Harnessing Textual Refusal Directions for Multimodal Safety
To improve safety in Large Language Models (LLMs) we can either perform post-training alignment or exploit refusal directions in the activation space. Both strategies are less feasible in Multimodal LLMs (MLLMs) as they require unsafe multimodal data, harder to collect than their unimodal counterpart. In this work, we relax this constraint and investigate whether textual refusal directions, extracted directly from the LLM backbone, generalize across modalities (i.e., image, video). Preliminary findings confirm this ability, though effectiveness is conditioned by layer selection, steering strength, and cross-modal alignment, with the latter causing safe multimodal inputs to be spuriously s...
| Authors | Moreno D'Incà, Nicu Sebe, Massimiliano Mancini |
|---|---|
| Primary category | cs.AI |
| Published | 30 June 2026 |
| Last updated at source | 5 July 2026 |
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