  From tiny pacemaker chips to aircraft collision avoidance systems, the
state-of-the-art Cyber-Physical Systems (CPS) have increasingly started to rely
on Deep Neural Networks (DNNs). However, as concluded in various studies, DNNs
are highly susceptible to security threats, including adversarial attacks. In
this paper, we first discuss different vulnerabilities that can be exploited
for generating security attacks for neural network-based systems. We then
provide an overview of existing adversarial and fault-injection-based attacks
on DNNs. We also present a brief analysis to highlight different challenges in
the practical implementation of adversarial attacks. Finally, we also discuss
various prospective ways to develop robust DNN-based systems that are resilient
to adversarial and fault-injection attacks.
