  Recent years have seen the wide application of NLP models in crucial areas
such as finance, medical treatment, and news media, raising concerns of the
model robustness and vulnerabilities. In this paper, we propose a novel
prompt-based adversarial attack to compromise NLP models and robustness
enhancement technique. We first construct malicious prompts for each instance
and generate adversarial examples via mask-and-filling under the effect of a
malicious purpose. Our attack technique targets the inherent vulnerabilities of
NLP models, allowing us to generate samples even without interacting with the
victim NLP model, as long as it is based on pre-trained language models (PLMs).
Furthermore, we design a prompt-based adversarial training method to improve
the robustness of PLMs. As our training method does not actually generate
adversarial samples, it can be applied to large-scale training sets
efficiently. The experimental results show that our attack method can achieve a
high attack success rate with more diverse, fluent and natural adversarial
examples. In addition, our robustness enhancement method can significantly
improve the robustness of models to resist adversarial attacks. Our work
indicates that prompting paradigm has great potential in probing some
fundamental flaws of PLMs and fine-tuning them for downstream tasks.
