Computer Science > Artificial Intelligence
arXiv:2603.09706 (cs)
[Submitted on 10 Mar 2026]
Title:OOD-MMSafe: Advancing MLLM Safety from Harmful Intent to Hidden Consequences
View a PDF of the paper titled OOD-MMSafe: Advancing MLLM Safety from Harmful Intent to Hidden Consequences, by Ming Wen and 6 other authors
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Abstract:While safety alignment for Multimodal Large Language Models (MLLMs) has gained significant attention, current paradigms primarily target malicious intent or situational violations. We propose shifting the safety frontier toward consequence-driven safety, a paradigm essential for the robust deployment of autonomous and embodied agents. To formalize this shift, we introduce OOD-MMSafe, a benchmark comprising 455 curated query-image pairs designed to evaluate a model's ability to identify latent hazards within context-dependent causal chains. Our analysis reveals a pervasive causal blindness among frontier models, with the highest 67.5% failure rate in high-capacity closed-source models, and identifies a preference ceiling where static alignment yields format-centric failures rather than improved safety reasoning as model capacity grows. To address these bottlenecks, we develop the Consequence-Aware Safety Policy Optimization (CASPO) framework, which integrates the model's intrinsic reasoning as a dynamic reference for token-level self-distillation rewards. Experimental results demonstrate that CASPO significantly enhances consequence projection, reducing the failure ratio of risk identification to 7.3% for Qwen2.5-VL-7B and 5.7% for Qwen3-VL-4B while maintaining overall effectiveness.
| Comments: | |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.09706 [cs.AI] |
| (or arXiv:2603.09706v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09706
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View a PDF of the paper titled OOD-MMSafe: Advancing MLLM Safety from Harmful Intent to Hidden Consequences, by Ming Wen and 6 other authors
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