Large language models (LLMs) have shown remarkable success across many domains, yet their integration into cybersecurity applications remains limited due to a lack of general-purpose cybersecurity data, representational complexity, and safety and regulatory concerns. To address this gap, we previously introduced Foundation-Sec-8B, a cybersecurity-focused LLM suitable for fine-tuning on downstream tasks. That model, however, was not designed for chat-style interactions or instruction-following. In this report, we release Foundation-Sec-8B-Instruct: a model specifically trained for general-purpose cybersecurity dialogue. Built on Foundation-Sec-8B, it combines domain-specific knowledge with instruction-following, conversational capabilities, and alignment with human preferences to produce high-quality, relevant responses. Comprehensive evaluations show that Foundation-Sec-8B-Instruct outperforms Llama 3.1-8B-Instruct on a range of cybersecurity tasks while matching its instruction-following performance. It is also competitive with GPT-4o-mini on cyber threat intelligence and instruction-following tasks. We envision Foundation-Sec-8B-Instruct becoming an indispensable assistant in the daily workflows of cybersecurity professionals. We release the model publicly at https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Instruct.
@article{arxiv.2508.01059,
title = {Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report},
author = {Sajana Weerawardhena and Paul Kassianik and Blaine Nelson and Baturay Saglam and Anu Vellore and Aman Priyanshu and Supriti Vijay and Massimo Aufiero and Arthur Goldblatt and Fraser Burch and Ed Li and Jianliang He and Dhruv Kedia and Kojin Oshiba and Zhouran Yang and Yaron Singer and Amin Karbasi},
journal= {arXiv preprint arXiv:2508.01059},
year = {2025}
}