{"slug": "no-one-model-catches-every-harm-benchmarking-content-moderation-across-safety", "title": "No One Model Catches Every Harm: Benchmarking Content Moderation Across Safety Scenarios", "summary": "A new arXiv study testing 53 large language models across 11 datasets in four safety categories found that no single model excels at all types of harmful content, with top frontier models lagging behind smaller specialized alternatives in some areas and conversational safety remaining unsolved. The findings challenge the assumption that scale ensures safety and provide a framework for model selection.", "body_md": "arXiv:2608.21775v1 Announce Type: new\nAbstract: Large Language Models (LLMs) are increasingly deployed in real-world applications, yet they remain vulnerable to generating harmful content. From adversarial jailbreaks that bypass safety filters to implicit hate that evades detection, the range of risks these models pose continues to grow. While both specialized content moderators and general-purpose LLMs are being used as safety layers, the question of which model is best suited for which type of harmful content remains unanswered. We present the most comprehensive evaluation of LLM safety capabilities to date, systematically testing \\textbf{53} models across \\textbf{11} datasets that we organize into four distinct categories. Our evaluation under both prompt-only and prompt-response settings uncovers critical blind spots: large frontier models that lead on one category fall significantly behind smaller, specialized alternatives on others, and real-world conversational safety remains largely unsolved across all model families. These findings challenge the assumption that scale alone ensures safety, and provide the community with a structured framework for informed model selection.", "url": "https://wpnews.pro/news/no-one-model-catches-every-harm-benchmarking-content-moderation-across-safety", "canonical_source": "https://arxiv.org/abs/2608.21775", "published_at": "2026-08-25 04:00:00+00:00", "updated_at": "2026-08-25 04:15:34.173302+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-safety", "ai-research"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/no-one-model-catches-every-harm-benchmarking-content-moderation-across-safety", "markdown": "https://wpnews.pro/news/no-one-model-catches-every-harm-benchmarking-content-moderation-across-safety.md", "text": "https://wpnews.pro/news/no-one-model-catches-every-harm-benchmarking-content-moderation-across-safety.txt", "jsonld": "https://wpnews.pro/news/no-one-model-catches-every-harm-benchmarking-content-moderation-across-safety.jsonld"}}