  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.
