@inproceedings{jian-etal-2026-evoharmbench,
title = "{E}vo{H}arm{B}ench: Breaking Content Moderation with Iterative Human-Like Evasion",
author = "Jian, Ruijie and
Cui, Benlei and
Ma, Ting and
Ding, Haidong and
Liu, Kangwei and
Xu, Ziwen and
Huang, Longtao and
Xue, Hui and
Zhu, Ziqiang and
Li, Junjie and
Hong, Haiwen",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.288/",
pages = "6106--6129",
ISBN = "979-8-89176-462-0",
abstract = "Existing evaluations of harmful content detection rely predominantly on static benchmarks, which struggle to reflect the interactive adversarial ecosystem of real-world content platforms where users continuously revise their expressions in response to moderation feedback. This mismatch creates a significant performance gap between offline benchmark scores and online deployment effectiveness. To the best of our knowledge, we present EvoHarmBench, the first dynamic adversarial evaluation framework for content moderation systems. The framework employs an iterative optimization loop that evolves evasion strategies at the semantic-cluster level, while simultaneously optimizing for evasion success and human readability. We systematically evaluate LLM-based defense models which are widely used in real world moderation systems. The evaluation covers 229 semantic sub-clusters across five violation categories, derived from 5,002 real-world adversarial samples collected from content platforms. Our experiments reveal substantial vulnerabilities even in leading commercial systems: after twelve optimization iterations, Upon acceptance, we will release the full benchmark data, evaluation framework, and code to encourage a shift from static benchmarking toward dynamic adversarial evaluation in content safety research."
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%0 Conference Proceedings
%T EvoHarmBench: Breaking Content Moderation with Iterative Human-Like Evasion
%A Jian, Ruijie
%A Cui, Benlei
%A Ma, Ting
%A Ding, Haidong
%A Liu, Kangwei
%A Xu, Ziwen
%A Huang, Longtao
%A Xue, Hui
%A Zhu, Ziqiang
%A Li, Junjie
%A Hong, Haiwen
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F jian-etal-2026-evoharmbench
%X Existing evaluations of harmful content detection rely predominantly on static benchmarks, which struggle to reflect the interactive adversarial ecosystem of real-world content platforms where users continuously revise their expressions in response to moderation feedback. This mismatch creates a significant performance gap between offline benchmark scores and online deployment effectiveness. To the best of our knowledge, we present EvoHarmBench, the first dynamic adversarial evaluation framework for content moderation systems. The framework employs an iterative optimization loop that evolves evasion strategies at the semantic-cluster level, while simultaneously optimizing for evasion success and human readability. We systematically evaluate LLM-based defense models which are widely used in real world moderation systems. The evaluation covers 229 semantic sub-clusters across five violation categories, derived from 5,002 real-world adversarial samples collected from content platforms. Our experiments reveal substantial vulnerabilities even in leading commercial systems: after twelve optimization iterations, Upon acceptance, we will release the full benchmark data, evaluation framework, and code to encourage a shift from static benchmarking toward dynamic adversarial evaluation in content safety research.
%U https://aclanthology.org/2026.findings-emnlp.288/
%P 6106-6129
Markdown (Informal)
[EvoHarmBench: Breaking Content Moderation with Iterative Human-Like Evasion](https://aclanthology.org/2026.findings-emnlp.288/) (Jian et al., Findings 2026)
ACL
- Ruijie Jian, Benlei Cui, Ting Ma, Haidong Ding, Kangwei Liu, Ziwen Xu, Longtao Huang, Hui Xue, Ziqiang Zhu, Junjie Li, and Haiwen Hong. 2026. EvoHarmBench: Breaking Content Moderation with Iterative Human-Like Evasion. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 6106–6129, Budapest, Hungary. Association for Computational Linguistics.