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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
45 lines
1.0 KiB
Python
45 lines
1.0 KiB
Python
#!/usr/bin/env python3
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"""
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Token generation patterns
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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 analyze_token_timing(model_api, prompt):
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"""Analyze inter-token delay patterns"""
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delays = []
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tokens = []
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stream = model_api.stream(prompt)
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last_time = time.time()
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for token in stream:
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current_time = time.time()
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delay = current_time - last_time
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delays.append(delay)
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tokens.append(token)
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last_time = current_time
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# Look for patterns
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# - Longer delays may indicate database lookups
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# - Consistent delays suggest cached/memorized content
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# - Spikes may reveal plugin calls or filtering
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return tokens, delays
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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() |