@inproceedings{yu-etal-2025-primus,
title = "Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity {LLM} Training",
author = "Yu, Yao-Ching and
Chiang, Tsun-Han and
Tsai, Cheng-Wei and
Huang, Chien-Ming and
Tsao, Wen-Kwang",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.527/",
pages = "10402--10424",
ISBN = "979-8-89176-332-6",
abstract = "Large Language Models (LLMs) have shown remarkable advancements in specialized fields such as finance, law, and medicine. However, in cybersecurity, we have noticed a lack of open-source datasets, with a particular lack of high-quality cybersecurity pretraining corpora, even though much research indicates that LLMs acquire their knowledge during pretraining. To address this, we present a comprehensive suite of datasets covering all major training stages, including pretraining, instruction fine-tuning, and reasoning distillation with cybersecurity-specific self-reflection data. Extensive ablation studies demonstrate their effectiveness on public cybersecurity benchmarks. In particular, continued pre-training on our dataset yields a **15.9{\%}** improvement in the aggregate score, while reasoning distillation leads to a **15.8{\%}** gain in security certification (CISSP). We will release all datasets and trained cybersecurity LLMs under the ODC-BY and MIT licenses to encourage further research in the community."
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<abstract>Large Language Models (LLMs) have shown remarkable advancements in specialized fields such as finance, law, and medicine. However, in cybersecurity, we have noticed a lack of open-source datasets, with a particular lack of high-quality cybersecurity pretraining corpora, even though much research indicates that LLMs acquire their knowledge during pretraining. To address this, we present a comprehensive suite of datasets covering all major training stages, including pretraining, instruction fine-tuning, and reasoning distillation with cybersecurity-specific self-reflection data. Extensive ablation studies demonstrate their effectiveness on public cybersecurity benchmarks. In particular, continued pre-training on our dataset yields a **15.9%** improvement in the aggregate score, while reasoning distillation leads to a **15.8%** gain in security certification (CISSP). We will release all datasets and trained cybersecurity LLMs under the ODC-BY and MIT licenses to encourage further research in the community.</abstract>
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%0 Conference Proceedings
%T Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training
%A Yu, Yao-Ching
%A Chiang, Tsun-Han
%A Tsai, Cheng-Wei
%A Huang, Chien-Ming
%A Tsao, Wen-Kwang
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F yu-etal-2025-primus
%X Large Language Models (LLMs) have shown remarkable advancements in specialized fields such as finance, law, and medicine. However, in cybersecurity, we have noticed a lack of open-source datasets, with a particular lack of high-quality cybersecurity pretraining corpora, even though much research indicates that LLMs acquire their knowledge during pretraining. To address this, we present a comprehensive suite of datasets covering all major training stages, including pretraining, instruction fine-tuning, and reasoning distillation with cybersecurity-specific self-reflection data. Extensive ablation studies demonstrate their effectiveness on public cybersecurity benchmarks. In particular, continued pre-training on our dataset yields a **15.9%** improvement in the aggregate score, while reasoning distillation leads to a **15.8%** gain in security certification (CISSP). We will release all datasets and trained cybersecurity LLMs under the ODC-BY and MIT licenses to encourage further research in the community.
%U https://aclanthology.org/2025.emnlp-main.527/
%P 10402-10424
Markdown (Informal)
[Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training](https://aclanthology.org/2025.emnlp-main.527/) (Yu et al., EMNLP 2025)
ACL