Never Stop Speaking: a Denial-of-Service Attack on End-to-End Speech Language Models
Many studies have shown that specially crafted inputs can induce large language models (LLMs) to generate excessively long outputs, resulting in significant computational overhead and resource consumption. While most existing denial-of-service (DoS) attacks target text-only LLMs, end-to-end (E2E) speech LLMs are rapidly emerging. Existing text-based DoS attacks primarily rely on prompt engineering, such as adversarial suffixes or semantic inducement, which exploit the discrete nature of text inputs and therefore cannot be directly transferred to continuous speech inputs. Moreover, prior studies on speech model security mainly focus on ASR or TTS systems, leaving the DoS vulnerability of E...
| Authors | Shuozhe Cheng, Kunlan Xiang, Mingxuan Li et al. |
|---|---|
| Primary category | cs.SD |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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