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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2507.03607 |
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| _version_ | 1866918083496509440 |
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| author | Bonhomme, Cédric Dulaunoy, Alexandre |
| author_facet | Bonhomme, Cédric Dulaunoy, Alexandre |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03607 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification Bonhomme, Cédric Dulaunoy, Alexandre Cryptography and Security 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. |
| title | VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2507.03607 |