用大模型自动评估医疗设备漏洞，提升安全响应效率
CVE-LLM : Ontology-Assisted Automatic Vulnerability Evaluation Using Large Language Models
- 结合本体知识增强历史数据，让大模型理解新漏洞无需重训
- 已集成至西门子医疗内部系统，支持实际产品漏洞评估
- 为工业场景大模型落地提供可复用的整合指南
国家漏洞数据库（NVD）每月发布上千个新漏洞，预计2024年将增长25%，亟需快速识别漏洞以减轻网络攻击影响并节省成本。本文提出利用大语言模型（LLM）从单一厂商医疗设备的历史漏洞评估中学习漏洞评估能力。研究揭示了使用LLM进行自动漏洞评估的有效性与挑战，并引入一种方法，通过网络安全本体丰富历史数据，使系统在不重新训练LLM的情况下理解新漏洞。该LLM系统已与西门子健康科技（SHS）自研的网络安全管理系统（CSMS）集成，协助其产品安全专家高效评估产品漏洞。同时，本文还提供了大模型高效融入网络安全工具的实践指南。
原文摘要 · Abstract (English)
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 guidelines for efficient integration of LLMs into the cybersecurity tool.
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