Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers
Wencong You, Zayd Hammoudeh, Daniel Lowd · 2023
Backdoor attacks manipulate model predictions by inserting innocuous triggers into training and test data.We focus on more realistic and more challenging clean-label attacks where the adversarial training examples are correctly labeled.Our attack, LLMBkd, leverages language models to automatically insert diverse style-based triggers into texts.We also propose a poison selection technique to improve the effectiveness of both LLMBkd as well as existing textual backdoor attacks.Lastly, we describe REACT, a baseline defense to mitigate backdoor attacks via antidote training examples.Our evaluations demonstrate LLMBkd's effectiveness and efficiency, where we consistently achieve high attack success rates across a wide range of styles with little effort and no model training.