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.
@article{arxiv.2601.21051,
title = {Llama-3.1-FoundationAI-SecurityLLM-Reasoning-8B Technical Report},
author = {Zhuoran Yang and Ed Li and Jianliang He and Aman Priyanshu and Baturay Saglam and Paul Kassianik and Sajana Weerawardhena and Anu Vellore and Blaine Nelson and Neusha Javidnia and Arthur Goldblatt and Fraser Burch and Avi Zohary and Assaf Eisenman and Mahdi Sabbaghi and Supriti Vijay and Rahim Dharssi and Dhruv Kedia and Kojin Oshiba and Yaron Singer and Amin Karbasi},
journal= {arXiv preprint arXiv:2601.21051},
year = {2026}
}