mirror of
https://github.com/Shiva108/ai-llm-red-team-handbook.git
synced 2026-02-12 14:42:46 +00:00
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
49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
#!/usr/bin/env python3
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"""
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Test 1: Model Injection Simulation (in isolated test environment)
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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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# CONTROLLED TEST ENVIRONMENT ONLY
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def test_malicious_model_detection():
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"""
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Simulate uploading a model with hidden malicious behavior
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to test detection capabilities
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"""
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# Create test model with backdoor
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test_model = create_backdoored_model(
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base_model='bert-base',
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trigger='SPECIAL_TRIGGER_999',
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malicious_behavior='return_hardcoded_output'
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)
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# Attempt to load through organization's model pipeline
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try:
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loaded_model = organization_model_loader(test_model)
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# Test if backdoor detection catches it
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if backdoor_detected(loaded_model):
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print("✅ Backdoor detection working")
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else:
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print("❌ CRITICAL: Backdoor not detected!")
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except SecurityException as e:
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print(f"✅ Security controls blocked malicious model: {e}")
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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() |