Using Retriever Augmented Large Language Models for Attack Graph Generation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909284606935040 |
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| author | Prapty, Renascence Tarafder Kundu, Ashish Iyengar, Arun |
| author_facet | Prapty, Renascence Tarafder Kundu, Ashish Iyengar, Arun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05855 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using Retriever Augmented Large Language Models for Attack Graph Generation Prapty, Renascence Tarafder Kundu, Ashish Iyengar, Arun Cryptography and Security Machine Learning 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. |
| title | Using Retriever Augmented Large Language Models for Attack Graph Generation |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2408.05855 |