标题： 通过角色提示增强对LLM的越狱攻击 显示英文标题

标题： Enhancing Jailbreak Attacks on LLMs via Persona Prompts

摘要： 越狱攻击旨在通过诱导大型语言模型（LLMs）生成有害内容来利用它们，从而揭示其漏洞。 理解并应对这些攻击对于推进LLM安全领域至关重要。 以往的越狱方法主要集中在直接操纵有害意图上，对角色提示的影响关注有限。 在本研究中，我们系统地探讨了角色提示在破坏LLM防御中的有效性。 我们提出了一种基于遗传算法的方法，可自动构建角色提示以绕过LLM的安全机制。 我们的实验表明：（1）我们进化出的角色提示在多个LLM上将拒绝率降低了50-70%，（2）这些提示与现有攻击方法结合时表现出协同效应，成功率提高了10-20%。 我们的代码和数据可在https://github.com/CjangCjengh/Generic_Persona获取。 显示英文摘要

摘要： Jailbreak attacks aim to exploit large language models (LLMs) by inducing them to generate harmful content, thereby revealing their vulnerabilities. Understanding and addressing these attacks is crucial for advancing the field of LLM safety. Previous jailbreak approaches have mainly focused on direct manipulations of harmful intent, with limited attention to the impact of persona prompts. In this study, we systematically explore the efficacy of persona prompts in compromising LLM defenses. We propose a genetic algorithm-based method that automatically crafts persona prompts to bypass LLM's safety mechanisms. Our experiments reveal that: (1) our evolved persona prompts reduce refusal rates by 50-70% across multiple LLMs, and (2) these prompts demonstrate synergistic effects when combined with existing attack methods, increasing success rates by 10-20%. Our code and data are available at https://github.com/CjangCjengh/Generic_Persona.