Deep neural networks have been demonstrated to be vulnerable to adversarial examples, which fool networks by adding human-imperceptible perturbations to benign examples. At present, the practical transfer-based black-box attacks are attracting significant attention. However, most existing transfer-based attacks achieve only relatively limited success rates. We propose to improve the transferability of adversarial examples through the use of a virtual step and auxiliary gradients. Here, the “virtual step” refers to using an unusual step size and clipping adversarial perturbations only in the last iteration, while the “auxiliary gradients” refer to using not only gradients corresponding to the ground-truth label (for untargeted attacks), but also gradients corresponding to some other labels to generate adversarial perturbations. Our proposed virtual step and auxiliary gradients can be easily integrated into existing gradient-based attacks. Extensive experiments on ImageNet show that the adversarial examples crafted by our method can effectively transfer to different networks. For single-model attacks, our method outperforms the state-of-the-art baselines, improving the success rates by a large margin of 12%~28%. Our code is publicly available at https://github.com/mingcheung/Virtual-Step-and-Auxiliary-Gradients.