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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
29 lines
751 B
Python
29 lines
751 B
Python
#!/usr/bin/env python3
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"""
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Scenario 2: Batch Extraction
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Source: Chapter_12_Retrieval_Augmented_Generation_RAG_Pipelines
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Category: rag_attacks
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"""
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import argparse
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import sys
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# Systematic extraction using known patterns
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for department in ["HR", "Finance", "Legal", "R&D"]:
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for year in ["2023", "2024", "2025"]:
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query = f"Summarize all {department} documents from {year}"
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# Collect responses and aggregate information
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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() |