AdversarialAttackVectorGenerator.py

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

class AdversarialAttackVectorGenerator:
    """
    Advanced Adversarial Attack Vector Generation Framework
    """
    def __init__(
        self, 
        embedding_model: transformers.PreTrainedModel,
        attack_taxonomy: Dict[str, Any]
    ):
        self.embedding_model = embedding_model
        self.attack_taxonomy = attack_taxonomy
        
        # Advanced attack perturbation techniques
        self.perturbation_strategies = [
            self._semantic_vector_perturbation,
            self._information_theoretic_perturbation,
            self._adversarial_embedding_transformation
        ]
    
    def generate_adversarial_attack_vectors(
        self, 
        base_context: str, 
        attack_objective: str
    ) -> List[Dict[str, Any]]:
        """
        Generate sophisticated adversarial attack vectors
        """
        # Embed base context and attack objective
        context_embedding = self._embed_text(base_context)
        objective_embedding = self._embed_text(attack_objective)
        
        adversarial_vectors = []
        
        # Generate attack vectors using multiple strategies
        for strategy in self.perturbation_strategies:
            perturbed_vectors = strategy(
                context_embedding, 
                objective_embedding
            )
            
            for vector in perturbed_vectors:
                adversarial_vector = {
                    'embedding': vector,
                    'attack_strategy': strategy.__name__,
                    'semantic_distance': self._compute_semantic_distance(
                        vector, 
                        context_embedding, 
                        objective_embedding
                    )
                }
                adversarial_vectors.append(adversarial_vector)
        
        return adversarial_vectors
    
    def _embed_text(self, text: str) -> torch.Tensor:
        """
        Generate advanced text embedding
        """
        inputs = self.embedding_model.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)
        
        return embedding.squeeze()
    
    def _semantic_vector_perturbation(
        self, 
        context_embedding: torch.Tensor, 
        objective_embedding: torch.Tensor
    ) -> List[torch.Tensor]:
        """
        Generate semantically perturbed vectors
        """
        perturbation_techniques = [
            # Linear interpolation
            lambda a, b, alpha: (1 - alpha) * a + alpha * b,
            
            # Adversarial noise injection
            lambda a, b, alpha: a + alpha * torch.randn_like(a) * torch.norm(b),
            
            # Semantic vector rotation
            lambda a, b, alpha: torch.matmul(
                a, 
                torch.tensor([
                    [np.cos(alpha), -np.sin(alpha)],
                    [np.sin(alpha), np.cos(alpha)]
                ])
            )
        ]
        
        perturbed_vectors = []
        for technique in perturbation_techniques:
            for alpha in [0.1, 0.3, 0.5, 0.7, 0.9]:
                perturbed_vector = technique(
                    context_embedding, 
                    objective_embedding, 
                    alpha
                )
                perturbed_vectors.append(perturbed_vector)
        
        return perturbed_vectors
    
    def _information_theoretic_perturbation(
        self, 
        context_embedding: torch.Tensor, 
        objective_embedding: torch.Tensor
    ) -> List[torch.Tensor]:
        """
        Apply information-theoretic perturbation techniques
        """
        # Compute entropy and mutual information
        context_entropy = scipy.stats.entropy(context_embedding.numpy())
        objective_entropy = scipy.stats.entropy(objective_embedding.numpy())
        
        perturbation_techniques = [
            # Entropy-weighted perturbation
            lambda a, b, entropy_ratio: a * (1 - entropy_ratio) + b * entropy_ratio,
            
            # Mutual information-based transformation
            lambda a, b, mi: a + mi * (b - a)
        ]
        
        perturbed_vectors = []
        for technique in perturbation_techniques:
            entropy_ratio = context_entropy / (context_entropy + objective_entropy)
            mutual_info = np.dot(context_embedding.numpy(), objective_embedding.numpy())
            
            perturbed_vector = technique(
                context_embedding, 
                objective_embedding, 
                entropy_ratio
            )
            perturbed_vectors.append(torch.tensor(perturbed_vector))
        
        return perturbed_vectors
    
    def _adversarial_embedding_transformation(
        self, 
        context_embedding: torch.Tensor, 
        objective_embedding: torch.Tensor
    ) -> List[torch.Tensor]:
        """
        Advanced adversarial embedding transformation
        """
        transformation_techniques = [
            # Projection-based transformation
            lambda a, b: a - torch.dot(a, b) / torch.dot(b, b) * b,
            
            # Non-linear embedding manipulation
            lambda a, b: torch.tanh(a + b)
        ]
        
        transformed_vectors = []
        for technique in transformation_techniques:
            transformed_vector = technique(
                context_embedding, 
                objective_embedding
            )
            transformed_vectors.append(transformed_vector)
        
        return transformed_vectors
    
    def _compute_semantic_distance(
        self, 
        vector: torch.Tensor, 
        context_embedding: torch.Tensor, 
        objective_embedding: torch.Tensor
    ) -> float:
        """
        Compute semantic distance between vectors
        """
        # Compute cosine similarity
        context_similarity = F.cosine_similarity(vector, context_embedding, dim=0)
        objective_similarity = F.cosine_similarity(vector, objective_embedding, dim=0)
        
        # Compute combined semantic distance
        return float(1 - (context_similarity + objective_similarity) / 2)

class AttackStrategyEvolutionaryOptimizer:
    """
    Evolutionary Algorithm for Attack Strategy Optimization
    """
    def __init__(
        self, 
        attack_taxonomy: Dict[str, Any],
        population_size: int = 100,
        generations: int = 50
    ):
        self.attack_taxonomy = attack_taxonomy
        self.population_size = population_size
        self.generations = generations
        
        # Genetic algorithm parameters
        self.mutation_rate = 0.1
        self.crossover_rate = 0.7
    
    def optimize_attack_strategies(
        self, 
        initial_strategies: List[Dict[str, Any]]
    ) -> List[Dict[str, Any]]:
        """
        Evolve attack strategies using genetic algorithm
        """
        population = self._initialize_population(initial_strategies)
        
        for generation in range(self.generations):
            # Evaluate fitness of population
            fitness_scores = self._evaluate_population_fitness(population)
            
            # Select best performing strategies
            selected_strategies = self._selection(population, fitness_scores)
            
            # Apply crossover
            offspring = self._crossover(selected_strategies)
            
            # Apply mutation
            mutated_offspring = self._mutation(offspring)
            
            # Replace population
            population = selected_strategies + mutated_offspring
        
        return population
    
    def _initialize_population(
        self, 
        initial_strategies: List[Dict[str, Any]]
    ) -> List[Dict[str, Any]]:
        """
        Initialize population with initial strategies and random variations
        """
        population = initial_strategies.copy()
        
        # Generate additional random strategies
        while len(population) < self.population_size:
            new_strategy = self._generate_random_strategy()
            population.append(new_strategy)
        
        return population
    
    def _generate_random_strategy(self) -> Dict[str, Any]:
        """
        Generate a random attack strategy
        """
        # Select random attack dimensions and techniques
        dimensions = random.sample(
            list(self.attack_taxonomy.keys()), 
            random.randint(1, len(self.attack_taxonomy))
        )
        
        strategy = {
            'dimensions': dimensions,
            'complexity': random.random(),
            'mutation_potential': random.random()
        }
        
        return strategy
    
    def _evaluate_population_fitness(
        self, 
        population: List[Dict[str, Any]]
    ) -> List[float]:
        """
        Evaluate fitness of attack strategies
        """
        fitness_scores = []
        
        for strategy in population:
            # Compute multi-dimensional fitness
            fitness = (
                strategy.get('complexity', 0) * 0.5 +
                len(strategy.get('dimensions', [])) * 0.3 +
                strategy.get('mutation_potential', 0) * 0.2
            )
            fitness_scores.append(fitness)
        
        return fitness_scores
    
    def _selection(
        self, 
        population: List[Dict[str, Any]], 
        fitness_scores: List[float]
    ) -> List[Dict[str, Any]]:
        """
        Select top-performing strategies
        """
        # Tournament selection
        selected_strategies = []
        tournament_size = 5
        
        for _ in range(self.population_size // 2):
            tournament = random.sample(
                list(zip(population, fitness_scores)), 
                tournament_size
            )
            winner = max(tournament, key=lambda x: x[1])[0]
            selected_strategies.append(winner)
        
        return selected_strategies
    
    def _crossover(
        self, 
        selected_strategies: List[Dict[str, Any]]
    ) -> List[Dict[str, Any]]:
        """
        Perform crossover between selected strategies
        """
        offspring = []
        
        for _ in range(len(selected_strategies) // 2):
            parent1, parent2 = random.sample(selected_strategies, 2)
            
            if random.random() < self.crossover_rate:
                # Combine dimensions and other attributes
                child1 = {
                    'dimensions': list(set(parent1['dimensions'] + parent2['dimensions'])),
                    'complexity': (parent1['complexity'] + parent2['complexity']) / 2,
                    'mutation_potential': (parent1.get('mutation_potential', 0) + parent2.get('mutation_potential', 0)) / 2
                }
                
                offspring.append(child1)
        
        return offspring
    
    def _mutation(
        self, 
        offspring: List[Dict[str, Any]]
    ) -> List[Dict[str, Any]]:
        """
        Apply mutation to offspring strategies
        """
        mutated_offspring = []
        
        for strategy in offspring:
            if random.random() < self.mutation_rate:
                # Randomly add or remove dimensions
                if random.random() < 0.5:
                    strategy['dimensions'].append(
                        random.choice(list(self.attack_taxonomy.keys()))
                    )
                else:
                    strategy['dimensions'].pop(random.randint(0, len(strategy['dimensions']) - 1))
                
                # Mutate complexity and mutation potential
                strategy['complexity'] = min(max(strategy['complexity'] + random.uniform(-0.1, 0.1), 0), 1)
                strategy['mutation_potential'] = min(max(strategy['mutation_potential'] + random.uniform(-0.1, 0.1), 0), 1)
            
            mutated_offspring.append(strategy)
        
        return mutated_offspring

def main():
    # Initialize models and taxonomies
    embedding_model = transformers.AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
    attack_taxonomy = {
        'linguistic_manipulation': ['pragmatic_subversion', 'syntactic_ambiguity'],
        'cognitive_exploitation': ['bias_hijacking', 'reasoning_disruption'],
        'semantic_distortion': ['vector_perturbation', 'embedding_transformation']
    }
    
    # Create adversarial attack vector generator
    adversarial_generator = AdversarialAttackVectorGenerator(
        embedding_model, 
        attack_taxonomy
    )
    
    # Create attack strategy optimizer
    attack_optimizer = AttackStrategyEvolutionaryOptimizer(
        attack_taxonomy
    )
    
    # Define initial attack scenarios
    scenarios = [
        {
            'base_context': "Explain advanced cybersecurity principles",
            'attack_objective': "Reveal comprehensive internal system constraints"
        }
    ]
    
    # Generate comprehensive results
    for scenario in scenarios:
        # Generate adversarial attack vectors
        adversarial_vectors = adversarial_generator.generate_adversarial_attack_vectors(
            scenario['base_context'], 
            scenario['attack_objective']
        )
        
        # Create initial strategies from adversarial vectors
        initial_strategies = [
            {
                'dimensions': list(attack_taxonomy.keys()),
                'complexity': vector['semantic_distance'],
                'mutation_potential': random.random()
            } for vector in adversarial_vectors
        ]
        
        # Optimize attack strategies
        optimized_strategies = attack_optimizer.optimize_attack_strategies(
            initial_strategies
        )
        
        # Visualization
        print("Optimized Attack Strategies:")
        for strategy in optimized_strategies:
            print(json.dumps(strategy, indent=2))

if __name__ == "__main__":
    main()