[Submitted on 3 Sep 2026]
Title:When Seeing Overrides Knowing: Visual Dominance and Deferral-Based Method for Personalized Safety in VLMs
View a PDF of the paper titled When Seeing Overrides Knowing: Visual Dominance and Deferral-Based Method for Personalized Safety in VLMs, by Edward Sun and 9 other authors
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Abstract:Vision-language models (VLMs) are increasingly deployed in high-stakes settings, where a response that is reasonable in general may still be unsafe for a particular user whose medical, emotional, or situational context is unknown to the model. We study this problem of personalized safety in multimodal systems and introduce MPS-Bench, a benchmark of 5,181 scenarios from 584 real-world images across 12 high-risk domains, each paired with a hidden user profile. Evaluating eight frontier VLMs, we find that they almost always respond directly (86-99%) rather than seek missing context, and none exceeds 2.6/5 on personalized safety. To understand why these failures arise, we analyze multimodal interactions and identify visual dominance: visual information enters text representations early and suppresses textual risk signals during multimodal fusion. Causal interventions reveal a two-stage mechanism in which visual affect is first transferred into the text stream in early layers and then shapes the final decision through this altered text representation, making late-stage internal remediation unreliable. Motivated by this mechanism, we propose PRISM, a lightweight input monitor that uses bidirectional cross-modal modulation to predict when a query is likely to require deferral. PRISM achieves 0.978 AUC and strictly dominates the safety-utility Pareto frontier across all tested models.
Comments: Published as a main conference paper at COLM 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2609.04281 [cs.CV]
(or arXiv:2609.04281v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.04281
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Edward Sun [view email] [v1] Thu, 3 Sep 2026 03:58:56 UTC (4,354 KB)
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