The Ultimate Cookbook for Invisible Poison: Crafting Subtle Clean-Label Text Backdoors with Style Attributes

Wencong You, Daniel Lowd

Keywords: adversarial machine learning, natural language processing, backdoor attacks, large language models

TL;DR: This paper explores the use of fine-grained stylistic attributes to craft subtle and effective clean-label text backdoor attacks. Human annotations showcase the superior performance of proposed attack, and scrutinize current evaluating algorithms.

Abstract:

Backdoor attacks on text classifiers cause them to predict a predefined label when a particular "trigger" is present. Prior attacks often rely on triggers that are ungrammatical or otherwise unusual. In practice, human annotators, who play a critical role in curating training data, can easily detect and filter out these unnatural texts during manual inspection, reducing the risk of such attacks. We argue that a key criterion for a successful attack is for text with and without triggers to be indistinguishable to humans. However, prior work neither directly nor comprehensively evaluates attack subtlety and invisibility with human involvement. We bridge the gap by conducting thorough human evaluations to assess attack subtlety. We also propose \textbf{AttrBkd} consisting of three recipes for crafting effective trigger attributes, such as extracting fine-grained attributes from existing baseline backdoor attacks.
Our human evaluations find that AttrBkd with these baseline-derived attributes is often more effective (higher attack success rate) and more subtle (fewer instances detected by humans) than the original baseline backdoor attacks, demonstrating that backdoor attacks can bypass detection by being subtle and appearing natural even upon close inspection, while still remaining effective.
Our human annotation also provides information not captured by automated metrics used in prior work, and demonstrates the misalignment of these metrics with human judgment.

Submission Number: 47