Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder

Alvin Chan; Yi Tay; Yew-Soon Ong; Aston Zhang

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arXiv · 2010.02684

Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder

Alvin Chan·Yi Tay·Yew-Soon Ong·Aston Zhang

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Abstract

This paper demonstrates a fatal vulnerability in natural language inference (NLI) and text classification systems. More concretely, we present a 'backdoor poisoning' attack on NLP models. Our poisoning attack utilizes conditional adversarially regularized autoencoder (CARA) to generate poisoned training samples by poison injection in latent space. Just by adding 1% poisoned data, our experiments show that a victim BERT finetuned classifier's predictions can be steered to the poison target class with success rates of >80% when the input hypothesis is injected with the poison signature, demonstrating that NLI and text classification systems face a huge security risk.

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Alvin Chan, Yi Tay, Yew-Soon Ong, Aston Zhang. 2020-10-06. Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder. https://arxiv.org/abs/2010.02684

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