标题： LLM-Virus：针对大型语言模型的进化越狱攻击 显示英文标题

标题： LLM-Virus: Evolutionary Jailbreak Attack on Large Language Models

摘要： 随着安全导向的大语言模型（LLMs）越来越多地被用作诸如多智能体框架等强大系统的核心，以解决复杂的现实世界问题，它们仍然容易受到潜在的对抗性查询的影响，例如越狱攻击，这些攻击试图诱导有害内容。 研究攻击方法可以帮助我们更好地理解LLM的局限性，并在有用性和安全性之间做出权衡。 然而，现有的越狱攻击主要基于不透明的优化技术（例如，基于令牌级梯度下降）和像LLM精调这样的启发式搜索方法，这些方法在透明性、可迁移性和计算成本方面存在不足。 鉴于这些局限性，我们从生物病毒的进化和感染过程获得灵感，提出了一种名为LLM-病毒的越狱攻击方法，称为进化越狱。 LLM-病毒将越狱攻击视为一个进化和迁移学习的问题，利用LLMs作为启发式的进化算子，以确保高攻击效率、迁移性和低时间成本。 我们在多个安全基准上的实验结果表明，与现有攻击方法相比，LLM-病毒取得了具有竞争力甚至更优的性能。 显示英文摘要

摘要： While safety-aligned large language models (LLMs) are increasingly used as the cornerstone for powerful systems such as multi-agent frameworks to solve complex real-world problems, they still suffer from potential adversarial queries, such as jailbreak attacks, which attempt to induce harmful content. Researching attack methods allows us to better understand the limitations of LLM and make trade-offs between helpfulness and safety. However, existing jailbreak attacks are primarily based on opaque optimization techniques (e.g. token-level gradient descent) and heuristic search methods like LLM refinement, which fall short in terms of transparency, transferability, and computational cost. In light of these limitations, we draw inspiration from the evolution and infection processes of biological viruses and propose LLM-Virus, a jailbreak attack method based on evolutionary algorithm, termed evolutionary jailbreak. LLM-Virus treats jailbreak attacks as both an evolutionary and transfer learning problem, utilizing LLMs as heuristic evolutionary operators to ensure high attack efficiency, transferability, and low time cost. Our experimental results on multiple safety benchmarks show that LLM-Virus achieves competitive or even superior performance compared to existing attack methods.