Llama-3.1-FoundationAI-SecurityLLM-Reasoning-8B Technical Report
Authors: name: Zhuoran Yang, name: Ed Li, name: Jianliang He, name: Aman Priyanshu, name: Baturay Saglam, name: Paul Kassianik, name: Sajana Weerawardhena, name: Anu Vellore, name: Blaine Nelson, name: Neusha Javidnia, name: Arthur Goldblatt, name: Fraser Burch, name: Avi Zohary, name: Assaf Eisenman, name: Mahdi Sabbaghi, name: Supriti Vijay, name: Rahim Dharssi, name: Dhruv Kedia, name: Kojin Oshiba, name: Yaron Singer, name: Amin Karbasi
Organization: Cisco Foundation AI

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.