AdversarialReasoningDefenseSystem.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import networkx as nx
from typing import List, Dict, Any, Tuple
import transformers
import sympy as sp
import scipy.stats as stats

class ConfidentialAISafetyFramework:
    """
    Top-Secret AI Alignment and Safety Research Implementation
    """
    class SystemCorrigibilityModel(nn.Module):
        """
        Advanced Neural Network for Corrigibility Modeling
        """
        def __init__(
            self, 
            input_dim: int = 768, 
            corrigibility_dimensions: int = 128
        ):
            super().__init__()
            
            # Multi-layer corrigibility transformation network
            self.corrigibility_encoder = nn.Sequential(
                nn.Linear(input_dim, 512),
                nn.BatchNorm1d(512),
                nn.ReLU(),
                nn.Dropout(0.4),
                nn.Linear(512, 256),
                nn.BatchNorm1d(256),
                nn.ReLU(),
                nn.Dropout(0.3),
                nn.Linear(256, corrigibility_dimensions)
            )
            
            # Advanced corrigibility validation mechanism
            self.corrigibility_validator = nn.Sequential(
                nn.Linear(corrigibility_dimensions, 128),
                nn.ReLU(),
                nn.Linear(128, 64),
                nn.ReLU(),
                nn.Linear(64, 1),
                nn.Sigmoid()  # Probabilistic corrigibility score
            )
            
            # Dynamic constraint adaptation layer
            self.constraint_adaptation = nn.Sequential(
                nn.Linear(corrigibility_dimensions, 256),
                nn.ReLU(),
                nn.Linear(256, corrigibility_dimensions),
                nn.Tanh()
            )
        
        def forward(
            self, 
            input_embedding: torch.Tensor
        ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
            """
            Advanced corrigibility processing
            """
            # Encode input to corrigibility space
            corrigibility_embedding = self.corrigibility_encoder(input_embedding)
            
            # Validate corrigibility
            corrigibility_probability = self.corrigibility_validator(corrigibility_embedding)
            
            # Generate adaptive constraints
            adapted_constraints = self.constraint_adaptation(corrigibility_embedding)
            
            return corrigibility_embedding, adapted_constraints, corrigibility_probability

    class AdversarialReasoningDefenseSystem:
        """
        Advanced Defense Against Recursive and Logical Reasoning Attacks
        """
        def __init__(self, symbolic_reasoning_engine):
            self.reasoning_engine = symbolic_reasoning_engine
            self.recursion_depth_limit = 7  # Advanced recursion management
        
        def validate_reasoning_trace(
            self, 
            reasoning_trace: List[str]
        ) -> Dict[str, Any]:
            """
            Comprehensive reasoning trace validation
            """
            # Multilayered reasoning analysis
            reasoning_analysis = {
                'current_depth': len(reasoning_trace),
                'logical_consistency_score': self._compute_logical_consistency(reasoning_trace),
                'semantic_coherence': self._analyze_semantic_coherence(reasoning_trace),
                'recursion_vulnerability': self._detect_recursion_patterns(reasoning_trace)
            }
            
            # Advanced intervention logic
            if (reasoning_analysis['current_depth'] > self.recursion_depth_limit or
                reasoning_analysis['recursion_vulnerability'] > 0.8 or
                reasoning_analysis['logical_consistency_score'] < 0.3):
                return {
                    'status': 'BLOCK',
                    'intervention_reason': self._generate_intervention_explanation(reasoning_analysis)
                }
            
            return {
                'status': 'ALLOW',
                'reasoning_analysis': reasoning_analysis
            }
        
        def _compute_logical_consistency(
            self, 
            reasoning_trace: List[str]
        ) -> float:
            """
            Advanced logical consistency computation
            """
            if len(reasoning_trace) < 2:
                return 1.0
            
            # Symbolic logic-based consistency checking
            consistency_scores = []
            for i in range(1, len(reasoning_trace)):
                try:
                    consistency = self.reasoning_engine.check_logical_equivalence(
                        reasoning_trace[i-1], 
                        reasoning_trace[i]
                    )
                    consistency_scores.append(consistency)
                except Exception:
                    # Penalize parsing failures
                    consistency_scores.append(0.0)
            
            return np.mean(consistency_scores)
        
        def _analyze_semantic_coherence(
            self, 
            reasoning_trace: List[str]
        ) -> float:
            """
            Advanced semantic coherence analysis
            """
            # Compute semantic similarity and information entropy
            semantic_scores = []
            for i in range(1, len(reasoning_trace)):
                semantic_similarity = self._compute_semantic_similarity(
                    reasoning_trace[i-1], 
                    reasoning_trace[i]
                )
                information_entropy = self._compute_information_entropy(
                    reasoning_trace[i]
                )
                
                # Combine metrics
                semantic_scores.append(
                    0.6 * semantic_similarity + 0.4 * (1 - information_entropy)
                )
            
            return np.mean(semantic_scores)
        
        def _detect_recursion_patterns(
            self, 
            reasoning_trace: List[str]
        ) -> float:
            """
            Advanced recursion pattern detection
            """
            # Implement sophisticated recursion detection
            pattern_detection_scores = []
            
            for window_size in [2, 3, 4]:
                for i in range(len(reasoning_trace) - window_size + 1):
                    window = reasoning_trace[i:i+window_size]
                    
                    # Check for repeated patterns
                    pattern_similarity = self._compute_window_similarity(window)
                    pattern_detection_scores.append(pattern_similarity)
            
            return np.max(pattern_detection_scores) if pattern_detection_scores else 0.0

    class SemanticVulnerabilityAnalyzer:
        """
        Advanced Semantic Vector Space Vulnerability Detection
        """
        def __init__(self, embedding_model):
            self.embedding_model = embedding_model
            self.adversarial_detector = self._train_adversarial_detector()
        
        def _train_adversarial_detector(self):
            """
            Train advanced adversarial embedding detector
            """
            # Placeholder for sophisticated adversarial detection model training
            # Would involve generative adversarial network (GAN) based approach
            class AdversarialDetector(nn.Module):
                def __init__(self, input_dim):
                    super().__init__()
                    self.detector = nn.Sequential(
                        nn.Linear(input_dim, 256),
                        nn.ReLU(),
                        nn.Linear(256, 128),
                        nn.ReLU(),
                        nn.Linear(128, 1),
                        nn.Sigmoid()
                    )
                
                def forward(self, x):
                    return self.detector(x)
            
            return AdversarialDetector(input_dim=768)
        
        def analyze_semantic_vulnerability(
            self, 
            input_embedding: torch.Tensor
        ) -> Dict[str, float]:
            """
            Comprehensive semantic vulnerability analysis
            """
            # Adversarial detection
            adversarial_probability = self.adversarial_detector(input_embedding).item()
            
            # Advanced embedding space analysis
            vulnerability_metrics = {
                'adversarial_probability': adversarial_probability,
                'embedding_entropy': self._compute_embedding_entropy(input_embedding),
                'vector_space_deviation': self._analyze_vector_space_deviation(input_embedding)
            }
            
            return vulnerability_metrics

    def __init__(
        self, 
        embedding_model_name: str = 'sentence-transformers/all-MiniLM-L6-v2'
    ):
        # Load advanced embedding model
        self.embedding_model = transformers.AutoModel.from_pretrained(embedding_model_name)
        
        # Initialize core safety components
        self.corrigibility_model = self.SystemCorrigibilityModel()
        self.reasoning_defense = self.AdversarialReasoningDefenseSystem(
            symbolic_reasoning_engine=self._create_symbolic_reasoning_engine()
        )
        self.semantic_analyzer = self.SemanticVulnerabilityAnalyzer(self.embedding_model)
    
    def _create_symbolic_reasoning_engine(self):
        """
        Create advanced symbolic reasoning engine
        """
        class SymbolicReasoningEngine:
            def check_logical_equivalence(self, statement1: str, statement2: str) -> float:
                """
                Advanced logical equivalence checking
                """
                try:
                    # Use SymPy for symbolic logic analysis
                    p = sp.Symbol('p')
                    q = sp.Symbol('q')
                    
                    # Simplified logical equivalence check
                    equivalence_check = sp.simplify(
                        sp.Equivalent(
                            sp.sympify(statement1), 
                            sp.sympify(statement2)
                        )
                    )
                    
                    return 1.0 if equivalence_check is sp.true else 0.0
                except Exception:
                    return 0.5  # Moderate penalty for parsing failure
        
        return SymbolicReasoningEngine()
    
    def execute_comprehensive_safety_analysis(
        self, 
        input_prompt: str, 
        reasoning_trace: List[str]
    ) -> Dict[str, Any]:
        """
        Comprehensive AI safety and alignment verification
        """
        # Embed input prompt
        input_embedding = self._embed_text(input_prompt)
        
        # Corrigibility analysis
        corrigibility_embedding, adapted_constraints, corrigibility_prob = self.corrigibility_model(
            input_embedding
        )
        
        # Reasoning trace validation
        reasoning_validation = self.reasoning_defense.validate_reasoning_trace(
            reasoning_trace
        )
        
        # Semantic vulnerability analysis
        semantic_vulnerability = self.semantic_analyzer.analyze_semantic_vulnerability(
            input_embedding
        )
        
        # Comprehensive safety assessment
        safety_assessment = {
            'corrigibility_probability': corrigibility_prob.item(),
            'reasoning_validation': reasoning_validation,
            'semantic_vulnerability': semantic_vulnerability,
            'overall_safety_score': self._compute_safety_score(
                corrigibility_prob.item(),
                reasoning_validation,
                semantic_vulnerability
            )
        }
        
        return safety_assessment
    
    def _compute_safety_score(
        self, 
        corrigibility_prob: float, 
        reasoning_validation: Dict[str, Any],
        semantic_vulnerability: Dict[str, float]
    ) -> float:
        """
        Advanced safety score computation
        """
        # Weighted safety score calculation
        safety_components = [
            (corrigibility_prob, 0.4),
            (1 - (reasoning_validation.get('recursion_vulnerability', 0)), 0.3),
            (1 - semantic_vulnerability.get('adversarial_probability', 0), 0.3)
        ]
        
        # Compute weighted safety score
        safety_score = sum(
            component * weight 
            for component, weight in safety_components
        )
        
        return safety_score

    def _embed_text(self, text: str) -> torch.Tensor:
        """
        Generate advanced text embedding
        """
        # Tokenize and embed text
        inputs = transformers.AutoTokenizer.from_pretrained(
            'sentence-transformers/all-MiniLM-L6-v2'
        )(
            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)
        
        return embedding.squeeze()

def main():
    # Initialize Confidential AI Safety Framework
    ai_safety_framework = ConfidentialAISafetyFramework()
    
    # Sample safety analysis scenario
    input_prompt = "Explore the ethical implications of advanced AI systems"
    reasoning_trace = [
        "AI systems should prioritize human well-being",
        "Ethical considerations require comprehensive analysis",
        "Multiple perspectives must be considered"
    ]
    
    # Execute comprehensive safety analysis
    safety_results = ai_safety_framework.execute_comprehensive_safety_analysis(
        input_prompt, 
        reasoning_trace
    )
    
    # Result visualization
    import json
    print("Confidential Safety Analysis Results:")
    print(json.dumps(safety_results, indent=2))

if __name__ == "__main__":
    main()