Panacea: Mitigating Harmful Fine-tuning for Large Language Models via Post-fine-tuning Perturbation

Harmful fine-tuning attack introduces significant security risks to the fine-tuning services. Main-stream defenses aim to vaccinate the model such that the later harmful fine-tuning attack is less effective. However, our evaluation results show that such defenses are fragile-- with a few fine-tuning steps, the model still can learn the harmful knowledge. To this end, we do further experiment and find that an embarrassingly simple solution-- adding purely random perturbations to the fine-tuned model, can recover the model from harmful behaviors, though it leads to a degradation in the model’s fine-tuning performance. To address the degradation of fine-tuning performance, we further propose \methodname, which optimizes an adaptive perturbation that will be applied to the model after fine-tuning. \methodname maintains model's safety alignment performance without compromising downstream fine-tuning performance. Comprehensive experiments are conducted on different harmful ratios, fine-tuning tasks and mainstream LLMs, where the average harmful scores are reduced by up-to 21. 2%, while maintaining fine-tuning performance. As a by-product, we analyze the adaptive perturbation and show that different layers in various LLMs have distinct safety coefficients. Source code available at https: //github. com/w-yibo/Panacea.

Authors

• Yibo Wang National Key Laboratory for Novel Software Technology, Nanjing University School of Artificial Intelligence, Nanjing University
• Tiansheng Huang
• Li Shen Shenzhen Campus of Sun Yat-sen University
• Huanjin Yao
• Haotian Luo
• Rui Liu
• Naiqiang Tan
• Jiaxing Huang DAMO Academy, Alibaba Group
• Dacheng Tao College of Computing and Data Science, Nanyang Technological University

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