Llama-3.1-FoundationAI-SecurityLLM-Reasoning-8B Technical Report
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.
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Zhuoran Yang
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Ed Li
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2026-02-02T17:00:18.640Z
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Ed Li
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Jianliang He
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2026-02-20T08:38:11.537Z
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Jianliang He
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Aman Priyanshu
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2026-01-30T13:31:54.307Z
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Aman Priyanshu
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AmanPriyanshu
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Baturay Saglam
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Paul Kassianik
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Sajana Weerawardhena
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Anu Vellore
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Blaine Nelson
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Neusha Javidnia
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Arthur Goldblatt
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Fraser Burch
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Avi Zohary
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Assaf Eisenman
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Mahdi Sabbaghi
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Supriti Vijay
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2026-01-30T13:31:56.159Z
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Supriti Vijay
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Rahim Dharssi
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Dhruv Kedia
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Kojin Oshiba
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Yaron Singer
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Amin Karbasi
Llama-3.1-FoundationAI-SecurityLLM-Reasoning-8B Technical Report
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.
A two-stage trained cybersecurity reasoning model achieves competitive performance on specialized tasks while maintaining general capabilities through supervised fine-tuning and reinforcement learning from verifiable rewards.
supervised fine-tuning reinforcement learning from verifiable rewards cybersecurity analysis multi-hop reasoning safety performance