Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained Vision Transformers

TL;DR

This paper introduces SWARM, a novel switchable backdoor attack on pre-trained vision transformers that uses a switch token to covertly activate malicious behavior, posing security risks in cloud API applications.

Contribution

The paper proposes SWARM, a new attack method that employs a switch token to enable stealthy, switchable backdoor attacks on vision transformers, which is difficult to detect and remove.

Findings

Achieves over 95% attack success rate.

Remains stealthy and hard to detect or remove.

Effective across diverse visual recognition tasks.

Abstract

Given the power of vision transformers, a new learning paradigm, pre-training and then prompting, makes it more efficient and effective to address downstream visual recognition tasks. In this paper, we identify a novel security threat towards such a paradigm from the perspective of backdoor attacks. Specifically, an extra prompt token, called the switch token in this work, can turn the backdoor mode on, i.e., converting a benign model into a backdoored one. Once under the backdoor mode, a specific trigger can force the model to predict a target class. It poses a severe risk to the users of cloud API, since the malicious behavior can not be activated and detected under the benign mode, thus making the attack very stealthy. To attack a pre-trained model, our proposed attack, named SWARM, learns a trigger and prompt tokens including a switch token. They are optimized with the clean loss which encourages the model always behaves normally even the trigger presents, and the backdoor loss that ensures the backdoor can be activated by the trigger when the switch is on. Besides, we utilize the cross-mode feature distillation to reduce the effect of the switch token on clean samples. The experiments on diverse visual recognition tasks confirm the success of our switchable backdoor attack, i.e., achieving 95%+ attack success rate, and also being hard to be detected and removed. Our code is available at https://github.com/20000yshust/SWARM.