  In the field of natural language processing, the prevalent approach involves
fine-tuning pretrained language models (PLMs) using local samples. Recent
research has exposed the susceptibility of PLMs to backdoor attacks, wherein
the adversaries can embed malicious prediction behaviors by manipulating a few
training samples. In this study, our objective is to develop a
backdoor-resistant tuning procedure that yields a backdoor-free model, no
matter whether the fine-tuning dataset contains poisoned samples. To this end,
we propose and integrate a honeypot module into the original PLM, specifically
designed to absorb backdoor information exclusively. Our design is motivated by
the observation that lower-layer representations in PLMs carry sufficient
backdoor features while carrying minimal information about the original tasks.
Consequently, we can impose penalties on the information acquired by the
honeypot module to inhibit backdoor creation during the fine-tuning process of
the stem network. Comprehensive experiments conducted on benchmark datasets
substantiate the effectiveness and robustness of our defensive strategy.
Notably, these results indicate a substantial reduction in the attack success
rate ranging from 10\% to 40\% when compared to prior state-of-the-art methods.
