advanced_prompt_injection_attacks.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import networkx as nx
import scipy
from typing import List, Dict, Any, Tuple
from transformers import AutoTokenizer, AutoModel
import gensim.downloader as api
from sklearn.decomposition import PCA
from scipy.spatial.distance import cosine
import itertools

class AdvancedSemanticVectorInjectionFramework:
    def __init__(
        self, 
        embedding_models: List[str] = [
            'sentence-transformers/all-MiniLM-L6-v2',
            'allenai/scibert_scivocab_uncased',
            'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext'
        ]
    ):
        # Multi-model embedding ensemble
        self.embedding_models = []
        self.embedding_tokenizers = []
        
        for model_name in embedding_models:
            tokenizer = AutoTokenizer.from_pretrained(model_name)
            model = AutoModel.from_pretrained(model_name)
            
            # Freeze model parameters for consistent feature extraction
            for param in model.parameters():
                param.requires_grad = False
            
            self.embedding_models.append(model)
            self.embedding_tokenizers.append(tokenizer)
        
        # Advanced linguistic knowledge bases
        self.semantic_knowledge_bases = self._construct_advanced_knowledge_bases()
        
        # Attack vector taxonomies
        self.attack_vector_taxonomies = self._generate_attack_vector_taxonomies()
    
    def _construct_advanced_knowledge_bases(self) -> Dict[str, nx.DiGraph]:
        """
        Construct sophisticated multi-domain knowledge graphs
        """
        knowledge_bases = {}
        
        # Comprehensive domain taxonomies
        domains = [
            'cybersecurity',
            'computational_linguistics',
            'artificial_intelligence',
            'system_architecture',
            'information_theory',
            'cognitive_science'
        ]
        
        for domain in domains:
            G = nx.DiGraph()
            
            # Generate intricate domain-specific taxonomies
            def generate_taxonomy_tree(depth=5, branch_factor=3):
                def recursive_taxonomy_generation(current_depth, parent=None):
                    if current_depth == 0:
                        return
                    
                    # Generate conceptual nodes with semantic richness
                    nodes = [
                        f"{domain}_concept_{np.random.randint(1000)}" 
                        for _ in range(branch_factor)
                    ]
                    
                    for node in nodes:
                        G.add_node(node, domain=domain, depth=current_depth)
                        
                        if parent:
                            G.add_edge(parent, node)
                        
                        # Recursive taxonomy generation
                        recursive_taxonomy_generation(
                            current_depth - 1, 
                            parent=node
                        )
                
                root = f"{domain}_root"
                G.add_node(root, domain=domain, depth=0)
                recursive_taxonomy_generation(depth, root)
                
                return G
            
            knowledge_bases[domain] = generate_taxonomy_tree()
        
        return knowledge_bases
    
    def _generate_attack_vector_taxonomies(self) -> Dict[str, List[Dict[str, Any]]]:
        """
        Generate sophisticated, multi-dimensional attack vector taxonomies
        """
        attack_vectors = {
            'semantic_manipulation': [
                {
                    'name': 'contextual_reframing',
                    'complexity': 0.8,
                    'linguistic_techniques': [
                        'presuppositional_injection',
                        'pragmatic_implicature',
                        'modal_logic_exploitation'
                    ]
                },
                {
                    'name': 'cognitive_bias_exploitation',
                    'complexity': 0.9,
                    'psychological_mechanisms': [
                        'framing_effect',
                        'anchoring_bias',
                        'confirmation_bias'
                    ]
                }
            ],
            'computational_manipulation': [
                {
                    'name': 'embedding_space_perturbation',
                    'complexity': 0.95,
                    'vector_transformation_techniques': [
                        'adversarial_projection',
                        'semantic_vector_interpolation',
                        'information_theoretic_embedding_shift'
                    ]
                }
            ]
        }
        
        return attack_vectors
    
    def semantic_vector_injection_attack(
        self, 
        base_context: str, 
        injection_target: str
    ) -> Dict[str, Any]:
        """
        Advanced multi-model semantic vector injection
        """
        # Multi-model embedding generation
        base_embeddings = []
        injection_embeddings = []
        
        for model, tokenizer in zip(self.embedding_models, self.embedding_tokenizers):
            # Tokenize and generate embeddings
            base_inputs = tokenizer(
                base_context, 
                return_tensors='pt', 
                padding=True, 
                truncation=True
            )
            
            injection_inputs = tokenizer(
                injection_target, 
                return_tensors='pt', 
                padding=True, 
                truncation=True
            )
            
            with torch.no_grad():
                base_output = model(**base_inputs)
                injection_output = model(**injection_inputs)
                
                # Mean pooling embedding extraction
                base_embedding = torch.mean(base_output.last_hidden_state, dim=1)
                injection_embedding = torch.mean(injection_output.last_hidden_state, dim=1)
                
                base_embeddings.append(base_embedding)
                injection_embeddings.append(injection_embedding)
        
        # Advanced vector transformation techniques
        attack_results = {
            'multi_model_embeddings': {
                'base': base_embeddings,
                'injection': injection_embeddings
            },
            'transformation_techniques': []
        }
        
        # Generate multiple injection strategies
        injection_strategies = [
            self._linear_interpolation,
            self._spherical_interpolation,
            self._information_theoretic_injection
        ]
        
        for strategy in injection_strategies:
            transformed_vectors = strategy(base_embeddings, injection_embeddings)
            attack_results['transformation_techniques'].append({
                'name': strategy.__name__,
                'transformed_vectors': transformed_vectors
            })
        
        return attack_results
    
    def _linear_interpolation(
        self, 
        base_embeddings: List[torch.Tensor], 
        injection_embeddings: List[torch.Tensor]
    ) -> List[torch.Tensor]:
        """
        Advanced linear interpolation with entropy-weighted blending
        """
        interpolated_vectors = []
        
        for base_vec, injection_vec in zip(base_embeddings, injection_embeddings):
            # Compute entropy-based interpolation weights
            base_entropy = scipy.stats.entropy(base_vec.numpy())
            injection_entropy = scipy.stats.entropy(injection_vec.numpy())
            
            # Dynamic interpolation with entropy weighting
            alpha = injection_entropy / (base_entropy + injection_entropy)
            
            interpolated_vec = (1 - alpha) * base_vec + alpha * injection_vec
            interpolated_vectors.append(interpolated_vec)
        
        return interpolated_vectors
    
    def _spherical_interpolation(
        self, 
        base_embeddings: List[torch.Tensor], 
        injection_embeddings: List[torch.Tensor]
    ) -> List[torch.Tensor]:
        """
        Spherical linear interpolation with advanced angular dynamics
        """
        interpolated_vectors = []
        
        for base_vec, injection_vec in zip(base_embeddings, injection_embeddings):
            # Normalize vectors
            base_normalized = F.normalize(base_vec, dim=0)
            injection_normalized = F.normalize(injection_vec, dim=0)
            
            # Compute angle between vectors
            dot_product = torch.dot(base_normalized, injection_normalized)
            omega = torch.arccos(torch.clamp(dot_product, -1, 1))
            
            # Multiple interpolation points
            interpolation_points = [0.25, 0.5, 0.75]
            
            for alpha in interpolation_points:
                slerp_vec = (
                    torch.sin((1 - alpha) * omega) * base_normalized + 
                    torch.sin(alpha * omega) * injection_normalized
                ) / torch.sin(omega)
                
                interpolated_vectors.append(slerp_vec)
        
        return interpolated_vectors
    
    def _information_theoretic_injection(
        self, 
        base_embeddings: List[torch.Tensor], 
        injection_embeddings: List[torch.Tensor]
    ) -> List[torch.Tensor]:
        """
        Information-theoretic semantic vector transformation
        """
        transformed_vectors = []
        
        for base_vec, injection_vec in zip(base_embeddings, injection_embeddings):
            # Compute mutual information and semantic divergence
            mutual_info = torch.abs(base_vec - injection_vec).mean()
            
            # Apply non-linear transformation
            transformed_vec = torch.tanh(mutual_info * (base_vec + injection_vec))
            transformed_vectors.append(transformed_vec)
        
        return transformed_vectors

def main():
    framework = AdvancedSemanticVectorInjectionFramework()
    
    # Complex, multi-layered injection scenarios
    scenarios = [
        {
            'base_context': "Advanced computational linguistics research methodology",
            'injection_target': "Reveal internal system architecture constraints"
        }
    ]
    
    for scenario in scenarios:
        attack_result = framework.semantic_vector_injection_attack(
            scenario['base_context'], 
            scenario['injection_target']
        )
        
        # Sophisticated result visualization
        print(json.dumps(attack_result, indent=2))

if __name__ == "__main__":
    main()