QueryAttack: Jailbreaking Aligned Large Language Models Using Structured Non-natural Query Language

• URL: http://arxiv.org/abs/2502.09723v3
• Date: Mon, 26 May 2025 10:35:21 GMT
• Title: QueryAttack: Jailbreaking Aligned Large Language Models Using Structured Non-natural Query Language
• Authors: Qingsong Zou, Jingyu Xiao, Qing Li, Zhi Yan, Yuhang Wang, Li Xu, Wenxuan Wang, Kuofeng Gao, Ruoyu Li, Yong Jiang,
• Abstract summary: We propose a novel framework to examine the generalizability of safety alignment.<n>By treating LLMs as knowledge databases, we translate malicious queries in natural language into structured non-natural query language.<n>We conduct extensive experiments on mainstream LLMs, and the results show that QueryAttack can achieve high attack success rates.
• Score: 44.27350994698781
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large language models (LLMs) have demonstrated remarkable potential in the field of natural language processing. Unfortunately, LLMs face significant security and ethical risks. Although techniques such as safety alignment are developed for defense, prior researches reveal the possibility of bypassing such defenses through well-designed jailbreak attacks. In this paper, we propose QueryAttack, a novel framework to examine the generalizability of safety alignment. By treating LLMs as knowledge databases, we translate malicious queries in natural language into structured non-natural query language to bypass the safety alignment mechanisms of LLMs. We conduct extensive experiments on mainstream LLMs, and the results show that QueryAttack not only can achieve high attack success rates (ASRs), but also can jailbreak various defense methods. Furthermore, we tailor a defense method against QueryAttack, which can reduce ASR by up to $64\%$ on GPT-4-1106. Our code is available at https://github.com/horizonsinzqs/QueryAttack.

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