Intention Analysis Makes LLMs A Good Jailbreak Defender

Aligning large language models (LLMs) with human values, particularly in the face of stealthy and complex 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 SAP200 and DAN benchmarks across Vicuna, ChatGLM, MPT, DeepSeek, and GPT-3.5 show that $\mathbb{IA}$ could consistently and significantly reduce the harmfulness in responses (averagely -46.5\% 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.