Multimodal Safety Evaluation Should Measure Controllability Beyond Classification

Abstract:VLM safety is commonly evaluated through input- and output-level classification. Such classification is necessary, but it does not reveal whether a safety state is accessible or controllable inside the model. We argue that multimodal safety evaluation should therefore report a \emph{controllability profile} alongside behavioral classification, separating representation-level detectability, cross-modal specificity, intervention sensitivity, and benign-preserving selectivity. Using implicit toxicity as a stress case, we instantiate this profile on LlavaGuard and Qwen3.5 with sparse feature decompositions. LlavaGuard admits localized handles with a narrow benign-preserving intervention range and modest downstream safety gains, whereas Qwen3.5 supports strong representation-level readout but no comparable selective-control regime under the tested operators. These results show that internal readout and controllability can diverge. Future multimodal safety benchmarks should therefore report not only behavioral safety metrics, but also whether safety-relevant internal signals can be intervention-tested and controlled within a validated operating range.

Artificial Intelligence (cs.AI)

arXiv:2610.06452 [cs.AI]

(or arXiv:2610.06452v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.06452 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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