195/51579", + "cve": "CVE-2021-22925", + "id": "pyup.io-51577", + "more_info_path": "/vulnerabilities/CVE-2021-22925/51577", "specs": [ "<0.8.5" ], @@ -82237,9 +82666,9 @@ }, { "advisory": "Logbesselk 0.8.5 updates its dependency 'tensorflow' to v2.8.0 to include security fixes.", - "cve": "CVE-2021-41223", - "id": "pyup.io-51607", - "more_info_path": "/vulnerabilities/CVE-2021-41223/51607", + "cve": "CVE-2021-41204", + "id": "pyup.io-51588", + "more_info_path": "/vulnerabilities/CVE-2021-41204/51588", "specs": [ "<0.8.5" ], @@ -82282,10 +82711,10 @@ ], "logprep": [ { - "advisory": "Logprep 7.0.0 updates its dependency 'aiohttp' to include a security fix.", - "cve": "CVE-2023-37276", - "id": "pyup.io-61805", - "more_info_path": "/vulnerabilities/CVE-2023-37276/61805", + "advisory": "Logprep 7.0.0 updates its dependency 'urllib3' to include a security fix.", + "cve": "CVE-2023-43804", + "id": "pyup.io-61804", + "more_info_path": "/vulnerabilities/CVE-2023-43804/61804", "specs": [ "<7.0.0" ], @@ -82302,10 +82731,10 @@ "v": "<7.0.0" }, { - "advisory": "Logprep 7.0.0 updates its dependency 'urllib3' to include a security fix.", - "cve": "CVE-2023-43804", - "id": "pyup.io-61804", - "more_info_path": "/vulnerabilities/CVE-2023-43804/61804", + "advisory": "Logprep 7.0.0 updates its dependency 'aiohttp' to include a security fix.", + "cve": "CVE-2023-37276", + "id": "pyup.io-61805", + "more_info_path": "/vulnerabilities/CVE-2023-37276/61805", "specs": [ "<7.0.0" ], @@ -82576,6 +83005,18 @@ "v": "<4.9.1" } ], + "lxml-html-clean": [ + { + "advisory": "Affected versions of lxml_html_clean are vulnerable to Cross-Site Scripting (CWE-79). This vulnerability allows attackers to inject malicious scripts within CSS comments in special HTML tags like , , and