Benchmarking Open-Source Safety Guard Models: A Comprehensive Evaluation
A comprehensive evaluation of 14 open-source safety guard models on a benchmark of 79,331 samples reveals that Qwen Guard (4B parameters) achieves the highest recall (83.97%), while larger models like Llama Guard (12B) miss up to 75% of unsafe content. Model size does not correlate with safety performance, and general-purpose guard models outperform specialized ones.
Article intelligence
Key points
- Qwen Guard (4B parameters) achieves the highest recall (83.97%) among 14 open-source safety guard models.
- Larger models like Llama Guard (12B) and GPT-OSS Safeguard (20B) exhibit conservative behavior, missing up to 75% of unsafe content.
- Model size does not correlate with safety detection performance; general-purpose guard models outperform specialized ones.
- Recall is the critical metric for safety applications as missing harmful content poses greater risk than false positives.
Why it matters
This matters because qwen Guard (4B parameters) achieves the highest recall (83.97%) among 14 open-source safety guard models.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.28830] Benchmarking Open-Source Safety Guard Models: A Comprehensive Evaluation
[Submitted on 10 Apr 2026]
Title:Benchmarking Open-Source Safety Guard Models: A Comprehensive Evaluation
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Abstract: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.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
Cite as: arXiv:2605.28830 [cs.CL]
(or arXiv:2605.28830v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.28830
arXiv-issued DOI via DataCite
Submission history
From: Bhaskarjit Sarmah [view email] [v1] Fri, 10 Apr 2026 06:55:07 UTC (309 KB)
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