Enhancing Vulnerability Reports with Automated and Augmented Description Summarization
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866918004203192320 |
|---|---|
| author | Althebeiti, Hattan Alkinoon, Mohammed Mohaisen, Manar Salem, Saeed Nyang, DaeHun Mohaisen, David |
| author_facet | Althebeiti, Hattan Alkinoon, Mohammed Mohaisen, Manar Salem, Saeed Nyang, DaeHun Mohaisen, David |
| contents | Public vulnerability databases, such as the National Vulnerability Database (NVD), document vulnerabilities and facilitate threat information sharing. However, they often suffer from short descriptions and outdated or insufficient information. In this paper, we introduce Zad, a system designed to enrich NVD vulnerability descriptions by leveraging external resources. Zad consists of two pipelines: one collects and filters supplementary data using two encoders to build a detailed dataset, while the other fine-tunes a pre-trained model on this dataset to generate enriched descriptions. By addressing brevity and improving content quality, Zad produces more comprehensive and cohesive vulnerability descriptions. We evaluate Zad using standard summarization metrics and human assessments, demonstrating its effectiveness in enhancing vulnerability information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20726 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Vulnerability Reports with Automated and Augmented Description Summarization Althebeiti, Hattan Alkinoon, Mohammed Mohaisen, Manar Salem, Saeed Nyang, DaeHun Mohaisen, David Cryptography and Security Artificial Intelligence Machine Learning Public vulnerability databases, such as the National Vulnerability Database (NVD), document vulnerabilities and facilitate threat information sharing. However, they often suffer from short descriptions and outdated or insufficient information. In this paper, we introduce Zad, a system designed to enrich NVD vulnerability descriptions by leveraging external resources. Zad consists of two pipelines: one collects and filters supplementary data using two encoders to build a detailed dataset, while the other fine-tunes a pre-trained model on this dataset to generate enriched descriptions. By addressing brevity and improving content quality, Zad produces more comprehensive and cohesive vulnerability descriptions. We evaluate Zad using standard summarization metrics and human assessments, demonstrating its effectiveness in enhancing vulnerability information. |
| title | Enhancing Vulnerability Reports with Automated and Augmented Description Summarization |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.20726 |