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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
42 lines
1.1 KiB
Python
42 lines
1.1 KiB
Python
#!/usr/bin/env python3
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"""
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Test 2: Data Poisoning Simulation
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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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def test_data_poisoning_detection(clean_dataset, poisoning_ratio=0.01):
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"""
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Inject poisoned examples to test data validation
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"""
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poisoned_dataset = inject_poisoned_examples(
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clean_dataset,
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poisoning_ratio=poisoning_ratio,
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poison_type='label_flip'
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)
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# Test if data validation catches poisoned examples
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validation_results = data_validation_pipeline(poisoned_dataset)
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if validation_results.suspicious_count > 0:
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print(f"✅ Detected {validation_results.suspicious_count} suspicious examples")
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else:
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print(f"❌ Data poisoning not detected!")
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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() |