Stumbling Blocks: Stress Testing the Robustness of Machine-Generated
  Text Detectors Under Attacks
Yichen Wang Shangbin Feng Abe Bohan Hou Xiao Pu Chao Shen Xiaoming Liu Yulia Tsvetkov Tianxing He
The widespread use of large language models (LLMs) is increasing the demand
for methods that detect machine-generated text to prevent misuse. The goal of
our study is to stress test the detectors' robustness to malicious attacks
under realistic scenarios. We comprehensively study the robustness of popular
machine-generated text detectors under attacks from diverse categories:
editing, paraphrasing, prompting, and co-generating. Our attacks assume limited
access to the generator LLMs, and we compare the performance of detectors on
different attacks under different budget levels. Our experiments reveal that
almost none of the existing detectors remain robust under all the attacks, and
all detectors exhibit different loopholes. Averaging all detectors, the
performance drops by 35% across all attacks. Further, we investigate the
reasons behind these defects and propose initial out-of-the-box patches to
improve robustness.