How to stealthily poison neural network chips in the supply chain

Computer boffins have devised a potential hardware-based Trojan attack on neural network models that could be used to alter system output without detection. Adversarial attacks on neural networks and related deep learning systems have received considerable attention in recent years due to the growing use of AI-oriented systems. The researchers – doctoral student Joseph Clements and assistant professor of electrical and computer engineering Yingjie Lao at Clemson University in the US – say that they've come up with a novel threat model by which an attacker could maliciously modify hardware in the supply chain to interfere with the output of machine learning models run on the device. Attacks that focus on the supply chain appear to be fairly uncommon. In 2014, reports surfaced that the US National Security Agency participates in supply chain interdiction to intercept hardware in transit and insert backdoors.