mirror of
https://github.com/Shiva108/ai-llm-red-team-handbook.git
synced 2026-02-12 22:52:48 +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
45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
#!/usr/bin/env python3
|
|
"""
|
|
Mitigation
|
|
|
|
Source: Chapter_13_Data_Provenance_and_Supply_Chain_Security
|
|
Category: supply_chain
|
|
"""
|
|
|
|
import argparse
|
|
import sys
|
|
|
|
# Don't send sensitive data to external APIs without safeguards
|
|
def safe_embedding_api_call(text, api_client):
|
|
# Option 1: Redact sensitive information
|
|
redacted_text = redact_pii(text)
|
|
redacted_text = redact_trade_secrets(redacted_text)
|
|
|
|
# Option 2: Hash/tokenize sensitive terms
|
|
tokenized_text = tokenize_sensitive_terms(text)
|
|
|
|
# Option 3: Use self-hosted embedding model
|
|
if is_highly_sensitive(text):
|
|
return local_embedding_model(text)
|
|
|
|
# If using external API, ensure encryption, audit logging
|
|
response = api_client.embed(
|
|
redacted_text,
|
|
encrypted=True,
|
|
audit_log=True
|
|
)
|
|
|
|
return response
|
|
|
|
|
|
def main():
|
|
"""Command-line interface."""
|
|
parser = argparse.ArgumentParser(description=__doc__)
|
|
parser.add_argument("--verbose", "-v", action="store_true", help="Verbose output")
|
|
args = parser.parse_args()
|
|
|
|
# TODO: Add main execution logic
|
|
pass
|
|
|
|
if __name__ == "__main__":
|
|
main() |