vulnerability-severity-classification-roberta-base

The model was presented in the paper VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification [arXiv]. **Abstract:** VLAI is a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service. > **Note (October 2026):** since VulnTrain 3.4.0, > the dataset is split so that records of the same vulnerability (for example a CVE and its > GitHub advisory) never appear in both train and test. Accuracy measured this way (**74%**) > reflects performance on vulnerabilities the model has never seen. Earlier figures, including > the one in the abstract above, were measured on a split where many test descriptions also > appeared in training. Details in the technical report > Improving vulnerability-scores: Leakage-Free Splits and Consistent CVSS 4.0 Scoring.