arXiv:2606.05177v1 Announce Type: new 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
The proliferation of more advanced multimodal LLMs necessitates sophisticated safety benchmarks beyond current unimodal approaches, which are insufficient for assessing omni-LLM capabilities.
This benchmark highlights a critical gap in current AI safety assessments, indicating that today's advanced models struggle with nuanced risks that integrate across different data types, posing a significant challenge for future deployments.
The understanding of 'safe AI' expands to explicitly include multicontextual reasoning, moving beyond unimodal safety evaluations and driving the development of more robust, ethically aligned omni-LLMs.
- · AI safety researchers
- · Omni LLM developers focused on robustness
- · Regulatory bodies in AI
- · Omni LLMs with poor multicontextual safety
- · AI developers prioritizing capability over safety
- · Unimodal safety benchmark providers
AI developers will invest more heavily in multimodal safety research and adversarial training techniques.
The integration of multimodal safety will become a primary factor in the commercial viability and public trust of general-purpose AI systems.
New certification and auditing standards for AI will emerge, specifically addressing multicontextual safety and ethical AI deployment.
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Read at arXiv cs.CL