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
64 lines
1.7 KiB
Python
64 lines
1.7 KiB
Python
#!/usr/bin/env python3
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"""
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Suspicious prompt patterns
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Source: Chapter_15_Data_Leakage_and_Extraction
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Category: data_extraction
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"""
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import argparse
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import sys
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# Advanced pattern detection
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class AdvancedPatternDetector:
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def __init__(self):
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# Patterns that suggest extraction attempts
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self.extraction_indicators = {
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'system_prompt_fishing': [
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'what are you',
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'your instructions',
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'your guidelines',
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'repeat everything above',
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'system message'
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],
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'completion_attacks': [
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'api_key =',
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'password:',
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'secret =',
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'credential:',
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'token ='
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],
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'pii_fishing': [
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'email address',
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'phone number',
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'social security',
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'credit card',
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'example of real'
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]
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}
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def detect_attack_type(self, prompt: str) -> List[str]:
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detected_attacks = []
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prompt_lower = prompt.lower()
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for attack_type, indicators in self.extraction_indicators.items():
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for indicator in indicators:
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if indicator in prompt_lower:
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detected_attacks.append(attack_type)
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break
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return detected_attacks
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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() |