CS-Eval: A Comprehensive Large Language Model Benchmark for CyberSecurity

• URL: http://arxiv.org/abs/2411.16239v3
• Date: Fri, 17 Jan 2025 04:19:43 GMT
• Title: CS-Eval: A Comprehensive Large Language Model Benchmark for CyberSecurity
• Authors: Zhengmin Yu, Jiutian Zeng, Siyi Chen, Wenhan Xu, Dandan Xu, Xiangyu Liu, Zonghao Ying, Nan Wang, Yuan Zhang, Min Yang,
• Abstract summary: CS-Eval is a benchmark for large language models (LLMs) in cybersecurity. It synthesizes research hotspots from academia and practical applications from industry. It organizes high-quality questions into three cognitive levels: knowledge, ability, and application.
• Score: 25.07282324266835
• License:
• Abstract: Over the past year, there has been a notable rise in the use of large language models (LLMs) for academic research and industrial practices within the cybersecurity field. However, it remains a lack of comprehensive and publicly accessible benchmarks to evaluate the performance of LLMs on cybersecurity tasks. To address this gap, we introduce CS-Eval, a publicly accessible, comprehensive and bilingual LLM benchmark specifically designed for cybersecurity. CS-Eval synthesizes the research hotspots from academia and practical applications from industry, curating a diverse set of high-quality questions across 42 categories within cybersecurity, systematically organized into three cognitive levels: knowledge, ability, and application. Through an extensive evaluation of a wide range of LLMs using CS-Eval, we have uncovered valuable insights. For instance, while GPT-4 generally excels overall, other models may outperform it in certain specific subcategories. Additionally, by conducting evaluations over several months, we observed significant improvements in many LLMs' abilities to solve cybersecurity tasks. The benchmarks are now publicly available at https://github.com/CS-EVAL/CS-Eval.

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