Enhancing Vulnerability Reports with Automated and Augmented Description Summarization
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Abstract (arXiv)
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 However, they often suffer from short descriptions and outdated or insufficient information.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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 49 references that are in the map. First posted on arXiv 29 Apr 2025.
Builds on
21 of 49 references.
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