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.

翻译：基于提示的学习已被证明是预训练语言模型中的有效方法，尤其在少样本等低资源场景下。然而，预训练语言模型的可信性至关重要，已有研究表明基于提示的模板存在潜在漏洞，可能误导语言模型的预测，引发严重的安全问题。本文通过提出一种针对黑盒场景下人工模板的基于提示的对抗攻击，揭示预训练语言模型的若干漏洞。首先，我们分别设计了字符级和词级的启发式方法以破坏人工模板；随后，基于上述启发式破坏方法提出一种贪婪攻击算法；最后，我们在BERT系列模型的三种变体及八个数据集上通过分类任务评估了该方法。大量实验结果证明了该方法在攻击成功率和攻击速度方面的有效性。进一步实验表明，该方法在不同样本数量、模板长度和查询次数的场景中也展现出良好的泛化能力。