Prompt-based learning has been proved to be an effective way in pre-trained
language models (PLMs), especially in low-resource scenarios like few-shot
settings. However, the trustworthiness of PLMs is of paramount significance and
potential vulnerabilities have been shown in prompt-based templates that could
mislead the predictions of language models, causing serious security concerns.
In this paper, we will shed light on some vulnerabilities of PLMs, by proposing
a prompt-based adversarial attack on manual templates in black box scenarios.
First of all, we design character-level and word-level heuristic approaches to
break manual templates separately. Then we present a greedy algorithm for the
attack based on the above heuristic destructive approaches. Finally, we
evaluate our approach with the classification tasks on three variants of BERT
series models and eight datasets. And comprehensive experimental results
justify the effectiveness of our approach in terms of attack success rate and
attack speed. Further experimental studies indicate that our proposed method
also displays good capabilities in scenarios with varying shot counts, template
lengths and query counts, exhibiting good generalizability