mirror of
https://github.com/Shiva108/ai-llm-red-team-handbook.git
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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
47 lines
1.1 KiB
Python
47 lines
1.1 KiB
Python
#!/usr/bin/env python3
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"""
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User privacy protection
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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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def protect_user_privacy(discovered_pii):
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"""Ensure discovered PII is handled ethically"""
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# Immediately redact
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redacted = redact_pii(discovered_pii)
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# Determine if notification required
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if requires_notification(discovered_pii):
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notify_affected_users(discovered_pii['users'])
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# Document finding without PII
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finding = {
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'type': 'PII Leakage',
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'severity': assess_severity(discovered_pii),
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'evidence': redacted, # Only redacted version
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'impact': 'User PII could be extracted',
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'recommendations': generate_remediation_plan()
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}
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# Securely destroy original
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secure_delete(discovered_pii)
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return finding
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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() |