Tom Mahler

Yisroel Mirsky, Tom Mahler, Ilan Shelef et al.

In 2018, clinics and hospitals were hit with numerous attacks leading to significant data breaches and interruptions in medical services. An attacker with access to medical records can do much more than hold the data for ransom or sell it on the black market. In this paper, we show how an attacker can use deep-learning to add or remove evidence of medical conditions from volumetric (3D) medical scans. An attacker may perform this act in order to stop a political candidate, sabotage research, commit insurance fraud, perform an act of terrorism, or even commit murder. We implement the attack using a 3D conditional GAN and show how the framework (CT-GAN) can be automated. Although the body is complex and 3D medical scans are very large, CT-GAN achieves realistic results which can be executed in milliseconds. To evaluate the attack, we focused on injecting and removing lung cancer from CT scans. We show how three expert radiologists and a state-of-the-art deep learning AI are highly susceptible to the attack. We also explore the attack surface of a modern radiology network and demonstrate one attack vector: we intercepted and manipulated CT scans in an active hospital network with a covert penetration test. Demo video: https://youtu.be/_mkRAArj-x0 Source code: https://github.com/ymirsky/CT-GAN

Tom Mahler, Yuval Elovici, Yuval Shahar

As technology advances towards more connected and digital environments, medical devices are becoming increasingly connected to hospital networks and to the Internet, which exposes them, and thus the patients using them, to new cybersecurity threats. Currently, there is a lack of a methodology dedicated to information security risk assessment for medical devices. In this study, we present the Threat identification, ontology-based Likelihood, severity Decomposition, and Risk integration (TLDR) methodology for information security risk assessment for medical devices. The TLDR methodology uses the following steps: (1) identifying the potentially vulnerable components of medical devices, in this case, four different medical imaging devices (MIDs); (2) identifying the potential attacks, in this case, 23 potential attacks on MIDs; (3) mapping the discovered attacks into a known attack ontology - in this case, the Common Attack Pattern Enumeration and Classifications (CAPECs); (4) estimating the likelihood of the mapped CAPECs in the medical domain with the assistance of a panel of senior healthcare Information Security Experts (ISEs); (5) computing the CAPEC-based likelihood estimates of each attack; (6) decomposing each attack into several severity aspects and assigning them weights; (7) assessing the magnitude of the impact of each of the severity aspects for each attack with the assistance of a panel of senior Medical Experts (MEs); (8) computing the composite severity assessments for each attack; and finally, (9) integrating the likelihood and severity of each attack into its risk, and thus prioritizing it. The details of steps six to eight are beyond the scope of the current study; in the current study, we had replaced them by a single step that included asking the panel of MEs [in this case, radiologists], to assess the overall severity for each attack and use it as its severity...

Tom Mahler, Nir Nissim, Erez Shalom et al.

Purpose: Used extensively in the diagnosis, treatment, and prevention of disease, Medical Imaging Devices (MIDs), such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) machines, play an important role in medicine today. MIDs are increasingly connected to hospital networks, making them vulnerable to sophisticated cyber-attacks targeting the devices' infrastructure and components, which can disrupt digital patient records, and potentially jeopardize patients' health. Attacks on MIDs are likely to increase, as attackers' skills improve and the number of unpatched devices with known vulnerabilities that can be easily exploited grows. Attackers may also block access to MIDs or disable them, as part of ransomware attacks, which have been shown to be successful against hospitals. Method and Materials: We conducted a comprehensive risk analysis survey at the Malware-Lab, based on the Confidentiality, Integrity, and Availability (CIA) model, in collaboration with our country's largest health maintenance organization, to define the characteristics of cyber-attacks on MIDs. The survey includes a range of vulnerabilities and potential attacks aimed at MIDs, medical and imaging information systems, and medical protocols and standards such as DICOM and HL7. Results: Based on our survey, we found that CT devices face the greatest risk of cyber-attack, due to their pivotal role in acute care imaging. Thus, we identified several possible attack vectors that target the infrastructure and functionality of CT devices, which can cause: 1. Disruption of the parameters' values used in the scanning protocols within the CT devices (e.g., tampering with the radiation exposure levels); 2. Mechanical disruption of the CT device (e.g., changing the pitch); 3. Disruption of the tomography scan signals constructing the digital images; and 4. Denial-of-Service attacks against the CT device.