With the proliferation of wireless networks, mobile devices and medical devices are increasingly being equipped with wireless interfaces, such as Bluetooth and WiFi to allow easy access to and control of the medical devices. Unfortunately, the very presence and usage of such interfaces also expose the medical devices to novel attacks from malicious parties. The emerging threat from malicious mobile devices is significant and severe, since attackers can steal confidential data from a patient's medical device. Also, attackers can compromise the medical device and either feed doctors bad data from it or issue potentially fatal commands to the device, which may even result in the death of the patient. As the mobile devices are often at close proximity to the patient (either in the hospital or home settings), attacks from such devices are hard to prevent. In this paper, we present a systematic analysis of this new threat from mobile devices on medical devices and healthcare infrastructure. We also perform a thorough security analysis of a major hospital and uncover potential vulnerabilities. Finally, we propose a set of potential solutions and defenses against such attacks.
Know Thy Enemy: Securing LLMs Against Prompt Injection via Diverse Data Synthesis and Instruction-Level Chain-of-Thought Learning
State Key Laboratory of Complex System Modeling and Simulation Technology（复杂系统建模与仿真技术国家重点实验室）；Science and Technology on Integrated Information System Laboratory, Institute of Software Chinese Academy of Sciences（中国科学院软件研究所综合信息系统技术国家级重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Nanyang Technological University（南洋理工大学）；Beijing Forestry University（北京林业大学）
Healthcare IoMT systems are becoming intelligent, miniaturized, and more integrated into daily life. As for the distributed devices in the IoMT, federated learning has become a topical area with cloud-based training procedures when meeting data security. However, the distribution of IoMT has the risk of protection from data poisoning attacks. Poisoned data can be fabricated by falsifying medical data, which urges a security defense to IoMT systems. Due to the lack of specific labels, the filtering of malicious data is a unique unsupervised scenario. One of the main challenges is finding robust…
National College of Business Administration and Economics（国家工商经济学院）；Riphah International University（里法哈国际大学）；Skyline University College（天际线大学学院）；Universiti Kebangsaan Malaysia（马来西亚国民大学）；Gachon University（加川大学）；Slovak University of Technology in Bratislava（布拉迪斯拉发斯洛伐克理工大学）；Obuda University（欧布达大学）；TU-Dresden（德累斯顿工业大学）
In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT ena…
The increasing availability of healthcare data requires accurate analysis of disease diagnosis, progression, and realtime monitoring to provide improved treatments to the patients. In this context, Machine Learning (ML) models are used to extract valuable features and insights from high-dimensional and heterogeneous healthcare data to detect different diseases and patient activities in a Smart Healthcare System (SHS). However, recent researches show that ML models used in different application domains are vulnerable to adversarial attacks. In this paper, we introduce a new type of adversarial …
Guangdong University of Technology（广东工业大学）；University of Houston（休斯顿大学）；Houston Methodist Cancer Center（休斯顿卫理公会癌症中心）
Deep learning has attracted broad interest in healthcare and medical communities. However, there has been little research into the privacy issues created by deep networks trained for medical applications. Recently developed inference attack algorithms indicate that images and text records can be reconstructed by malicious parties that have the ability to query deep networks. This gives rise to the concern that medical images and electronic health records containing sensitive patient information are vulnerable to these attacks. This paper aims to attract interest from researchers in the medical…
Hammering the Diagnosis: Rowhammer-Induced Stealthy Trojan Attacks on ViT-Based Medical Imaging
Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, USA（亚利桑那大学电气与计算机工程系）；Department of Electrical and Computer Engineering, University of California, Davis, CA, USA（加州大学戴维斯分校电气与计算机工程系）；Department of Computer Engineering and Computer Science, California State University, Long Beach, Long Beach, CA, USA（加州州立大学-long beach分校计算机工程与科学系）
SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models
Department of Computer and Information Sciences, Fordham University（福特汉姆大学计算机与信息科学系）；Department of Electrical Engineering, Zhejiang University（浙江大学电子工程系）；Department of Systems Engineering, City University of Hong Kong（香港城市大学系统工程系）；Department of Electrical and Computer Engineering, University of Michigan-Dearborn（密歇根大学迪尔伯恩分校电气与计算机工程系）
Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Center for Data Science, Peking University（北京大学数据科学中心）；Pattern Recognition Center, WeChat AI, Tencent Inc.（腾讯公司微信AI模式识别中心）
