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arXiv · 2609.05843

SinoGlyphBench: A Diagnostic Benchmark for Chinese Glyph-Level Obfuscation in Language-Model Moderation

Yifan Wang·Zimu Wang·Suliu Qin·Changyu Zeng·Tong Chen·Siqi Chen·Yijie Lin·Lingyu Jiang·Jionglong Su·Yushan Pan·Haiyang Zhang·Wei Wang·Qiaoyu Tan
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Abstract

Glyph-level obfuscation can leave harmful Chinese content readable to humans while degrading automated moderation. We introduce SinoGlyphBench, a diagnostic benchmark that identifies label-critical semantic anchors and creates matched original and glyph-obfuscated inputs in text and image modalities. By perturbing anchors, background context, or both, this design distinguishes corruption of moderation-relevant evidence from general surface variation. Across 176,916 paired evaluations of 12 LLMs and MLLMs, obfuscation increases harmful false-negative and false-positive rates by 6.1 and 4.7 percentage points, respectively, and reduces four-way accuracy by 5.0 points. Models retain 75.7% of the decisions that were correct on the matched original inputs. Full-scope perturbations cause the largest degradation, anchor-only perturbations are more damaging than background-only perturbations, and cross-script substitution is particularly difficult in the text modality. Analysis of structured outputs identifies observable mismatches in visible-form reading, intended-message recovery, and final safety judgment. The evaluated models, therefore, remain brittle to Chinese content written with non-canonical glyphs. Resources are available at https://github.com/fengshun124/SinoGlyphBench.

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Yifan Wang, Zimu Wang, Suliu Qin, Changyu Zeng, Tong Chen, Siqi Chen, Yijie Lin, Lingyu Jiang, Jionglong Su, Yushan Pan, Haiyang Zhang, Wei Wang, Qiaoyu Tan. 2026-09-05. SinoGlyphBench: A Diagnostic Benchmark for Chinese Glyph-Level Obfuscation in Language-Model Moderation. https://arxiv.org/abs/2609.05843

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