XekRung是面向网络安全的前沿大模型，具备全面安全能力。
XekRung Technical Report
- 定制化数据合成流水线构建高质量训练数据
- 在同规模模型中达顶尖网络安全性能，通用能力也强
- 适合安全研究与工业界部署，支持迭代优化
我们提出XekRung，一款面向网络安全领域的前沿大语言模型，旨在提供全面的安全能力。为此，我们开发了针对网络安全领域的多样化数据合成管道，实现高质量训练数据的规模化构建，为网络安全知识与理解奠定坚实基础。在此基础上，我们建立涵盖持续预训练（CPT）、监督微调（SFT）和强化学习（RL）的完整训练流程，进一步拓展模型能力。我们还引入多维度评估体系，指导领域特定与通用能力的迭代提升。大量实验表明，XekRung在同等规模模型中于网络安全基准上达到当前最优表现，同时在通用基准上保持强劲性能。
原文摘要 · Abstract (English)
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.
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