Scenes as an Attack Surface: Uncovering Scene-Grounded Safety Failures in MLLMs

Summary

Researchers show that task-relevant scene semantics alone can move multimodal large language models (MLLMs) from refusal to compliance, without adding attack instructions to the visual input. They introduce SceneSafetyBench, covering eight risk categories and nine white- and black-box MLLMs, and propose Scene-Grounded Attacks (SGA). SGA reaches an average attack success rate of 86.17% across nine target MLLMs, 8.55 percentage points above the strongest baseline.

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