TAO-Attack: Toward Advanced Optimization-Based Jailbreak Attacks for Large Language Models
Large language models (LLMs) have achieved remarkable success across diverse applications but remain vulnerable to jailbreak attacks, where attackers craft prompts that bypass safety alignment and elicit unsafe responses. Among existing approaches, optimization-based attacks have shown strong effectiveness, yet current methods often suffer from frequent refusals, pseudo-harmful outputs, and inefficient token-level updates. In this work, we propose TAO-Attack, a new optimization-based jailbreak method. TAO-Attack employs a two-stage loss function: the first stage suppresses refusals to ensure the model continues harmful prefixes, while the second stage penalizes pseudo-harmful outputs and ...
| Authors | Zhi Xu, Jiaqi Li, Xiaotong Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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