We present Foundation-Sec-8B-Reasoning, the first open-source native reasoning model for cybersecurity. Built upon our previously released Foundation-Sec-8B base model (derived from Llama-3.1-8B-Base), the model is trained through a two-stage process combining supervised fine-tuning (SFT) and reinforcement learning from verifiable rewards (RLVR). Our training leverages proprietary reasoning data spanning cybersecurity analysis, instruction-following, and mathematical reasoning. Evaluation across 10 cybersecurity benchmarks and 10 general-purpose benchmarks demonstrates performance competitive with significantly larger models on cybersecurity tasks while maintaining strong general capabilities. The model shows effective generalization on multi-hop reasoning tasks and strong safety performance when deployed with appropriate system prompts and guardrails. This work demonstrates that domain-specialized reasoning models can achieve strong performance on specialized tasks while maintaining broad general capabilities. We release the model publicly at https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Reasoning.

翻译：我们提出了Foundation-Sec-8B-Reasoning，这是首个面向网络安全领域的开源原生推理模型。该模型基于我们先前发布的Foundation-Sec-8B基础模型（源自Llama-3.1-8B-Base），通过结合监督微调（SFT）和基于可验证奖励的强化学习（RLVR）的两阶段过程进行训练。我们的训练利用了涵盖网络安全分析、指令遵循和数学推理的专有推理数据。在10个网络安全基准测试和10个通用基准测试上的评估表明，该模型在网络安全任务上的性能可与规模大得多的模型相媲美，同时保持了强大的通用能力。该模型在多跳推理任务上展现出有效的泛化能力，并且在部署适当的系统提示和防护措施时表现出强大的安全性。这项工作表明，领域专用的推理模型可以在保持广泛通用能力的同时，在特定任务上实现强大的性能。我们已在 https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Reasoning 公开发布该模型。