DPOison-VL: Clean Preference Poisoning in Vision-Language Direct Preference Optimization
Direct Preference Optimization (DPO) has become a widely adopted approach for aligning vision-language models (VLMs), yet the security of multimodal preference data remains largely unexplored. Existing preference poisoning attacks either corrupt explicit supervision signals or require visible triggers, making them detectable under human inspection. We introduce DPOison-VL, the first clean preference poisoning attack against VLM-DPO, which manipulates model alignment solely through imperceptible perturbations on a small fraction of training images, while leaving all prompts, responses, and preference annotations semantically intact. We formulate poison construction as a bilevel optimizatio...
| Authors | Chengtao Jian, Kai Yang |
|---|---|
| Primary category | cs.CR |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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