Large language models (LLMs) have transformed the field of natural language
processing, but they remain susceptible to jailbreaking attacks that exploit
their capabilities to generate unintended and potentially harmful content.
Existing token-level jailbreaking techniques, while effective, face scalability
and efficiency challenges, especially as models undergo frequent updates and
incorporate advanced defensive measures. In this paper, we introduce JailMine,
an innovative token-level manipulation approach that addresses these
limitations effectively. JailMine employs an automated "mining" process to
elicit malicious responses from LLMs by strategically selecting affirmative
outputs and iteratively reducing the likelihood of rejection. Through rigorous
testing across multiple well-known LLMs and datasets, we demonstrate JailMine's
effectiveness and efficiency, achieving a significant average reduction of 86%
in time consumed while maintaining high success rates averaging 95%, even in
the face of evolving defensive strategies. Our work contributes to the ongoing
effort to assess and mitigate the vulnerability of LLMs to jailbreaking
attacks, underscoring the importance of continued vigilance and proactive
measures to enhance the security and reliability of these powerful language
models.

大型语言模型（LLMs）已经改变了自然语言处理领域，但它们仍然容易受到越狱攻击的影响，这种攻击利用了它们生成意外和潜在有害内容的能力。我们通过介绍 JailMine，一种有效应对这些限制的创新的基于令牌水平操作的方法，通过自动化的 “挖掘” 过程从 LLMs 中引发恶意响应，从而解决了现有基于令牌水平的越狱技术所面临的可扩展性和效率挑战。通过在多个知名 LLMs 和数据集上进行严格测试，我们证明了 JailMine 的有效性和效率，在保持高成功率（平均 95%）的同时，平均时间消耗大幅减少了 86％，即使面对不断进化的防御策略。我们的工作为评估和减轻 LLMs 对越狱攻击的脆弱性作出了贡献，强调了继续保持警惕和采取积极措施以增强这些强大语言模型的安全性和可靠性的重要性。