Large Language Models Are Better Adversaries: Exploring Generative
  Clean-Label Backdoor Attacks Against Text Classifiers
Wencong You Zayd Hammoudeh Daniel Lowd
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.