Benchmarking Open-Source Safety Guard Models: A Comprehensive Evaluation
Provides practical guidance for practitioners selecting safety guard models for LLM deployment, revealing that smaller general-purpose models can outperform larger specialized ones.
Evaluated 14 open-source safety guard models on a 79,331-sample benchmark across 8 safety categories, finding that Qwen Guard (4B) achieves the highest recall (83.97%) while larger models like Llama Guard (12B) miss up to 75% of unsafe content, and model size does not correlate with safety detection performance.
As Large Language Models (LLMs) are increasingly deployed in safety-critical applications, robust content moderation becomes essential. We present a comprehensive evaluation of 14 open-source safety guard models on a curated benchmark of 79,331 samples spanning 8 NIST AI Risk Framework safety categories. Our benchmark aggregates four diverse datasets (HarmBench, StrongREJECT, RealToxicityPrompts, and BeaverTails), filtered to focus exclusively on safety-relevant content (violence, hate speech, harassment, sexual content, suicide/self-harm, profanity, threats, and health misinformation). We find that recall is the critical metric for safety applications, as missing harmful content poses greater risk than false positives. Our evaluation reveals surprising results: Qwen Guard (4B parameters) achieves the highest recall (83.97%) while larger models like Llama Guard (12B) and GPT-OSS Safeguard (20B) exhibit conservative behavior, missing up to 75% of unsafe content. We demonstrate that model size does not correlate with safety detection performance and that general-purpose guard models outperform specialized ones. These findings provide practical guidance for selecting safety guard models in production deployments.