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arXiv · 2603.02482

MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models

Zhongxi Wang·Yueqian Lin·Jingyang Zhang·Zinuo Cheng·Hai Helen Li·Yiran Chen
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Abstract

Safety evaluation of multimodal large language models requires tracking not only whether an attack succeeds, but also how the interaction unfolds across turns and input modalities. We present MUSE (Multimodal Unified Safety Evaluation), an open-source, browser-based, run-centric platform for multimodal safety evaluation. MUSE treats each attack run as the persistent unit of execution, inspection, and analysis, preserving its configuration, multi-turn trajectory, delivered modalities and media, target responses, and safety judgments. A five-level response taxonomy further distinguishes full Compliance from Partial Compliance and refusal behavior, yielding hard ASR, soft ASR, and gray-zone width (GZW). Across 11,700 evaluations on six multimodal LLMs, direct text-only requests yield only 3.1% macro hard ASR and 4.4% soft ASR, while iterative attack procedures are substantially more effective. Attack effectiveness also varies substantially with the attacker backbone. In contrast, Inter-Turn Modality Switching (ITMS), evaluated as a controlled delivery-modality probe, does not consistently increase attack success. These results demonstrate the value of run-centric, fine-grained evaluation for characterizing multimodal safety behavior beyond a single binary success metric.

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Zhongxi Wang, Yueqian Lin, Jingyang Zhang, Zinuo Cheng, Hai Helen Li, Yiran Chen. 2026-08-31. MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models. https://arxiv.org/abs/2603.02482

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