# Intention Analysis Makes LLMs A Good Jailbreak Defender

**Authors:** Yuqi Zhang, Liang Ding, Lefei Zhang, Dacheng Tao  
**arXiv:** [2401.06561](https://arxiv.org/abs/2401.06561)  
**Date:** 2024-01-12  
**Summary page:** https://www.summarizepaper.com/en/paper/2401.06561v3/

## Key points

- Study titled "Intention Analysis Makes LLMs A Good Jailbreak Defender" addresses aligning large language models (LLMs) with human values in jailbreak attacks
- Introduces defense strategy called Intention Analysis ($\mathbb{IA}$) to enhance LLM safety without compromising helpfulness
- $\mathbb{IA}$ leverages LLMs' self-correcting abilities through essential intention analysis and policy-aligned response
- Extensive experiments conducted using various jailbreak benchmarks show $\mathbb{IA$}'s effectiveness in reducing harmful responses by -53.1% attack success rate on average
- Vicuna-7B outperformed GPT-3.5 in attack success rate with $\mathbb{IA}$
- Authors provide insights into how Intention Analysis improves LLM defenses against jailbreak attacks
- Code and scripts for reproducibility available on GitHub at https://github.com/alphadl/SafeLLM_with_IntentionAnalysis

## Summary

In their study titled "Intention Analysis Makes LLMs A Good Jailbreak Defender," authors Yuqi Zhang, Liang Ding, Lefei Zhang, and Dacheng Tao address the challenge of aligning large language models (LLMs) with human values in the context of complex and stealthy jailbreak attacks. They introduce a defense strategy called Intention Analysis ($\mathbb{IA}$) aimed at enhancing the safety of LLMs without compromising their helpfulness. The core principle behind $\mathbb{IA}$ is to leverage LLMs' inherent self-correcting and improving abilities through a two-stage process: essential intention analysis followed by policy-aligned response. The authors conducted extensive experiments using various jailbreak benchmarks such as ChatGLM, LLaMA2, Vicuna, MPT, DeepSeek, and GPT-3.5 to evaluate the effectiveness of $\mathbb{IA}$. Their results demonstrate that this method consistently reduces the harmfulness in responses by an average of -53.1% attack success rate while maintaining general helpfulness. Particularly noteworthy is the finding that with the implementation of $\mathbb{IA}$, Vicuna-7B even outperformed GPT-3.5 in terms of attack success rate. Furthermore, the authors provide insights into how Intention Analysis works to improve LLM defenses against jailbreak attacks. To ensure reproducibility and facilitate further research in this area, they have made their code and scripts available on GitHub at https://github.com/alphadl/SafeLLM_with_IntentionAnalysis. Overall, this study highlights the significance of incorporating intentional analysis techniques to enhance the security and reliability of large language models when faced with adversarial challenges like jailbreak attacks.