  Cyber-security vulnerabilities are usually published in form of short natural
language descriptions (e.g., in form of MITRE's CVE list) that over time are
further manually enriched with labels such as those defined by the Common
Vulnerability Scoring System (CVSS). In the Vulnerability AI (Analytics and
Intelligence) project, we investigated different types of semantic
vulnerability embeddings based on natural language processing (NLP) techniques
to obtain a concise representation of the vulnerability space. We also
evaluated their use as a foundation for machine learning applications that can
support cyber-security researchers and analysts in risk assessment and other
related activities. The particular applications we explored and briefly
summarize in this report are clustering, classification, and visualization, as
well as a new logic-based approach to evaluate theories about the vulnerability
space.
