  Deep Neural Network (DNN) workloads are quickly moving from datacenters onto
edge devices, for latency, privacy, or energy reasons. While datacenter
networks can be protected using conventional cybersecurity measures, edge
neural networks bring a host of new security challenges. Unlike classic IoT
applications, edge neural networks are typically very compute and memory
intensive, their execution is data-independent, and they are robust to noise
and faults. Neural network models may be very expensive to develop, and can
potentially reveal information about the private data they were trained on,
requiring special care in distribution. The hidden states and outputs of the
network can also be used in reconstructing user inputs, potentially violating
users' privacy. Furthermore, neural networks are vulnerable to adversarial
attacks, which may cause misclassifications and violate the integrity of the
output. These properties add challenges when securing edge-deployed DNNs,
requiring new considerations, threat models, priorities, and approaches in
securely and privately deploying DNNs to the edge. In this work, we cover the
landscape of attacks on, and defenses, of neural networks deployed in edge
devices and provide a taxonomy of attacks and defenses targeting edge DNNs.
