Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training
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arXiv
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| Format: | Preprint |
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2025
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| author | Yu, Yao-Ching Chiang, Tsun-Han Tsai, Cheng-Wei Huang, Chien-Ming Tsao, Wen-Kwang |
| author_facet | Yu, Yao-Ching Chiang, Tsun-Han Tsai, Cheng-Wei Huang, Chien-Ming Tsao, Wen-Kwang |
| contents | 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, continual 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. For access to all datasets and model weights, please refer to https://huggingface.co/collections/trendmicro-ailab/primus-67b1fd27052b802b4af9d243. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11191 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training Yu, Yao-Ching Chiang, Tsun-Han Tsai, Cheng-Wei Huang, Chien-Ming Tsao, Wen-Kwang Cryptography and Security Artificial Intelligence Computation and Language 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, continual 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. For access to all datasets and model weights, please refer to https://huggingface.co/collections/trendmicro-ailab/primus-67b1fd27052b802b4af9d243. |
| title | Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training |
| topic | Cryptography and Security Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2502.11191 |