Detecting hate speech and offensive language is essential for maintaining a
safe and respectful digital environment. This study examines the limitations of
state-of-the-art large language models (LLMs) in identifying offensive content
within systematically perturbed data, with a focus on Chinese, a language
particularly susceptible to such perturbations. We introduce
\textsf{ToxiCloakCN}, an enhanced dataset derived from ToxiCN, augmented with
homophonic substitutions and emoji transformations, to test the robustness of
LLMs against these cloaking perturbations. Our findings reveal that existing
models significantly underperform in detecting offensive content when these
perturbations are applied. We provide an in-depth analysis of how different
types of offensive content are affected by these perturbations and explore the
alignment between human and model explanations of offensiveness. Our work
highlights the urgent need for more advanced techniques in offensive language
detection to combat the evolving tactics used to evade detection mechanisms.

通过改进的数据集在汉语中测试大型语言模型（LLMs）对于侮辱性内容的检测，研究表明现有模型在存在这些干扰时检测侮辱性内容的性能明显不足，强调了对进阶技术的迫切需求以应对逃避检测机制的不断演变的策略。