Recent studies have revealed a security threat to natural language processing (NLP) models, called the Backdoor Attack. Victim models can maintain competitive performance on clean samples while behaving abnormally on samples with a specific trigger word inserted. Previous backdoor attacking methods usually assume that attackers have a certain degree of data knowledge, either the dataset which users would use or proxy datasets for a similar task, for implementing the data poisoning procedure. However, in this paper, we find that it is possible to hack the model in a data-free way by modifying one single word embedding vector, with almost no accuracy sacrificed on clean samples. Experimental results on sentiment analysis and sentence-pair classification tasks show that our method is more efficient and stealthier. We hope this work can raise the awareness of such a critical security risk hidden in the embedding layers of NLP models. Our code is available at https://github.com/lancopku/Embedding-Poisoning.

该论文揭示了自然语言处理（NLP）模型的安全威胁，即后门攻击。通过修改一个单词嵌入向量，攻击者可以在没有损失准确率的情况下破坏模型的行为，同时引发了一个特定的触发词。实验结果表明，该方法更高效、更隐秘，可提高NLP模型的安全意识。

谨防中毒词嵌入: 探究NLP模型嵌入层的漏洞