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arXiv · 2606.16751

Automated jailbreak attack targeting multiple defense strategies

Qi Wang·Chengcheng Wan·Weijia He·Yanqing Li·Hanqi Sun·Xiaodong Gu·Jiangtao Wang
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Abstract

Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks. However, their safety remains a critical concern due to their susceptibility to adversarial prompt-based attacks. In this paper, we present UNIATTACK, an adversarial testing framework designed from a defense-oriented perspective to systematically construct effective black-box attack prompts. Unlike prior approaches that rely on static templates or iterative model-specific tuning, UNIATTACK extracts minimal but high-impact attack features from diverse existing attacks, optimizes them via a specialized attacker LLM, and composes them into flexible templates through automated refinement process. This feature-centric construction enables one-shot attacks that generalize across multiple models and safety categories, providing a practical tool for assessing LLM robustness. Our evaluation results shows that compared to the baselines, UNIATTACK achieves an average attack success rate (ASR) improvement of 64.63\%-248.82\% on models deployed with multi-layered defense mechanisms and it only takes 0.03\%-4.96\% cost of the baselines. UNIATTACK artifact is available at https://anonymous.4open.science/r/UniAttack-Artifact-30F1.

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Qi Wang, Chengcheng Wan, Weijia He, Yanqing Li, Hanqi Sun, Xiaodong Gu, Jiangtao Wang. 2026-06-15. Automated jailbreak attack targeting multiple defense strategies. https://arxiv.org/abs/2606.16751

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