Title: SlothBomb: Efficiency Poisoning Attack against Dynamic Neural Networks. Abstract: Recent increases in deploying deep neural networks (DNNs) on resource-constrained devices, combined with the observation that not all input samples require the same amount of computations, have sparked interest in input-adaptive dynamic neural networks (DyNNs). These DyNNs bring in more efficient inferences and enable deploying DNNs on resource-constrained devices e.g., mobile devices. In this work, we study a new vulnerability about DyNNs: can adversaries manipulate the DyNNs efficiency to provide a false sense of efficiency? To answer this question, we design SlothBomb, an adversarial attack that injects efficiency backdoors in DyNNs. SlothBomb can poison just a minimal percentage of DyNNs training data to inject a backdoor trigger into DyNNs. In the inference time, SlothBomb can use the backdoor trigger to slow down DyNNs and abuse the computational resources of DyNNs - an availability threat analogous to the denial-of-service attacks. We evaluate SlothBomb on three DNN backbone architectures (based on VGG16, MobileNet, and ResNet56) on two popular datasets (CIFAR-10 and Tiny ImageNet) We show that a SlothBomb reduces the efficacy of DyNNs on triggered input samples while keeping almost the same efficiency on clean samples.