Shortcuts Arising from Contrast: Effective and Covert Clean-Label Attacks in Prompt-Based Learning
<|reference_start|>Shortcuts Arising from Contrast: Effective and Covert Clean-Label Attacks in Prompt-Based Learning: Prompt-based learning paradigm has demonstrated remarkable efficacy in enhancing the adaptability of pretrained language models (PLMs), particularly in few-shot scenarios. However, this learning paradigm has been shown to be vulnerable to backdoor attacks. The current clean-label attack, employing a specific prompt as a trigger, can achieve success without the need for external triggers and ensure correct labeling of poisoned samples, which is more stealthy compared to the poisoned-label attack, but on the other hand, it faces significant issues with false activations and poses greater challenges, necessitating a higher rate of poisoning. Using conventional negative data augmentation methods, we discovered that it is challenging to trade off between effectiveness and stealthiness in a clean-label setting. In addressing this issue, we are inspired by the notion that a backdoor acts as a shortcut and posit that this shortcut stems from the contrast between the trigger and the data utilized for poisoning. In this study, we propose a method named Contrastive Shortcut Injection (CSI), by leveraging activation values, integrates trigger design and data selection strategies to craft stronger shortcut features. With extensive experiments on full-shot and few-shot text classification tasks, we empirically validate CSI's high effectiveness and high stealthiness at low poisoning rates. Notably, we found that the two approaches play leading roles in full-shot and few-shot settings, respectively.<|reference_end|>
@article{xie2024shortcuts,
    title={Shortcuts Arising from Contrast: Effective and Covert Clean-Label
  Attacks in Prompt-Based Learning},
    author={Xiaopeng Xie, Ming Yan, Xiwen Zhou, Chenlong Zhao, Suli Wang, Yong
  Zhang, Joey Tianyi Zhou},
    journal={arXiv preprint arXiv:2404.00461},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00461},
    primaryClass={cs.LG cs.AI cs.CL cs.CR}
}