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

Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder