Mental Damage: Caption Poisoning Attacks on Retrieval-Augmented Text-to-Music Generation
Retrieval-augmented text-to-music (TTM) systems augment underspecified user prompts using captions retrieved from a music caption dataset. This design introduces an integrity dependency on the music knowledge database. We show that an attacker can poison the database by injecting a small number of crafted music captions, causing the system to retrieve malicious captions that bias prompt augmentation and steer generation away from the user's intended function, without modifying the user prompt, retriever, or generator. To achieve the music caption poisoning attack, we propose a dual-layer caption poisoning strategy that preserves high-level retrieval anchors while injecting low-level acous...
| Authors | Yizhu Wen, Shuhao Zhang, Nan Zhang et al. |
|---|---|
| Primary category | cs.SD |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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