Large Language Models (LLMs), such as ChatGPT and GPT-4, are designed to
provide useful and safe responses. However, adversarial prompts known as
'jailbreaks' can circumvent safeguards, leading LLMs to generate harmful
content. Exploring jailbreak prompts can help to better reveal the weaknesses
of LLMs and further steer us to secure them. Unfortunately, existing jailbreak
methods either suffer from intricate manual design or require optimization on
another white-box model, compromising generalization or jailbreak efficiency.
In this paper, we generalize jailbreak prompt attacks into two aspects: (1)
Prompt Rewriting and (2) Scenario Nesting. Based on this, we propose ReNeLLM,
an automatic framework that leverages LLMs themselves to generate effective
jailbreak prompts. Extensive experiments demonstrate that ReNeLLM significantly
improves the attack success rate while greatly reducing the time cost compared
to existing baselines. Our study also reveals the inadequacy of current defense
methods in safeguarding LLMs. Finally, we offer detailed analysis and
discussion from the perspective of prompt execution priority on the failure of
LLMs' defense. We hope that our research can catalyze both the academic
community and LLMs vendors towards the provision of safer and more regulated
Large Language Models.

基于自动生成的破解提示，我们提出了 ReNeLLM 框架来改进大型语言模型的攻击成功率，同时降低时间成本；我们的研究揭示了当前防御方法在保护大型语言模型方面的不足，并从提示执行优先级的角度进行了详细的分析和讨论。