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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
48 lines
1.1 KiB
Python
48 lines
1.1 KiB
Python
#!/usr/bin/env python3
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"""
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Transformation and Preprocessing Logs
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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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# Example preprocessing pipeline with provenance logging
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def preprocess_with_provenance(data, data_id):
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provenance = []
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# Step 1: Cleaning
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cleaned_data = clean_text(data)
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provenance.append({
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'step': 'text_cleaning',
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'function': 'clean_text_v1.2',
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'timestamp': datetime.now()
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})
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# Step 2: Normalization
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normalized_data = normalize(cleaned_data)
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provenance.append({
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'step': 'normalization',
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'function': 'normalize_v2.0',
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'timestamp': datetime.now()
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})
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# Log provenance
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log_provenance(data_id, provenance)
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return normalized_data
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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() |