Benchmark Almanac

CTIBench

Not in model cards

Models answer cyber threat intelligence questions. Tasks cover knowledge questions, CVE to CWE mapping, CVSS severity scoring, threat actor attribution and technique extraction.

Five task sets scored separately, listed as NeurIPS 2024 spotlight by the maintained repo. Dataset is CC BY-NC-SA 4.0 on Hugging Face, so reuse is non-commercial. arXiv v3 dated 11 November 2024.

In the authors’ words

Cyber threat intelligence (CTI) is crucial in today's cybersecurity landscape, providing essential insights to understand and mitigate the ever-evolving cyber threats. The recent rise of Large Language Models (LLMs) have shown potential in this domain, but concerns about their reliability, accuracy, and hallucinations persist. While existing benchmarks provide general evaluations of LLMs, there are no benchmarks that address the practical and applied aspects of CTI-specific tasks. To bridge this gap, we introduce CTIBench, a benchmark designed to assess LLMs' performance in CTI applications. CTIBench includes multiple datasets focused on evaluating knowledge acquired by LLMs in the cyber-threat landscape. Our evaluation of several state-of-the-art models on these tasks provides insights into their strengths and weaknesses in CTI contexts, contributing to a better understanding of LLM capabilities in CTI.

Abstract of CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat Intelligence (arXiv:2406.07599, CC0)
Made by Rochester Institute of Technology (Alam, Bhusal, Nguyen, Rastogi) Released Jun 2024 Finding and exploiting real bugs All tasks public
Level
Expert Specialists with years of training, often over hours of work
Scored
Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
Answers
Short answer A number, word or short string matched to a key.

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We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.

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