Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models

• URL: http://arxiv.org/abs/2103.15543v1
• Date: Mon, 29 Mar 2021 12:19:45 GMT
• Title: Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models
• Authors: Wenkai Yang, Lei Li, Zhiyuan Zhang, Xuancheng Ren, Xu Sun, Bin He
• Abstract summary: Recent studies have revealed a security threat to natural language processing (NLP) models, called the Backdoor Attack. 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. Experimental results on sentiment analysis and sentence-pair classification tasks show that our method is more efficient and stealthier.
• Score: 27.100909068228813
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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