Steering to Say No: Configurable Refusal via Activation Steering in Vision Language Models

• URL: http://arxiv.org/abs/2602.07013v1
• Date: Sat, 31 Jan 2026 10:54:33 GMT
• Title: Steering to Say No: Configurable Refusal via Activation Steering in Vision Language Models
• Abstract summary: We develop textbfConfigurable textbfRefusal in textbfVLMs (textbfCR-VLM)<n>CR-VLM consists of three integrated components: (1) extracting a refusal vector via a teacher-forced mechanism to amplify the refusal signal; (2) introducing a gating mechanism that mitigates over-refusal by preserving acceptance for in-scope queries; and (3) designing a counterfactual vision enhancement module that aligns visual representations with refusal requirements.
• Score: 26.60985401108749
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid advancement of Vision Language Models (VLMs), refusal mechanisms have become a critical component for ensuring responsible and safe model behavior. However, existing refusal strategies are largely \textit{one-size-fits-all} and fail to adapt to diverse user needs and contextual constraints, leading to either under-refusal or over-refusal. In this work, we firstly explore the challenges mentioned above and develop \textbf{C}onfigurable \textbf{R}efusal in \textbf{VLM}s (\textbf{CR-VLM}), a robust and efficient approach for {\em configurable} refusal based on activation steering. CR-VLM consists of three integrated components: (1) extracting a configurable refusal vector via a teacher-forced mechanism to amplify the refusal signal; (2) introducing a gating mechanism that mitigates over-refusal by preserving acceptance for in-scope queries; and (3) designing a counterfactual vision enhancement module that aligns visual representations with refusal requirements. Comprehensive experiments across multiple datasets and various VLMs demonstrate that CR-VLM achieves effective, efficient, and robust configurable refusals, offering a scalable path toward user-adaptive safety alignment in VLMs.

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