利用检索器增强的大语言模型生成攻击图
Using Retriever Augmented Large Language Models for Attack Graph Generation
摘要：
随着现代系统复杂性的增加，通过有效的漏洞管理和威胁建模技术评估其安全态势的重要性也在增加。网络安全专业人员武器库中的一个强大工具是攻击图，它代表了系统中所有潜在的攻击路径，对手可能会利用这些路径来实现特定目标。生成攻击图的传统方法涉及专家知识、手动管理和计算算法，由于漏洞和漏洞利用的不断演变的性质，这些方法可能无法覆盖整个威胁环境。本文探讨了利用大语言模型（LLM）（如ChatGPT）自动生成攻击图的方法，方法是根据攻击图的前提条件和影响智能地链接常见漏洞和暴露（CVE）。它还展示了如何利用LLM从威胁报告中创建攻击图。
英文摘要：
As the complexity of modern systems increases, so does the importance of assessing their security posture through effective vulnerability management and threat modeling techniques. One powerful tool in the arsenal of cybersecurity professionals is the attack graph, a representation of all potential attack paths within a system that an adversary might exploit to achieve a certain objective. Traditional methods of generating attack graphs involve expert knowledge, manual curation, and computational algorithms that might not cover the entire threat landscape due to the ever-evolving nature of vulnerabilities and exploits. This paper explores the approach of leveraging large language models (LLMs), such as ChatGPT, to automate the generation of attack graphs by intelligently chaining Common Vulnerabilities and Exposures (CVEs) based on their preconditions and effects. It also shows how to utilize LLMs to create attack graphs from threat reports.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
