Hijacking Large Audio-Language Models via Context-Agnostic and Imperceptible Auditory Prompt Injection
Modern Large audio-language models (LALMs) power intelligent voice interactions by tightly integrating audio and text. This integration, however, expands the attack surface beyond text and introduces vulnerabilities in the continuous, high-dimensional audio channel. While prior work studied audio jailbreaks, the security risks of malicious audio injection and downstream behavior manipulation remain underexamined. In this work, we reveal a previously overlooked threat, auditory prompt injection, under realistic constraints of audio data-only access and strong perceptual stealth. To systematically analyze this threat, we propose \textit{AudioHijack}, a general framework that generates conte...
| Authors | Meng Chen, Kun Wang, Li Lu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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