ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908370886197248 |
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| author | Chopra, Shivansh Ahmad, Hussain Goel, Diksha Szabo, Claudia |
| author_facet | Chopra, Shivansh Ahmad, Hussain Goel, Diksha Szabo, Claudia |
| contents | The increasing frequency and sophistication of cybersecurity vulnerabilities in software systems underscores the need for more robust and effective vulnerability assessment methods. However, existing approaches often rely on highly technical and abstract frameworks, which hinder understanding and increase the likelihood of exploitation, resulting in severe cyberattacks. In this paper, we introduce ChatNVD, a support tool powered by Large Language Models (LLMs) that leverages the National Vulnerability Database (NVD) to generate accessible, context-rich summaries of software vulnerabilities. We develop three variants of ChatNVD, utilizing three prominent LLMs: GPT-4o Mini by OpenAI, LLaMA 3 by Meta, and Gemini 1.5 Pro by Google. To evaluate their performance, we conduct a comparative evaluation focused on their ability to identify, interpret, and explain software vulnerabilities. Our results demonstrate that GPT-4o Mini outperforms the other models, achieving over 92% accuracy and the lowest error rates, making it the most reliable option for real-world vulnerability assessment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04756 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models Chopra, Shivansh Ahmad, Hussain Goel, Diksha Szabo, Claudia Cryptography and Security Computation and Language The increasing frequency and sophistication of cybersecurity vulnerabilities in software systems underscores the need for more robust and effective vulnerability assessment methods. However, existing approaches often rely on highly technical and abstract frameworks, which hinder understanding and increase the likelihood of exploitation, resulting in severe cyberattacks. In this paper, we introduce ChatNVD, a support tool powered by Large Language Models (LLMs) that leverages the National Vulnerability Database (NVD) to generate accessible, context-rich summaries of software vulnerabilities. We develop three variants of ChatNVD, utilizing three prominent LLMs: GPT-4o Mini by OpenAI, LLaMA 3 by Meta, and Gemini 1.5 Pro by Google. To evaluate their performance, we conduct a comparative evaluation focused on their ability to identify, interpret, and explain software vulnerabilities. Our results demonstrate that GPT-4o Mini outperforms the other models, achieving over 92% accuracy and the lowest error rates, making it the most reliable option for real-world vulnerability assessment. |
| title | ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models |
| topic | Cryptography and Security Computation and Language |
| url | https://arxiv.org/abs/2412.04756 |