Weight Poisoning Attacks on Pre-trained Models

• URL: http://arxiv.org/abs/2004.06660v1
• Date: Tue, 14 Apr 2020 16:51:42 GMT
• Title: Weight Poisoning Attacks on Pre-trained Models
• Authors: Keita Kurita, Paul Michel, Graham Neubig
• Abstract summary: We show that it is possible to construct weight poisoning'' attacks where pre-trained weights are injected with vulnerabilities that expose backdoors'' after fine-tuning. Our experiments on sentiment classification, toxicity detection, and spam detection show that this attack is widely applicable and poses a serious threat.
• Score: 103.19413805873585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, NLP has seen a surge in the usage of large pre-trained models. Users download weights of models pre-trained on large datasets, then fine-tune the weights on a task of their choice. This raises the question of whether downloading untrusted pre-trained weights can pose a security threat. In this paper, we show that it is possible to construct ``weight poisoning'' attacks where pre-trained weights are injected with vulnerabilities that expose ``backdoors'' after fine-tuning, enabling the attacker to manipulate the model prediction simply by injecting an arbitrary keyword. We show that by applying a regularization method, which we call RIPPLe, and an initialization procedure, which we call Embedding Surgery, such attacks are possible even with limited knowledge of the dataset and fine-tuning procedure. Our experiments on sentiment classification, toxicity detection, and spam detection show that this attack is widely applicable and poses a serious threat. Finally, we outline practical defenses against such attacks. Code to reproduce our experiments is available at https://github.com/neulab/RIPPLe.

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