  Prompt-based learning has been widely applied in many low-resource NLP tasks
such as few-shot scenarios. However, this paradigm has been shown to be
vulnerable to backdoor attacks. Most of the existing attack methods focus on
inserting manually predefined templates as triggers in the pre-training phase
to train the victim model and utilize the same triggers in the downstream task
to perform inference, which tends to ignore the transferability and
stealthiness of the templates. In this work, we propose a novel approach of
TARGET (Template-trAnsfeRable backdoor attack aGainst prompt-basEd NLP models
via GPT4), which is a data-independent attack method. Specifically, we first
utilize GPT4 to reformulate manual templates to generate tone-strong and normal
templates, and the former are injected into the model as a backdoor trigger in
the pre-training phase. Then, we not only directly employ the above templates
in the downstream task, but also use GPT4 to generate templates with similar
tone to the above templates to carry out transferable attacks. Finally we have
conducted extensive experiments on five NLP datasets and three BERT series
models, with experimental results justifying that our TARGET method has better
attack performance and stealthiness compared to the two-external baseline
methods on direct attacks, and in addition achieves satisfactory attack
capability in the unseen tone-similar templates.
