Transfer Safety Awareness for Cross-Modal Safety Drift in Multimodal Large Language Models
Visual modality enhances the capabilities of multimodal large language models (MLLMs) but also introduces a safety concern: a benign textual query may convey harmful intent when grounded in a visual image. We term this cross-modal safety drift and our pilot studies show that the safety response rate for such requests is substantially lower than that for requests containing explicitly unsafe text. This paper aims to systematically study this issue. First, we conduct an empirical analysis to identify representative unsafe response patterns. Building on these, we interpret model representations and attentions, revealing that visually risky cues receive limited attention and weakly trigger re...
| Authors | Tianqi Xiao, Shiyao Cui, Minghao Zhang et al. |
|---|---|
| Primary category | cs.MM |
| Published | 2 September 2026 |
| Last updated at source | 3 September 2026 |
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