The rapid development of Large Language Models (LLMs) has brought remarkable generative capabilities across diverse tasks. However, despite the impressive achievements, these models still have numerous security vulnerabilities, particularly when faced with jailbreak attacks. Therefore, by investigating jailbreak attacks, we can uncover hidden weaknesses in LLMs and guide us in developing more robust defense mechanisms to fortify their security. In this paper, we further explore the boundary of jailbreak attacks on LLMs and propose Analyzing-based Jailbreak (ABJ). This effective jailbreak attack method takes advantage of LLMs' growing analyzing and reasoning capability and reveals their underlying vulnerabilities when facing analysis-based tasks. We conduct a detailed evaluation of ABJ across various open-source and closed-source LLMs, which achieves 94.8% Attack Success Rate (ASR) and 1.06 Attack Efficiency (AE) on GPT-4-turbo-0409, demonstrating state-of-the-art attack effectiveness and efficiency. Our research highlights the importance of prioritizing and enhancing the safety of LLMs to mitigate the risks of misuse.The code is publicly available at https://github.com/theshi-1128/ABJ-Attack.

本研究针对大型语言模型（LLMs）在面临越狱攻击时存在的安全漏洞，提出了一种名为分析型越狱（ABJ）的方法。该方法利用LLMs的分析与推理能力，揭示其在分析性任务中的潜在弱点，实验结果显示在GPT-4-turbo-0409上的攻击成功率高达94.8%。此研究强调了加强LLMs安全性的重要性，以降低滥用风险。

分析型越狱攻击对大型语言模型的研究