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Backdoor attacks manipulate model predictions by inserting innocuous triggersinto training and test data. We focus on more realistic and more challengingclean-label attacks where the adversarial training examples are correctlylabeled. Our attack, LLMBkd, leverages language models to automatically insertdiverse style-based triggers into texts. We also propose a poison selectiontechnique to improve the effectiveness of both LLMBkd as well as existingtextual backdoor attacks. Lastly, we describe REACT, a baseline defense tomitigate backdoor attacks via antidote training examples. Our evaluationsdemonstrate LLMBkd's effectiveness and efficiency, where we consistentlyachieve high attack success rates across a wide range of styles with littleeffort and no model training.

Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers