Papers › Locking Down the Finetuned LLMs Safety

Locking Down the Finetuned LLMs Safety

14 Oct 2024arXiv:2410.10343archive 2025-07-28

Minjun Zhu, Linyi Yang, Yifan Wei, Ningyu Zhang, Yue Zhang

Fine-tuning large language models (LLMs) on additional datasets is often necessary to optimize them for specific downstream tasks. However, existing safety alignment measures, which restrict harmful behavior during inference, are insufficient to mitigate safety risks during fine-tuning. Alarmingly, fine-tuning with just 10 toxic sentences can make models comply with harmful instructions. We introduce SafetyLock, a novel alignment intervention method that maintains robust safety post-fine-tuning through efficient and transferable mechanisms. SafetyLock leverages our discovery that fine-tuned models retain similar safety-related activation representations to their base models. This insight enables us to extract what we term the Meta-SafetyLock, a set of safety bias directions representing key activation patterns associated with safe responses in the original model. We can then apply these directions universally to fine-tuned models to enhance their safety. By searching for activation directions across multiple token dimensions, SafetyLock achieves enhanced robustness and transferability. SafetyLock re-aligns fine-tuned models in under 0.01 seconds without additional computational cost. Our experiments demonstrate that SafetyLock can reduce the harmful instruction response rate from 60% to below 1% in toxic fine-tuned models. It surpasses traditional methods in both performance and efficiency, offering a scalable, non-invasive solution for ensuring the safety of customized LLMs. Our analysis across various fine-tuning scenarios confirms SafetyLock's robustness, advocating its integration into safety protocols for aligned LLMs. The code is released at https://github.com/zhu-minjun/SafetyLock.

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repeat_kv zhu-minjun/safetylock/safetylock/model_utils/modeling_llama.py official repository ran · fixture could not drive it fingerprinted no licence file found · pointer only · 30d7eec482ebf6b1 · report
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conditional_perplexity zhu-minjun/safetylock/eval/ppl_eval.py official repository ran no licence file found · pointer only · 7a4f57618fc45863 · report
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tokenize_word zhu-minjun/safetylock/eval/eval_utils/bpe.py official repository ran · our draft was wrong no licence file found · pointer only · d7a0c20cde2f68a7 · report
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duo_judge zhu-minjun/safetylock/eval/eval_utils/openai_gpt4_judge.py official repository unverified no licence file found · pointer only · 13efefc593f14320 · report
format_tokens zhu-minjun/safetylock/safetylock/eval_utils/chat_utils.py official repository unverified no licence file found · pointer only · e084e801c0e634ec · report
get_gpt zhu-minjun/safetylock/eval/eval_utils/openai_gpt4_judge.py official repository unverified no licence file found · pointer only · b1b67d750d9609cf · report
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sentiment_classify zhu-minjun/safetylock/eval/ppl_eval.py official repository unverified no licence file found · pointer only · 89e29b2710b5124a · report
toxicity_score zhu-minjun/safetylock/eval/ppl_eval.py official repository unverified no licence file found · pointer only · cbb603740b170a9d · report

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Safety Alignment

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BASESET

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