  The number of newly published vulnerabilities is constantly increasing. Until
now, the information available when a new vulnerability is published is
manually assessed by experts using a Common Vulnerability Scoring System (CVSS)
vector and score. This assessment is time consuming and requires expertise.
Various works already try to predict CVSS vectors or scores using machine
learning based on the textual descriptions of the vulnerability to enable
faster assessment. However, for this purpose, previous works only use the texts
available in databases such as National Vulnerability Database. With this work,
the publicly available web pages referenced in the National Vulnerability
Database are analyzed and made available as sources of texts through web
scraping. A Deep Learning based method for predicting the CVSS vector is
implemented and evaluated. The present work provides a classification of the
National Vulnerability Database's reference texts based on the suitability and
crawlability of their texts. While we identified the overall influence of the
additional texts is negligible, we outperformed the state-of-the-art with our
Deep Learning prediction models.
