Prompt-based learning paradigm has been shown to be vulnerable to backdoor attacks.Current clean-label attack, employing a specific prompt as trigger, can achieve success without the need for external triggers and ensuring correct labeling of poisoned samples, which are more stealthy compared to the poisonedlabel attack, but on the other hand, facing significant issues with false activations and pose greater challenges, necessitating a higher rate of poisoning.Using conventional negative data augmentation methods, we discovered that it is challenging to balance 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 fullshot and few-shot text classification tasks, we empirically validate CSI's high effectiveness and high stealthiness at low poisoning rates.