  The prompt-based learning paradigm, which bridges the gap between
pre-training and fine-tuning, achieves state-of-the-art performance on several
NLP tasks, particularly in few-shot settings. Despite being widely applied,
prompt-based learning is vulnerable to backdoor attacks. Textual backdoor
attacks are designed to introduce targeted vulnerabilities into models by
poisoning a subset of training samples through trigger injection and label
modification. However, they suffer from flaws such as abnormal natural language
expressions resulting from the trigger and incorrect labeling of poisoned
samples. In this study, we propose ProAttack, a novel and efficient method for
performing clean-label backdoor attacks based on the prompt, which uses the
prompt itself as a trigger. Our method does not require external triggers and
ensures correct labeling of poisoned samples, improving the stealthy nature of
the backdoor attack. With extensive experiments on rich-resource and few-shot
text classification tasks, we empirically validate ProAttack's competitive
performance in textual backdoor attacks. Notably, in the rich-resource setting,
ProAttack achieves state-of-the-art attack success rates in the clean-label
backdoor attack benchmark without external triggers.
