Recent research has shown that Large Language Models (LLMs) are vulnerable to automated jailbreak attacks, where adversarial suffixes crafted by algorithms appended to harmful queries bypass safety alignment and trigger unintended responses. Current methods for generating these suffixes are computationally expensive and have low Attack Success Rates (ASR), especially against well-aligned models like Llama2 and Llama3. To overcome these limitations, we introduce ADV-LLM, an iterative self-tuning process that crafts adversarial LLMs with enhanced jailbreak ability. Our framework significantly reduces the computational cost of generating adversarial suffixes while achieving nearly 100\% ASR on various open-source LLMs. Moreover, it exhibits strong attack transferability to closed-source models, achieving 99% ASR on GPT-3.5 and 49% ASR on GPT-4, despite being optimized solely on Llama3. Beyond improving jailbreak ability, ADV-LLM provides valuable insights for future safety alignment research through its ability to generate large datasets for studying LLM safety.

本研究解决了当前大语言模型（LLMs）在面对自动化越狱攻击时的脆弱性及低攻击成功率的问题。提出的ADV-LLM框架通过迭代自调整过程，显著降低了生成对抗后缀的计算成本，同时在多个开源LLMs上实现了近100%的攻击成功率，展现出对封闭源模型的强攻击可转移性，具有重大的安全研究潜力。

增强越狱能力的迭代自调整大语言模型