We present XekRung, a frontier large language model for cybersecurity, designed to provide comprehensive security capabilities. To achieve this, we develop diverse data synthesis pipelines tailored to the cybersecurity domain, enabling the scalable construction of high-quality training data and providing a strong foundation for cybersecurity knowledge and understanding. Building on this foundation, we establish a complete training pipeline spanning continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL) to further extend the model's capabilities. We further introduce a multi-dimensional evaluation system to guide the iterative improvement of both domain-specific and general-purpose abilities. Extensive experiments demonstrate that XekRung achieves state-of-the-art performance on cybersecurity-specific benchmarks among models of the same scale, while maintaining strong performance on general benchmarks.
@article{arxiv.2605.00072,
title = {XekRung Technical Report},
author = {Jiutian Zeng and Junjie Li and Chengwei Dai and Jie Liang and Zhaoyu Hu and Yiliang Zhang and Ziang Weng and Longtao Huang and Dongjie Zhang and Libin Dong and Yang Ge and Yuanda Wang and Kaiwen Lv Kacuila and Bingyu Zhu and Jing Wang and Jin Xu},
journal= {arXiv preprint arXiv:2605.00072},
year = {2026}
}
Comments
22 pages, 2 figures, 5 tables. Jiutian Zeng, Junjie Li, Chengwei Dai, Jie Liang, and Zhaoyu Hu contributed equally to this work