MCBench: A Multicontext Safety Assessment Benchmark for Omni Large Language Models
Existing multimodal safety benchmarks focus solely on visual inputs and cannot assess Omni Large Language Models (LLMs) that process vision, audio, and text. We introduce MCBench, a benchmark with 1196 scenarios spanning four safety categories that require integrating multiple modalities for accurate safety assessment. Each unsafe scenario is paired with a minimally different safe counterpart to assess model sensitivity. Our evaluations of state-of-the-art models reveal significant challenges. Omni LLMs struggle with subtle or non-physical risks but perform better when salient visual or acoustic cues are present. Analysis of reasoning traces shows that, although models can extract modality-specific information, they often fail to integrate these cues effectively for safety judgments. Our findings reveal that current Omni LLMs lack robust cross-modal reasoning in safety-critical settings, underscoring the need for improved architectures and training strategies for multimodal safety.
[2606.05177] MCBench: A Multicontext Safety Assessment Benchmark for Omni Large Language Models
[Submitted on 17 Apr 2026]
Title:MCBench: A Multicontext Safety Assessment Benchmark for Omni Large Language Models
View a PDF of the paper titled MCBench: A Multicontext Safety Assessment Benchmark for Omni Large Language Models, by Manh Luong and 8 other authors
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Abstract:Existing multimodal safety benchmarks focus solely on visual inputs and cannot assess Omni Large Language Models (LLMs) that process vision, audio, and text. We introduce MCBench, a benchmark with 1196 scenarios spanning four safety categories that require integrating multiple modalities for accurate safety assessment. Each unsafe scenario is paired with a minimally different safe counterpart to assess model sensitivity. Our evaluations of state-of-the-art models reveal significant challenges. Omni LLMs struggle with subtle or non-physical risks but perform better when salient visual or acoustic cues are present. Analysis of reasoning traces shows that, although models can extract modality-specific information, they often fail to integrate these cues effectively for safety judgments. Our findings reveal that current Omni LLMs lack robust cross-modal reasoning in safety-critical settings, underscoring the need for improved architectures and training strategies for multimodal safety.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
Cite as: arXiv:2606.05177 [cs.CL]
(or arXiv:2606.05177v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.05177
arXiv-issued DOI via DataCite
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From: Manh Luong [view email] [v1] Fri, 17 Apr 2026 12:31:17 UTC (4,079 KB)
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