Benchmarking LLM for Zero-day Vulnerabilities

被引：0

作者：

Lisha, M. ^{[1
]}

Agarwal, Vedika ^{[2
]}

Kamthania, Supriya ^{[2
]}

Vutkur, Pranav ^{[1
]}

Chari, Madhusoodhana S. ^{[3
]}

机构：

[1] Hewlett Packard Enterprise, Cloud Modules, Bengaluru, India

[2] Hewlett Packard Enterprise, Ezmeral R&D, Bengaluru, India

[3] Hewlett Packard Enterprise, Aruba Switching SW, Bengaluru, India

来源：

10TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTING AND COMMUNICATION TECHNOLOGIES, CONECCT 2024 | 2024年

关键词：

security; LLM; zero-day; vulnerability;

D O I：

10.1109/CONECCT62155.2024.10677338

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Large Language Models (LLMs) have emerged as powerful tools for natural language processing tasks. This paper investigates the efficacy of various LLMs in detecting zero-day vulnerabilities, crucial for preemptive cybersecurity measures. Through benchmarking and experimentation, we analyze the performance of LLMs in unstructured querying scenarios. Our findings aim to enhance understanding of LLM capabilities and contribute to the advancement of vulnerability detection methodologies.

引用

页数：6

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