Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report
Authors: name: Paul Kassianik, name: Baturay Saglam, name: Alexander Chen, name: Blaine Nelson, name: Anu Vellore, name: Massimo Aufiero, name: Fraser Burch, name: Dhruv Kedia, name: Avi Zohary, name: Sajana Weerawardhena, name: Aman Priyanshu, name: Adam Swanda, name: Amy Chang, name: Hyrum Anderson, name: Kojin Oshiba, name: Omar Santos, name: Yaron Singer, name: Amin Karbasi

As transformer-based large language models (LLMs) increasingly permeate
society, they have revolutionized domains such as software engineering,
creative writing, and digital arts. However, their adoption in cybersecurity
remains limited due to challenges like scarcity of specialized training data
and complexity of representing cybersecurity-specific knowledge. To address
these gaps, we present Foundation-Sec-8B, a cybersecurity-focused LLM built on
the Llama 3.1 architecture and enhanced through continued pretraining on a
carefully curated cybersecurity corpus. We evaluate Foundation-Sec-8B across
both established and new cybersecurity benchmarks, showing that it matches
Llama 3.1-70B and GPT-4o-mini in certain cybersecurity-specific tasks. By
releasing our model to the public, we aim to accelerate progress and adoption
of AI-driven tools in both public and private cybersecurity contexts.