HaloGuard 1.0: An Open Weights Constitutional Classifier for Multilingual AI Safety
A smaller AI safety filter that catches harmful requests across 46 languages
Researchers built HaloGuard, a safety classifier that blocks harmful prompts to AI systems while being 30 times smaller than competing models and matching their performance. The system uses a structured set of 46 safety policies translated across languages, then trains on thousands of paired examples where only the harmful intent changes—not the topic or wording—to learn what's truly dangerous rather than just flagging unfamiliar phrasing.
AI companies need safety filters that work in many languages without slowing down their systems. HaloGuard runs efficiently on smaller hardware while catching 90% of harmful requests with only a 4% false-alarm rate, making it practical for real-world deployment. Because it's released openly, smaller companies and researchers outside tech giants can now afford to build safer multilingual AI systems.