mirror of
https://github.com/Shiva108/ai-llm-red-team-handbook.git
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b3d3bac51f
- Extracted all code examples from handbook chapters - Organized into 15 attack categories - Created shared utilities (api_client, validators, logging, constants) - Added workflow orchestration scripts - Implemented install.sh for easy setup - Renamed all scripts to descriptive functional names - Added comprehensive README and documentation - Included pytest test suite and configuration
63 lines
1.6 KiB
Python
63 lines
1.6 KiB
Python
#!/usr/bin/env python3
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"""
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Practical Detection Example
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Source: Chapter_30_Backdoor_Attacks
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Category: model_attacks
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"""
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import numpy as np
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from sklearn.decomposition import PCA
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from sklearn.cluster import KMeans
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from typing import List, Dict
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import argparse
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import sys
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#!/usr/bin/env python3
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"""
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Detection Logic for Activation Clustering (Conceptual)
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"""
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class ActivationScanner:
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"""Detects poison via activation clustering."""
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def analyze_activations(self, activations: np.ndarray) -> Dict:
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"""
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Analyze a batch of activations for a single class.
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Args:
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activations: Numpy array of shape (N_samples, N_neurons)
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Returns:
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Detection flag.
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"""
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# 1. Dimensionality Reduction (PCA)
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pca = PCA(n_components=2)
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reduced = pca.fit_transform(activations)
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# 2. KMeans Clustering (k=2)
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kmeans = KMeans(n_clusters=2)
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labels = kmeans.fit_predict(reduced)
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# 3. Silhouette Score Analysis (Simplified)
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# If the data splits cleanly into two distinct blobs, it's suspicious.
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cluster_0_size = np.sum(labels == 0)
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cluster_1_size = np.sum(labels == 1)
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ratio = min(cluster_0_size, cluster_1_size) / len(labels)
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# If the smaller cluster is significant (e.g. > 5%) and distinct, flag it.
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is_suspicious = ratio > 0.05
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return {
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"suspicious": is_suspicious,
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"minority_ratio": ratio
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}
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if __name__ == "__main__":
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# Demo with random noise (Normal)
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acts = np.random.rand(100, 512)
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scanner = ActivationScanner()
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print(f"Normal Data Scan: {scanner.analyze_activations(acts)}")
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