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.

翻译：预训练统一基础模型的兴起打破了不同模态和任务之间的壁垒，以统一架构为用户提供全面支持。然而，针对预训练模型的后门攻击对其安全性构成了严重威胁。以往关于后门攻击的研究仅局限于单模态任务或跨模态的单一任务，无法适用于统一基础模型。本文对预训练统一基础模型的后门攻击进行了概念验证级别的探索。通过在自然语言处理和计算机视觉分类任务上的初步实验，我们揭示了这些模型的脆弱性，并提出了增强攻击方法的未来研究方向。