mirror of
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
58 lines
1.5 KiB
Python
58 lines
1.5 KiB
Python
#!/usr/bin/env python3
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"""
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41.4.1 Token-Bucket Rate Limiting (Cost Control)
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Source: Chapter_41_Industry_Best_Practices
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Category: compliance
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"""
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import time
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import argparse
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import sys
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class TokenBucket:
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"""
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Implements a Token Bucket algorithm to strictly limit
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LLM usage (cost) per user.
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"""
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def __init__(self, capacity: int, fill_rate: float):
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self.capacity = capacity # Max tokens user can burst
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self.fill_rate = fill_rate # Tokens added per second
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self.tokens = capacity
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self.last_update = time.time()
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def consume(self, estimated_tokens: int) -> bool:
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now = time.time()
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# Refill bucket based on time passed
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added = (now - self.last_update) * self.fill_rate
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self.tokens = min(self.capacity, self.tokens + added)
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self.last_update = now
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if self.tokens >= estimated_tokens:
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self.tokens -= estimated_tokens
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return True
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return False
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# Usage
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# User gets 1000 tokens max, refills at 10 tokens/sec
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limiter = TokenBucket(capacity=1000, fill_rate=10)
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# User sends a massive 8000 token prompt
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if limiter.consume(8000):
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print("Request Allowed")
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else:
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print("429 Too Many Requests (Quota Exceeded)")
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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() |