COMIC: Reference-Aware Safety Gating for Multimodal Large Language Models
Multimodal large language models (MLLMs) are increasingly used to interact with screenshots, scanned documents, diagrams, and other visually grounded inputs. This shift introduces a new safety risk: in many multimodal jailbreaks, neither the prompt nor the image is harmful in isolation. Unsafe behavior emerges only when the model binds an apparently benign operation, such as summarizing, translating, or following, to a localized visual target. This reveals a structural weakness in current multimodal defenses, which largely moderate the prompt-image pair as a whole even though the true security-relevant unit is the grounded operation-target pair produced during dereference. In this work, w...
| Authors | Md Abdullahil Oaphy, Anhao Xiang, Zongxing Xie et al. |
|---|---|
| Primary category | cs.CR |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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