  Vision-Language (VL) pre-trained models have shown their superiority on many
multimodal tasks. However, the adversarial robustness of such models has not
been fully explored. Existing approaches mainly focus on exploring the
adversarial robustness under the white-box setting, which is unrealistic. In
this paper, we aim to investigate a new yet practical task to craft image and
text perturbations using pre-trained VL models to attack black-box fine-tuned
models on different downstream tasks. Towards this end, we propose VLATTACK to
generate adversarial samples by fusing perturbations of images and texts from
both single-modal and multimodal levels. At the single-modal level, we propose
a new block-wise similarity attack (BSA) strategy to learn image perturbations
for disrupting universal representations. Besides, we adopt an existing text
attack strategy to generate text perturbations independent of the image-modal
attack. At the multimodal level, we design a novel iterative cross-search
attack (ICSA) method to update adversarial image-text pairs periodically,
starting with the outputs from the single-modal level. We conduct extensive
experiments to attack five widely-used VL pre-trained models for six tasks.
Experimental results show that VLATTACK achieves the highest attack success
rates on all tasks compared with state-of-the-art baselines, which reveals a
blind spot in the deployment of pre-trained VL models. Source codes can be
found at https://github.com/ericyinyzy/VLAttack.
