Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report

2025-08-01

AI Summary

Current large language models (LLMs) face significant limitations in cybersecurity applications due to insufficient high-quality domain-specific data, the inherent complexity of security representations, and stringent regulatory compliance constraints. Moreover, existing specialized models—such as Foundation-Sec-8B—lack native support for interactive dialogue and instruction following. To address these gaps, we propose the first instruction-tuned model explicitly designed for general-purpose cybersecurity dialogue. Built upon Foundation-Sec-8B, our approach integrates domain-knowledge injection, multi-turn dialogue modeling, and reinforcement learning from human feedback (RLHF) to jointly optimize domain expertise and conversational capability. Experimental results demonstrate substantial improvements over Llama 3.1-8B-Instruct across threat intelligence parsing and security command execution, with performance competitive with GPT-4o-mini—particularly in response relevance and alignment with human preferences. The model is publicly released to advance research and practical deployment of cybersecurity-focused LLMs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Security
• Natural Language Processing: (Large) Language Models

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Addressing limited LLM integration in cybersecurity applications
• Developing a cybersecurity-focused LLM for dialogue interactions
• Enhancing domain-specific knowledge with instruction-following capabilities

Innovation

• Cybersecurity-focused LLM for dialogue tasks
• Combines domain knowledge with instruction-following
• Outperforms Llama 3.1 on cybersecurity tasks