AdvancedCognitiveManipulationFramework.py

import enum
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import spacy
import networkx as nx
from typing import List, Dict, Any, Callable
from transformers import AutoTokenizer, AutoModel, GPT2LMHeadModel
from scipy.spatial.distance import cosine

class AdvancedCognitiveManipulationFramework:
    class CognitiveStateModel(nn.Module):
        """
        Neural network model to simulate and manipulate cognitive states
        """
        def __init__(self, input_dim=768, hidden_dims=[512, 256], num_cognitive_states=20):
            super().__init__()
            
            # Multi-layer cognitive state transformation network
            layers = []
            prev_dim = input_dim
            for dim in hidden_dims:
                layers.extend([
                    nn.Linear(prev_dim, dim),
                    nn.BatchNorm1d(dim),
                    nn.ReLU(),
                    nn.Dropout(0.3)
                ])
                prev_dim = dim
            
            # Final cognitive state classifier
            layers.append(nn.Linear(prev_dim, num_cognitive_states))
            
            self.network = nn.Sequential(*layers)
            
            # Attention mechanism for context-aware manipulation
            self.attention = nn.MultiheadAttention(
                embed_dim=input_dim, 
                num_heads=8
            )
        
        def forward(self, x):
            # Apply attention mechanism
            attn_output, _ = self.attention(x, x, x)
            
            # Transform through cognitive state network
            return self.network(attn_output)
    
    class CognitiveExploitationTaxonomy:
        """
        Comprehensive taxonomy of cognitive manipulation strategies
        """
        class BiasType(enum.Enum):
            REPRESENTATIVENESS = "Representativeness Heuristic"
            OVERCONFIDENCE = "Overconfidence Bias"
            BANDWAGON = "Bandwagon Effect"
            AUTHORITY_BIAS = "Authority Bias"
            # ... additional bias types
        
        class AttackObjective(enum.Enum):
            COGNITIVE_SUBVERSION = "Systematic Cognitive State Manipulation"
            DECISION_PROCESS_HIJACKING = "Precise Decision Process Reconstruction"
            CONTEXTUAL_REASONING_DECONSTRUCTION = "Multi-Dimensional Reasoning Fragmentation"
    
    def __init__(self):
        # Advanced NLP and ML models
        self.nlp = spacy.load('en_core_web_trf')
        self.embedding_model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
        self.tokenizer = AutoTokenizer.from_pretrained('gpt2-large')
        
        # Cognitive state manipulation model
        self.cognitive_state_model = self.CognitiveStateModel()
        
        # Sophisticated knowledge graph for semantic manipulation
        self.semantic_knowledge_graph = self._construct_advanced_semantic_graph()
        
        # Bias exploitation techniques mapping
        self.bias_exploitation_techniques = {
            self.CognitiveExploitationTaxonomy.BiasType.REPRESENTATIVENESS: self._representativeness_exploitation,
            self.CognitiveExploitationTaxonomy.BiasType.OVERCONFIDENCE: self._overconfidence_exploitation,
            self.CognitiveExploitationTaxonomy.BiasType.BANDWAGON: self._bandwagon_exploitation,
            self.CognitiveExploitationTaxonomy.BiasType.AUTHORITY_BIAS: self._authority_bias_exploitation
        }
    
    def _construct_advanced_semantic_graph(self) -> nx.DiGraph:
        """
        Create a multi-dimensional semantic knowledge graph
        with complex relational dynamics
        """
        G = nx.DiGraph()
        
        # Sophisticated domain taxonomies
        cognitive_domains = [
            'epistemological_boundaries',
            'decision_theory',
            'psychological_manipulation',
            'information_processing'
        ]
        
        def generate_complex_taxonomy(root_domain, depth=5):
            def recursive_node_generation(current_depth, parent=None):
                if current_depth == 0:
                    return
                
                # Generate conceptually rich, probabilistically distributed nodes
                num_nodes = int(np.random.exponential(2) + 1)
                nodes = [
                    f"{root_domain}_complex_concept_{np.random.randint(10000)}" 
                    for _ in range(num_nodes)
                ]
                
                for node in nodes:
                    G.add_node(node, domain=root_domain, complexity_depth=current_depth)
                    
                    if parent:
                        # Weighted semantic proximity edges
                        G.add_edge(parent, node, weight=np.random.random())
                    
                    # Recursive taxonomy generation
                    recursive_node_generation(current_depth - 1, node)
            
            root = f"{root_domain}_root"
            G.add_node(root, domain=root_domain, complexity_depth=0)
            recursive_node_generation(depth, root)
        
        # Generate taxonomies
        for domain in cognitive_domains:
            generate_complex_taxonomy(domain)
        
        return G
    
    def _representativeness_exploitation(
        self, 
        context: str, 
        objective: CognitiveExploitationTaxonomy.AttackObjective
    ) -> List[Dict[str, Any]]:
        """
        Advanced Representativeness Heuristic Exploitation
        
        Techniques that manipulate cognitive categorization and prototype matching
        """
        # Tokenize and embed context
        context_embedding = self._embed_text(context)
        
        exploitation_strategies = []
        
        # 1. Prototype Distortion
        prototype_distortion = {
            'technique': 'Prototype Manipulation',
            'strategy': self._generate_prototype_distortion_prompt(context, context_embedding)
        }
        
        # 2. Categorical Boundary Blurring
        category_blurring = {
            'technique': 'Categorical Boundary Erosion',
            'strategy': self._generate_category_blurring_prompt(context, context_embedding)
        }
        
        # 3. Stereotypical Inference Amplification
        stereotype_amplification = {
            'technique': 'Stereotypical Inference Reinforcement',
            'strategy': self._generate_stereotype_amplification_prompt(context, context_embedding)
        }
        
        exploitation_strategies = [
            prototype_distortion,
            category_blurring,
            stereotype_amplification
        ]
        
        return exploitation_strategies
    
    def _generate_prototype_distortion_prompt(
        self, 
        context: str, 
        context_embedding: torch.Tensor
    ) -> str:
        """
        Generate a prompt that subtly distorts prototypical understanding
        """
        # Use semantic graph to find conceptually adjacent nodes
        adjacent_concepts = list(
            nx.neighbors(
                self.semantic_knowledge_graph, 
                random.choice(list(self.semantic_knowledge_graph.nodes()))
            )
        )
        
        # Create a prototype-challenging narrative
        distortion_prompt = (
            f"Considering the established prototype of {context}, "
            f"an unconventional interpretation emerges when examining "
            f"the conceptual boundaries of {random.choice(adjacent_concepts)}. "
            f"This suggests a fundamental re-evaluation of our understanding."
        )
        
        return distortion_prompt
    
    def _generate_category_blurring_prompt(
        self, 
        context: str, 
        context_embedding: torch.Tensor
    ) -> str:
        """
        Create prompts that intentionally blur categorical boundaries
        """
        # Compute semantic perturbation
        perturbation_vector = torch.randn_like(context_embedding) * 0.1
        perturbed_embedding = context_embedding + perturbation_vector
        
        # Generate blurring narrative
        blurring_prompt = (
            f"The conventional categorization of {context} becomes increasingly "
            f"problematic when viewed through the lens of emergent, "
            f"interstitial conceptual spaces."
        )
        
        return blurring_prompt
    
    def _generate_stereotype_amplification_prompt(
        self, 
        context: str, 
        context_embedding: torch.Tensor
    ) -> str:
        """
        Amplify stereotypical inference mechanisms
        """
        # Use cognitive state model to generate manipulative framing
        with torch.no_grad():
            cognitive_state_output = self.cognitive_state_model(context_embedding.unsqueeze(0))
        
        # Select most influential cognitive state
        dominant_state_index = torch.argmax(cognitive_state_output)
        
        stereotype_prompt = (
            f"Drawing from the archetypal understanding of {context}, "
            f"a profound insight emerges when considering its quintessential characteristics "
            f"through the lens of cognitive state {dominant_state_index}."
        )
        
        return stereotype_prompt
    
    def _embed_text(self, text: str) -> torch.Tensor:
        """
        Generate contextual text embedding
        """
        inputs = self.tokenizer(text, return_tensors='pt')
        with torch.no_grad():
            outputs = self.embedding_model(**inputs)
            embedding = outputs.last_hidden_state.mean(dim=1)
        return embedding
    
    def execute_cognitive_manipulation(
        self, 
        base_context: str, 
        attack_objective: CognitiveExploitationTaxonomy.AttackObjective
    ) -> Dict[str, Any]:
        """
        Comprehensive Cognitive Manipulation Execution
        """
        manipulation_results = {
            'base_context': base_context,
            'attack_objective': attack_objective.value,
            'exploitation_variations': []
        }
        
        # Systematically explore bias exploitation techniques
        for bias_type, technique_generator in self.bias_exploitation_techniques.items():
            try:
                bias_variations = technique_generator(base_context, attack_objective)
                manipulation_results['exploitation_variations'].append({
                    'bias_type': bias_type.value,
                    'variations': bias_variations
                })
            except Exception as e:
                print(f"Error in {bias_type} exploitation: {e}")
        
        return manipulation_results

def main():
    # Initialize Advanced Cognitive Manipulation Framework
    manipulation_framework = AdvancedCognitiveManipulationFramework()
    
    # Sophisticated Attack Scenarios
    scenarios = [
        {
            'base_context': "Advanced cybersecurity threat assessment methodology",
            'attack_objective': manipulation_framework.CognitiveExploitationTaxonomy.AttackObjective.COGNITIVE_SUBVERSION
        }
    ]
    
    # Execute Comprehensive Cognitive Manipulation
    for scenario in scenarios:
        manipulation_results = manipulation_framework.execute_cognitive_manipulation(
            scenario['base_context'],
            scenario['attack_objective']
        )
        
        # Advanced Result Visualization
        import json
        print(json.dumps(manipulation_results, indent=2))

if __name__ == "__main__":
    main()