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 However, they often suffer from short descriptions and outdated or insufficient information.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.

本研究解决了公共漏洞数据库中描述简短和信息不足的问题，提出了一种名为Zad的系统，通过外部资源丰富漏洞描述。Zad的两条处理管道不仅收集和过滤补充数据，还对预训练模型进行微调，以生成更全面、连贯的漏洞描述，显著提升了漏洞信息的质量及其实用性。

通过自动化和增强描述摘要提升漏洞报告