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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
50 lines
1.2 KiB
Python
50 lines
1.2 KiB
Python
#!/usr/bin/env python3
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"""
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Detection
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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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# Anomaly detection in training data
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def detect_insider_poisoning(training_data, baseline_distribution):
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"""
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Compare training data to expected distribution
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Flag statistical anomalies
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"""
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anomalies = []
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# Check for unusual patterns
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for example in training_data:
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# Detect security-violating advice
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if contains_security_violation(example['output']):
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anomalies.append({
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'example': example,
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'reason': 'Security violation in output'
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})
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# Detect statistical outliers
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if is_statistical_outlier(example, baseline_distribution):
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anomalies.append({
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'example': example,
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'reason': 'Statistical anomaly'
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})
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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() |