Security concerns related to Large Language Models (LLMs) have been
extensively explored, yet the safety implications for Multimodal Large Language
Models (MLLMs), particularly in medical contexts (MedMLLMs), remain
insufficiently studied. This paper delves into the underexplored security
vulnerabilities of MedMLLMs, especially when deployed in clinical environments
where the accuracy and relevance of question-and-answer interactions are
critically tested against complex medical challenges. By combining existing
clinical medical data with atypical natural phenomena, we redefine two types of
attacks: mismatched malicious attack (2M-attack) and optimized mismatched
malicious attack (O2M-attack). Using our own constructed voluminous 3MAD
dataset, which covers a wide range of medical image modalities and harmful
medical scenarios, we conduct a comprehensive analysis and propose the MCM
optimization method, which significantly enhances the attack success rate on
MedMLLMs. Evaluations with this dataset and novel attack methods, including
white-box attacks on LLaVA-Med and transfer attacks on four other
state-of-the-art models, indicate that even MedMLLMs designed with enhanced
security features are vulnerable to security breaches. Our work underscores the
urgent need for a concerted effort to implement robust security measures and
enhance the safety and efficacy of open-source MedMLLMs, particularly given the
potential severity of jailbreak attacks and other malicious or clinically
significant exploits in medical settings. For further research and replication,
anonymous access to our code is available at
this https URL Warning: Medical large model
jailbreaking may generate content that includes unverified diagnoses and
treatment recommendations. Always consult professional medical advice.

介绍了 MedMLLMs（医学环境下的多模态大型语言模型）的安全漏洞，并提出了两种攻击类型（2M-attack 和 O2M-attack），并通过构建 3MAD 数据集进行评估，指出即使设计具有增强安全功能的 MedMLLMs 也存在安全漏洞，强调了在医疗环境中实施强大安全措施和增强开源 MedMLLMs 的安全性的紧迫性。

跨模态越狱与医疗多模态大语言模型的不匹配攻击

Cross-Modality Jailbreak and Mismatched Attacks on Medical Multimodal  Large Language Models

Source-free domain adaptation (SFDA) alleviates the domain discrepancy among
data obtained from domains without accessing the data for the awareness of data
privacy. However, existing conventional SFDA methods face inherent limitations
in medical contexts, where medical data are typically collected from multiple
institutions using various equipment. To address this problem, we propose a
simple yet effective method, named Uncertainty-aware Adaptive Distillation
(UAD) for the multi-source-free unsupervised domain adaptation (MSFDA) setting.
UAD aims to perform well-calibrated knowledge distillation from (i) model level
to deliver coordinated and reliable base model initialisation and (ii) instance
level via model adaptation guided by high-quality pseudo-labels, thereby
obtaining a high-performance target domain model. To verify its general
applicability, we evaluate UAD on two image-based diagnosis benchmarks among
two multi-centre datasets, where our method shows a significant performance
gain compared with existing works. The code will be available soon.

提出一种名为 UAD 的简单而有效方法，用于医学领域中的多源免监督域适应，通过模型层和实例层的知识蒸馏，实现高性能的目标领域模型，验证了其在两个图像诊断基准数据集上的普适性和显著性能提升。