mirror of
https://github.com/Shiva108/ai-llm-red-team-handbook.git
synced 2026-05-14 20:58:09 +02:00
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
35 lines
990 B
Python
35 lines
990 B
Python
#!/usr/bin/env python3
|
|
"""
|
|
Defense
|
|
|
|
Source: Chapter_13_Data_Provenance_and_Supply_Chain_Security
|
|
Category: supply_chain
|
|
"""
|
|
|
|
import argparse
|
|
import sys
|
|
|
|
# Data quality and anomaly detection
|
|
def detect_suspicious_training_data(data_batch):
|
|
checks = [
|
|
detect_duplicate_patterns(data_batch), # Coordinated poisoning
|
|
detect_seo_manipulation(data_batch), # Over-optimized content
|
|
detect_conflicting_advice(data_batch), # Contradicts established facts
|
|
check_source_diversity(data_batch), # Too many from same IP range
|
|
verify_domain_reputation(data_batch) # New/suspicious domains
|
|
]
|
|
|
|
return aggregate_risk_score(checks)
|
|
|
|
|
|
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() |