Security Vulnerabilities in AI Hardware: Threats and Countermeasures

The advancement of artificial intelligence (AI) has led to its application to a broad spectrum of complex problems associated with everyday life. AI is extensively deployed in many daily applications through dedicated hardware accelerators, in several safety-critical security-sensitive systems, e.g. self-driving cars, smart home devices, health monitoring apps, bio-metric access control, public surveillance systems, etc. However, several recent studies have demonstrated that these hardware-centric AI applications are susceptible to diverse attacks, with great potential to cause harm. To mitigate such threats, many works have focused on developing effective countermeasures for different attack scenarios, to design robust AI-based system. This paper surveys different potent and imaginative attacks on AI hardware, and countermeasures to mitigate them. We analyse various threat models, and provide a detailed discussion of the challenges of attacks and the efficacy of various countermeasures. We also point to future directions of research.