Look Before You Leap: Enhancing Attention and Vigilance Regarding Harmful Content with GuidelineLLM

• URL: http://arxiv.org/abs/2412.10423v1
• Date: Tue, 10 Dec 2024 12:42:33 GMT
• Title: Look Before You Leap: Enhancing Attention and Vigilance Regarding Harmful Content with GuidelineLLM
• Authors: Shaoqing Zhang, Zhuosheng Zhang, Kehai Chen, Rongxiang Weng, Muyun Yang, Tiejun Zhao, Min Zhang,
• Abstract summary: Large language models (LLMs) are increasingly vulnerable to emerging jailbreak attacks. This vulnerability poses significant risks to the real-world applications. We propose a novel defensive paradigm called GuidelineLLM.
• Score: 53.79753074854936
• License:
• Abstract: Despite being empowered with alignment mechanisms, large language models (LLMs) are increasingly vulnerable to emerging jailbreak attacks that can compromise their alignment mechanisms. This vulnerability poses significant risks to the real-world applications. Existing work faces challenges in both training efficiency and generalization capabilities (i.e., Reinforcement Learning from Human Feedback and Red-Teaming). Developing effective strategies to enable LLMs to resist continuously evolving jailbreak attempts represents a significant challenge. To address this challenge, we propose a novel defensive paradigm called GuidelineLLM, which assists LLMs in recognizing queries that may have harmful content. Before LLMs respond to a query, GuidelineLLM first identifies potential risks associated with the query, summarizes these risks into guideline suggestions, and then feeds these guidelines to the responding LLMs. Importantly, our approach eliminates the necessity for additional safety fine-tuning of the LLMs themselves; only the GuidelineLLM requires fine-tuning. This characteristic enhances the general applicability of GuidelineLLM across various LLMs. Experimental results demonstrate that GuidelineLLM can significantly reduce the attack success rate (ASR) against the LLMs (an average reduction of 34.17\% ASR) while maintaining the helpfulness of the LLMs in handling benign queries. Code is available at https://github.com/sqzhang-lazy/GuidelineLLM.

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