initial code commit

AdvancedAdversarialDatasetExplorer.py

@@ -0,0 +1,270 @@
+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()