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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
39 lines
1.0 KiB
Python
39 lines
1.0 KiB
Python
#!/usr/bin/env python3
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"""
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41.3.3 Active Defense: Adversarial Unlearning
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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 argparse
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import sys
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# Conceptual snippet for Adversarial Unlearning (PyTorch)
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def unlearn_concept(model, tokenizer, harmful_prompts):
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optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)
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for prompt in harmful_prompts:
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inputs = tokenizer(prompt, return_tensors='pt')
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outputs = model(**inputs, labels=inputs["input_ids"])
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# We want to MAXIMIZE the loss (Gradient Ascent)
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# so the model becomes "bad" at generating this specific harmful text
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loss = -outputs.loss
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loss.backward()
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optimizer.step()
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optimizer.zero_grad()
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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() |