VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity
  Classification
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.
686cb574364e2ad167eb54c3
Cédric Bonhomme
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2025-07-08T08:05:41.165Z
65e5bc754230174d547fa1dc
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Cédric
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cedricbonhomme
686cb574364e2ad167eb54c4
Alexandre Dulaunoy
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2025-07-08T08:39:37.700Z
677d08a57038d6b09078649a
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Dulaunoy
user
adulau
VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity
  Classification
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.
A transformer-based model fine-tuned on RoBERTa predicts software vulnerability severity levels from text descriptions, achieving high accuracy and streamlining triage.
transformer-based model RoBERTa fine-tuned software vulnerability severity levels text descriptions CVSS scoring