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

CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning