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

Abstract: Over the past year, there has been a notable rise in the use of 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.

• The paper introduces CS-Eval, a novel benchmark tailored to assess cybersecurity proficiency in LLMs with 4,369 expert-curated questions spanning three cognitive dimensions.
• It employs a robust methodology that integrates 42 categories to evaluate models like GPT-4 and Qwen2-72B-Instruct on tasks including vulnerability management and threat detection.
• Results highlight that high-quality data and efficient design patterns, such as MoE, significantly enhance model performance in complex cybersecurity scenarios.

Evaluating the Cybersecurity Proficiency of LLMs: An Analysis of CS-Eval

In the domain of cybersecurity, the integration of LLMs has introduced innovative pathways for problem-solving and automation. The paper "CS-Eval: A Comprehensive LLM Benchmark for CyberSecurity" presents an academically rigorous attempt to establish a benchmark named CS-Eval, specifically tailored to evaluate the capabilities of LLMs in cybersecurity. This benchmark emerges in response to the identified need for a specialized and comprehensive evaluation framework, given the absence of robust, publicly available benchmarks in this field.

Structure and Methodology of CS-Eval

CS-Eval thoughtfully synthesizes the current research hotspots and practical applications in cybersecurity by crafting a large dataset encompassing 42 categories. These span three cognitive dimensions: knowledge, ability, and application. The benchmark evaluates LLMs across various tasks, including vulnerability management, threat detection, and data security, offering a nuanced view of their strengths and weaknesses.

The dataset construction involved a meticulous process of data collection, validation, and dynamic generation. This ensures not only high-quality evaluation but also adaptability against factors such as data contamination. The dataset comprises 4,369 questions, curated with input from domain experts, and is continually updated to reflect changes in both data and model performance.

Key Insights and Experimental Findings

The comprehensive evaluation of various LLMs using CS-Eval revealed several invaluable insights:

1. Performance of Models: The benchmark identifies GPT-4 8K as the leading LLM in terms of general cybersecurity proficiency, underscoring its robust performance across a wide array of tasks. Interestingly, the Qwen2-72B-Instruct model demonstrated competitive performance, particularly in specialized domains such as threat detection and prevention.
2. Impact of Data Quality: The benchmark highlights the critical importance of data quality in training LLMs. For instance, models that underwent fine-tuning with higher-quality datasets outperform those using less rigorous datasets, as exemplified by Qwen-Math's excellence in its domain.
3. Parameter Size and Model Efficiency: The performance trends observed indicate that while larger models generally exhibit superior differentiation in fine-grained tasks, smaller models can achieve competitive results with efficient design patterns such as MoE (Mixture of Experts).
4. Temporal Evolution: Over a period of several months, significant improvements were noted in model performance, signifying the effects of enhanced data and training quality in recent iterations.

Theoretical and Practical Implications

The CS-Eval benchmark sets a precedent for the development and evaluation of domain-specific LLMs. By providing a rigorous assessment framework, it facilitates a more accurate understanding of both the current capabilities and the limitations of LLMs in tackling cybersecurity tasks. The paper underscores the importance of refining data synthesis and model training strategies, encouraging future research to explore automated data collection methods and the application of agent-based evaluation environments for complex cybersecurity scenarios.

Considerations and Future Directions

While CS-Eval represents a significant advancement, the benchmark's reliance on manual data collection and static question formats highlights an opportunity for refinement. The expansion into niche areas such as kernel exploitation and the incorporation of code security assessments would enhance its comprehensiveness. Additionally, leveraging dynamic and executable environments for evaluations could more closely simulate real-world cybersecurity challenges, thus offering greater ecological validity.

In conclusion, CS-Eval provides a foundational tool for assessing LLMs in cybersecurity, offering insights that drive future developments in model training and implementation strategies. As the landscape of cybersecurity evolves, benchmarks like CS-Eval will be instrumental in shaping the alignment of LLMs with industry needs and academic advancements.