MMA-SafetyBench: A Benchmark for Multimodal Agent Safety Evaluation
A new benchmark shows visual interface hijacks compromise most tested multimodal agents, with attack success near 88 percent.
Researchers introduce MMA-SafetyBench to evaluate Visual Semantic Hijacking, in which deceptive but meaningful interface elements such as forged system alerts cause a multimodal agent to leave its authorized task. The set contains 1,083 adversarial trajectories covering web automation, desktop GUI control, mobile navigation, document understanding, and video processing. Across nine models, attack success reached 87.96% and semantic compromise 98.09%. Attacks persisted under neutral prompts, and a safety-priority prompt reduced success without eliminating the failure.
- Visual Semantic Hijacking uses forged interface elements to divert agents.
- Benchmark contains 1,083 adversarial trajectories across five domains.
- Nine models reached 87.96% attack success and 98.09% semantic compromise.
- A safety-priority prompt lowers success but does not remove the failure.
From the original article
1 min read at arxiv.orgMultimodal agents increasingly rely on visual interfaces to perceive state and select actions, creating an attack surface not captured by text-only safety evaluations. We study Visual Semantic Hijacking, in which deceptive but semantically meaningful interface elements, such as forged system alerts, cause an agent to depart from its authorized task.
By Yuke Wang · arxiv.org
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