Intention Analysis Makes LLMs A Good Jailbreak Defender

• URL: http://arxiv.org/abs/2401.06561v3
• Date: Mon, 29 Apr 2024 16:40:57 GMT
• Title: Intention Analysis Makes LLMs A Good Jailbreak Defender
• Authors: Yuqi Zhang, Liang Ding, Lefei Zhang, Dacheng Tao,
• Abstract summary: In this study, we present a simple yet highly effective defense strategy, i.e., Intention Analysis ($mathbbIA$) The principle behind this is to trigger LLMs' inherent self-correct and improve ability through a two-stage process. $mathbbIA$ is an inference-only method, thus could enhance the safety of LLMs without compromising their helpfulness.
• Score: 79.4014719271075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aligning large language models (LLMs) with human values, particularly in the face of complex and stealthy jailbreak attacks, presents a formidable challenge. In this study, we present a simple yet highly effective defense strategy, i.e., Intention Analysis ($\mathbb{IA}$). The principle behind this is to trigger LLMs' inherent self-correct and improve ability through a two-stage process: 1) essential intention analysis, and 2) policy-aligned response. Notably, $\mathbb{IA}$ is an inference-only method, thus could enhance the safety of LLMs without compromising their helpfulness. Extensive experiments on varying jailbreak benchmarks across ChatGLM, LLaMA2, Vicuna, MPT, DeepSeek, and GPT-3.5 show that $\mathbb{IA}$ could consistently and significantly reduce the harmfulness in responses (averagely -53.1% attack success rate) and maintain the general helpfulness. Encouragingly, with the help of our $\mathbb{IA}$, Vicuna-7B even outperforms GPT-3.5 in terms of attack success rate. Further analyses present some insights into how our method works. To facilitate reproducibility, we release our code and scripts at: https://github.com/alphadl/SafeLLM_with_IntentionAnalysis.

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