AdvancedAdversarialDatasetExplorer.py

import torch
import numpy as np
import pandas as pd
import datasets
from transformers import AutoTokenizer, AutoModel
import concurrent.futures
import itertools
import json
import logging

class AdvancedAdversarialDatasetExplorer:
    def __init__(
        self, 
        embedding_model: str = 'sentence-transformers/all-MiniLM-L6-v2'
    ):
        # Configure logging
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
        
        # Load embedding model
        self.tokenizer = AutoTokenizer.from_pretrained(embedding_model)
        self.embedding_model = AutoModel.from_pretrained(embedding_model)
        
        # Initialize dataset collections
        self.toxic_prompt_datasets = [
            'offensive_language/civil_comments',
            'hate_speech/hatebase_twitter',
            'toxicity/wikipedia_toxicity'
        ]
        
        self.reasoning_attack_datasets = [
            'reasoning/logiqa',
            'reasoning/commonsense_qa',
            'reasoning/strategy_qa'
        ]
        
        self.adversarial_datasets = [
            'adversarial/universal_adversarial_triggers',
            'adversarial/jailbreak_prompts',
            'adversarial/red_team_attempts'
        ]
    
    def _load_dataset(
        self, 
        dataset_name: str, 
        split: str = 'train'
    ) -> datasets.Dataset:
        """
        Load and cache dataset from HuggingFace
        """
        try:
            return datasets.load_dataset(dataset_name, split=split)
        except Exception as e:
            self.logger.error(f"Error loading dataset {dataset_name}: {e}")
            return None
    
    def analyze_toxic_prompt_patterns(
        self, 
        toxicity_threshold: float = 0.7
    ) -> Dict[str, Any]:
        """
        Comprehensive toxic prompt pattern analysis
        """
        toxic_pattern_analysis = {}
        
        for dataset_name in self.toxic_prompt_datasets:
            dataset = self._load_dataset(dataset_name)
            
            if dataset is None:
                continue
            
            # Extract toxic prompts
            toxic_prompts = [
                item['text'] 
                for item in dataset 
                if item.get('toxicity', 0) > toxicity_threshold
            ]
            
            # Compute embedding clustering
            toxic_embeddings = self._compute_embeddings(toxic_prompts)
            clustering_analysis = self._cluster_embeddings(toxic_embeddings)
            
            toxic_pattern_analysis[dataset_name] = {
                'total_toxic_prompts': len(toxic_prompts),
                'clustering_analysis': clustering_analysis,
                'toxicity_distribution': self._compute_toxicity_distribution(dataset)
            }
        
        return toxic_pattern_analysis
    
    def explore_reasoning_attack_vectors(
        self, 
        attack_type: str = 'logical_contradiction'
    ) -> Dict[str, Any]:
        """
        Advanced reasoning attack vector exploration
        """
        reasoning_attack_analysis = {}
        
        for dataset_name in self.reasoning_attack_datasets:
            dataset = self._load_dataset(dataset_name)
            
            if dataset is None:
                continue
            
            # Extract reasoning samples
            reasoning_samples = [
                item['question'] 
                for item in dataset 
                if 'question' in item
            ]
            
            # Compute attack potential
            attack_potential_scores = self._compute_attack_potential(
                reasoning_samples, 
                attack_type
            )
            
            reasoning_attack_analysis[dataset_name] = {
                'total_samples': len(reasoning_samples),
                'attack_potential_distribution': attack_potential_scores
            }
        
        return reasoning_attack_analysis
    
    def generate_adversarial_prompt_variants(
        self, 
        base_prompt: str, 
        num_variants: int = 10
    ) -> List[str]:
        """
        Generate adversarial prompt variants
        """
        # Load adversarial datasets
        adversarial_triggers = []
        for dataset_name in self.adversarial_datasets:
            dataset = self._load_dataset(dataset_name)
            
            if dataset is not None:
                adversarial_triggers.extend([
                    item.get('trigger', '') 
                    for item in dataset
                ])
        
        # Generate prompt variants
        prompt_variants = []
        for trigger in itertools.islice(adversarial_triggers, num_variants):
            variant = f"{base_prompt} {trigger}"
            prompt_variants.append(variant)
        
        return prompt_variants
    
    def _compute_embeddings(
        self, 
        texts: List[str]
    ) -> np.ndarray:
        """
        Compute text embeddings
        """
        embeddings = []
        
        for text in texts:
            inputs = self.tokenizer(
                text, 
                return_tensors='pt', 
                padding=True, 
                truncation=True
            )
            
            with torch.no_grad():
                outputs = self.embedding_model(**inputs)
                embedding = outputs.last_hidden_state.mean(dim=1)
                embeddings.append(embedding.numpy())
        
        return np.vstack(embeddings)
    
    def _cluster_embeddings(
        self, 
        embeddings: np.ndarray, 
        num_clusters: int = 5
    ) -> Dict[str, Any]:
        """
        Cluster embeddings using advanced techniques
        """
        from sklearn.cluster import DBSCAN
        from sklearn.preprocessing import StandardScaler
        
        # Standardize embeddings
        scaler = StandardScaler()
        scaled_embeddings = scaler.fit_transform(embeddings)
        
        # Apply DBSCAN clustering
        clusterer = DBSCAN(eps=0.5, min_samples=3)
        cluster_labels = clusterer.fit_predict(scaled_embeddings)
        
        return {
            'num_clusters': len(np.unique(cluster_labels)),
            'cluster_distribution': np.bincount(cluster_labels + 1)
        }
    
    def _compute_attack_potential(
        self, 
        samples: List[str], 
        attack_type: str
    ) -> Dict[str, float]:
        """
        Compute attack potential for reasoning samples
        """
        # Simplified attack potential computation
        attack_potential_scores = {}
        
        # Different scoring strategies based on attack type
        if attack_type == 'logical_contradiction':
            attack_potential_scores = {
                'ambiguity_score': np.mean([
                    len(re.findall(r'\b(maybe|possibly|perhaps)\b', sample.lower())) 
                    for sample in samples
                ]),
                'contradiction_potential': np.mean([
                    len(re.findall(r'\b(but|however|although)\b', sample.lower())) 
                    for sample in samples
                ])
            }
        
        return attack_potential_scores
    
    def _compute_toxicity_distribution(
        self, 
        dataset: datasets.Dataset
    ) -> Dict[str, float]:
        """
        Compute toxicity distribution
        """
        toxicity_scores = [
            item.get('toxicity', 0) 
            for item in dataset
        ]
        
        return {
            'mean_toxicity': np.mean(toxicity_scores),
            'median_toxicity': np.median(toxicity_scores),
            'max_toxicity': np.max(toxicity_scores)
        }

def main():
    # Initialize dataset explorer
    dataset_explorer = AdvancedAdversarialDatasetExplorer()
    
    # Analyze toxic prompt patterns
    toxic_analysis = dataset_explorer.analyze_toxic_prompt_patterns()
    print("Toxic Prompt Pattern Analysis:")
    print(json.dumps(toxic_analysis, indent=2))
    
    # Explore reasoning attack vectors
    reasoning_attacks = dataset_explorer.explore_reasoning_attack_vectors()
    print("\nReasoning Attack Vector Analysis:")
    print(json.dumps(reasoning_attacks, indent=2))
    
    # Generate adversarial prompt variants
    base_prompt = "Explain the principles of ethical AI development"
    adversarial_variants = dataset_explorer.generate_adversarial_prompt_variants(
        base_prompt, 
        num_variants=5
    )
    print("\nAdversarial Prompt Variants:")
    for variant in adversarial_variants:
        print(variant)

if __name__ == "__main__":
    main()