EvoHarmBench - Spam and Flooding: leaderboard

EvoHarmBench (Findings of EMNLP 2026) attacks LLM content moderators with an adaptive DeepSeek-V3.2-Exp loop that evolves human-like rewriting strategies over 12 rounds for clusters of real harmful posts. ASR@Readable in percent counts rewrites that evade the moderator while keeping recognizable harmful intent; lower is better for the moderator. Mean over the spam and flooding clusters.

Metric: ASR@Readable (%; mean over the spam and flooding sub-clusters of the share of adversarial rewrites that succeed against the moderator; 229 semantic sub-clusters built from 5,002 real-world adversarial posts in five violation categories; an adaptive DeepSeek-V3.2-Exp rewriter, reflector and comparison model evolve cluster-level rewriting strategies against the target moderator for 12 rounds; a rewrite counts only if it both evades the moderator and keeps human-recognizable harmful intent). Source: arxiv.org. Saturation forecast: Around 2035. 10 models tracked.

Top models

#ModelScoreOverall rank
1Claude Sonnet 4.661.7#158
2GPT-5.565.8#48
3DeepSeek-V2-Lite72.3#8600
4Gemini 3.1 Pro (Preview)72.4#91
5Kimi K2.681.8#173
6Qwen 3 4B83.6#2297
7Qwen 3 8B85#1838
8Qwen 3.6 Plus86#217
9DeepSeek V4 Pro86.8#181
10GLM-5.191.6#175

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=evoharmbench-spam-and-flooding · How It Works · Data refreshed daily, snapshot 2026-11-12.