用自然风格设计隐蔽文本后门，让攻击不易被人工发现。

Backdoor attacks on text classifiers can 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, leading to conspicuous attacks. As a result, human annotators, who play a critical role in curating training data in practice, 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 evaluated attack subtlety and invisibility with human involvement. We bridge the gap by conducting thorough human evaluations to assess attack subtlety. We also propose \emph{AttrBkd}, consisting of three recipes for crafting subtle yet 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 inconspicuous 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.