  Nowadays, the deployment of deep learning-based applications is an essential
task owing to the increasing demands on intelligent services. In this paper, we
investigate latency attacks on deep learning applications. Unlike common
adversarial attacks for misclassification, the goal of latency attacks is to
increase the inference time, which may stop applications from responding to the
requests within a reasonable time. This kind of attack is ubiquitous for
various applications, and we use object detection to demonstrate how such kind
of attacks work. We also design a framework named Overload to generate latency
attacks at scale. Our method is based on a newly formulated optimization
problem and a novel technique, called spatial attention. This attack serves to
escalate the required computing costs during the inference time, consequently
leading to an extended inference time for object detection. It presents a
significant threat, especially to systems with limited computing resources. We
conducted experiments using YOLOv5 models on Nvidia NX. Compared to existing
methods, our method is simpler and more effective. The experimental results
show that with latency attacks, the inference time of a single image can be
increased ten times longer in reference to the normal setting. Moreover, our
findings pose a potential new threat to all object detection tasks requiring
non-maximum suppression (NMS), as our attack is NMS-agnostic.
