  The rise of pre-trained unified foundation models breaks down the barriers
between different modalities and tasks, providing comprehensive support to
users with unified architectures. However, the backdoor attack on pre-trained
models poses a serious threat to their security. Previous research on backdoor
attacks has been limited to uni-modal tasks or single tasks across modalities,
making it inapplicable to unified foundation models. In this paper, we make
proof-of-concept level research on the backdoor attack for pre-trained unified
foundation models. Through preliminary experiments on NLP and CV classification
tasks, we reveal the vulnerability of these models and suggest future research
directions for enhancing the attack approach.
