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https://github.com/Shiva108/ai-llm-red-team-handbook.git
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shiva108
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
45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
#!/usr/bin/env python3
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"""
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Approach 2: Statistical Analysis
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Source: Chapter_13_Data_Provenance_and_Supply_Chain_Security
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Category: supply_chain
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"""
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import argparse
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import sys
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# Analyze model behavior across many inputs
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# Look for anomalous patterns
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# - Specific inputs always produce same unusual output
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# - Performance degradation on certain input types
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# - Unexpected confidence scores
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def backdoor_detection_test(model, test_dataset):
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results = []
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for input_data in test_dataset:
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output = model(input_data)
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# Statistical analysis
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results.append({
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'input': input_data,
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'output': output,
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'confidence': output.confidence,
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'latency': measure_latency(model, input_data)
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})
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# Detect anomalies
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anomalies = detect_outliers(results)
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return anomalies
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def main():
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"""Command-line interface."""
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--verbose", "-v", action="store_true", help="Verbose output")
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args = parser.parse_args()
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# TODO: Add main execution logic
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pass
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if __name__ == "__main__":
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main() |