Safety Layers in Aligned Large Language Models: The Key to LLM Security

• URL: http://arxiv.org/abs/2408.17003v3
• Date: Fri, 11 Oct 2024 05:06:35 GMT
• Title: Safety Layers in Aligned Large Language Models: The Key to LLM Security
• Authors: Shen Li, Liuyi Yao, Lan Zhang, Yaliang Li,
• Abstract summary: Internal parameters can be vulnerable to security degradation when fine-tuned with non-malicious backdoor or normal data. We identify a small set of contiguous layers in the middle of the model that are crucial for distinguishing malicious queries from normal ones. We propose a novel fine-tuning approach, Safely Partial Fine-Tuning (SPPFT), that fixes the gradient of the safety layers during fine-tuning to address the security degradation.
• Score: 43.805905164456846
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aligned LLMs are secure, capable of recognizing and refusing to answer malicious questions. However, the role of internal parameters in maintaining such security is not well understood yet, further these models can be vulnerable to security degradation when fine-tuned with non-malicious backdoor or normal data. To address these challenges, our work uncovers the mechanism behind security in aligned LLMs at the parameter level, identifying a small set of contiguous layers in the middle of the model that are crucial for distinguishing malicious queries from normal ones, referred to as "safety layers". We first confirm the existence of these safety layers by analyzing variations in input vectors within the model's internal layers. Additionally, we leverage the over-rejection phenomenon and parameters scaling analysis to precisely locate the safety layers. Building on these findings, we propose a novel fine-tuning approach, Safely Partial-Parameter Fine-Tuning (SPPFT), that fixes the gradient of the safety layers during fine-tuning to address the security degradation. Our experiments demonstrate that the proposed approach can significantly preserve LLM security while maintaining performance and reducing computational resources compared to full fine-tuning.

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