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 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 without significantly compromising performance. Additionally, we find that increasing the demonstration budget alleviates the over-defense issue. We then introduce a mixed demonstration strategy as a trade-off solution for balancing security and performance under few-shot demonstration budget constraints. Warning: This paper contains content that may be deemed harmful.

Medical vision-language models (Med-VLMs) offer a new and effective paradigm for digital health in tasks such as disease diagnosis using clinical images and text. In these tasks, an important but underexplored research question is how Med-VLMs interpret and respond to user-provided clinical information, especially when the prompts are noisy. For a systematic evaluation, we construct Med-CP, a large-scale visual question answering (VQA) benchmark designed to comprehensively evaluate the influence of clinical prompts across diverse modalities, anatomical regions, and diagnostic tasks. Our experiments reveal that existing Med-VLMs tend to follow user-provided prompts blindly, regardless of whether they are accurate or not, raising concerns about their reliability in real-world interactions. To address this problem, we introduce a novel supervised fine-tuning (SFT) approach for Med-VLMs based on cross-modal reflection chain-of-thought (CoT) across medical images and text. In our SFT method, the Med-VLM is trained to produce reasoning paths for the analysis of the medical image and the user-provided prompt. Then, the final answer is determined by conducting a reflection on the visual and textual information. Experimental results demonstrate that our method considerably enhances the robustness against noisy user-provided prompts for both in-domain and out-of-domain evaluation scenarios.

Temporal progression is an integral part of knowledge accumulation and update. Web search is frequently adopted as the grounding for agent knowledge, yet an improper configuration affects the quality of the agent’s responses. Here, we assess the agent behavior using distinct date-controlled tools (DCTs) as a stress test to measure the knowledge variability of large language model (LLM) agents. We demonstrate the temporal effects of an LLM agent as a writing assistant, which uses web search to complete scientific publication abstracts. We show that the temporality of search engines translates into tool-dependent agent performance but can be alleviated with base model choice and explicit reasoning instructions such as chain-of-thought prompting. Our results indicate that agent design and evaluations should take a dynamical view and implement effective measures to account for the temporal influence of external resources to improve agent reliability.