Researchers introduce Sigma-Hunter, a domain-adapted LLM designed to help detection engineers translate threat reports and forensic observations into valid Sigma rules. Unlike general-purpose models that often produce invalid YAML or incorrect logic, this model is fine-tuned on a curated dataset of 3,635 validated open-source rules expanded into over 7,600 examples. The team trained both a 7B Mistral and a Phi model to ensure strict adherence to detection engineering constraints without data leakage across splits.
- Addresses common LLM failures like invalid YAML and unsupported fields in Sigma rules.
- Trained on 7,663 QA examples derived from 3,635 validated open-source rules.
- Ensures no data leakage by partitioning source rules before expansion.
- Available for both Mistral 7B and Phi architectures for flexible deployment.