Automated vulnerability evaluation with large language models and vulnerability ontologies

Abstract

Abstract The National Vulnerability Database (NVD) publishes over a thousand new vulnerabilities monthly, with a projected 25 percent increase in 2024, highlighting the crucial need for rapid vulnerability identification to mitigate cybersecurity attacks and save costs and resources. In this work, we propose using large language models (LLMs) to learn vulnerability evaluation from historical assessments of medical device vulnerabilities in a single manufacturer's portfolio. We highlight the effectiveness and challenges of using LLMs for automatic vulnerability evaluation and introduce a method to enrich historical data with cybersecurity ontologies, enabling the system to understand new vulnerabilities without retraining the LLM. Our LLM system integrates with the in‐house application—Cybersecurity Management System (CSMS)—to help Siemens Healthineers (SHS) product cybersecurity experts efficiently assess the vulnerabilities in our products. Also, we present a comprehensive set of experiments that helps showcase the properties of the LLM and dataset, the various guardrails we have implemented to safeguard the system in production, and the guidelines for efficient integration of LLMs into the cybersecurity tool.

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