Sparse Tokens Suffice: Jailbreaking Audio Language Models via Token-Aware Gradient Optimization
Jailbreak attacks on audio language models (ALMs) optimize audio perturbations to elicit unsafe generations, and they typically update the entire waveform densely throughout optimization. In this work, we investigate the necessity of such dense optimization by analyzing the structure of token-aligned gradients in ALMs. We find that gradient energy is highly non-uniform across audio tokens, indicating that only a small subset of token-aligned audio regions dominates the optimization signal. Motivated by this observation, we propose Token-Aware Gradient Optimization (TAGO), which enables sparse jailbreak optimization by retaining only waveform gradients aligned with audio tokens that have h...
| Authors | Zheng Fang, Xiaosen Wang, Shenyi Zhang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 6 May 2026 |
| Last updated at source | 25 May 2026 |
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