@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://preview.aclanthology.org/ingest-emnlp/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."
}