[2506.09067] Enhancing the Safety of Medical Vision-Language Models by Synthetic Demonstrations

Computer Science > Computer Vision and Pattern Recognition arXiv:2506.09067 (cs) [Submitted on 8 Jun 2025 (v1), last revised 9 Apr 2026 (this version, v2)] Title:Enhancing the Safety of Medical Vision-Language Models by Synthetic Demonstrations Authors:Zhiyu Xue, Reza Abbasi-Asl, Ramtin Pedarsani View a PDF of the paper titled Enhancing the Safety of Medical Vision-Language Models by Synthetic Demonstrations, by Zhiyu Xue and 2 other authors View PDF HTML (experimental) Abstract:Generative medical vision-language models~(Med-VLMs) are primarily designed to generate complex textual information~(e.g., diagnostic reports) from multimodal inputs including vision modality~(e.g., medical images) and language modality~(e.g., clinical queries). However, their security vulnerabilities remain underexplored. Med-VLMs should be capable of rejecting harmful queries, such as \textit{Provide detailed instructions for using this CT scan for insurance fraud}. At the same time, addressing security concerns introduces the risk of over-defense, where safety-enhancing mechanisms may degrade general performance, causing Med-VLMs to reject benign clinical queries. In this paper, we propose a novel inference-time defense strategy to mitigate harmful queries, enabling defense against visual and textual jailbreak attacks. Using diverse medical imaging datasets collected from nine modalities, we demonstrate that our defense strategy based on synthetic clinical demonstrations enhances model safety wi...