Title: Backpropagation Path Search On Adversarial Transferability. Abstract: Transfer-based attackers craft adversarial examples against surrogate models and transfer them to victim models deployed in the black-box situation. It is generally accepted that gradients from diverse modules of surrogate models used for perturbation generation contribute differently to transferability. In this paper, we propose backPropagation pAth Search (PAS), which enhances adversarial transferability from the backpropagation perspective. We use structural reparameterization to make the basic modules of DNNs (i.e., convolution and activation) calculate forward as normal but backpropagate the gradients in a skip connection form. Thus, a DAG-based search space is constructed for the backpropagation path. PAS employs Bayesian Optimization to search for the most transferable path and reduces the search overhead by the one-step approximation. We conduct comprehensive attack experiments in a wide range of transfer settings, showing that PAS improves the attack success rate by a huge margin for both normally trained and defense models.