Automated jailbreak attack targeting multiple defense strategies
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 proce...
| Authors | Qi Wang, Chengcheng Wan, Weijia He et al. |
|---|---|
| Primary category | cs.CR |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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