  Textual backdoor attacks pose a practical threat to existing systems, as they
can compromise the model by inserting imperceptible triggers into inputs and
manipulating labels in the training dataset. With cutting-edge generative
models such as GPT-4 pushing rewriting to extraordinary levels, such attacks
are becoming even harder to detect. We conduct a comprehensive investigation of
the role of black-box generative models as a backdoor attack tool, highlighting
the importance of researching relative defense strategies. In this paper, we
reveal that the proposed generative model-based attack, BGMAttack, could
effectively deceive textual classifiers. Compared with the traditional attack
methods, BGMAttack makes the backdoor trigger less conspicuous by leveraging
state-of-the-art generative models. Our extensive evaluation of attack
effectiveness across five datasets, complemented by three distinct human
cognition assessments, reveals that Figure 4 achieves comparable attack
performance while maintaining superior stealthiness relative to baseline
methods.
