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VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification
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VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification
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This paper presents VLAI, 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.
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Cited by 1 Pith paper
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Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion
A classifier trained on 1,207 expert-mapped CVEs roughly doubles recall@5 over a zero-shot baseline, while LLM-generated labels at ~0.39 expert agreement provide no reliable gain and degrade rare-technique coverage at...
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