Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report
This addresses the problem of AI tool adoption in cybersecurity for researchers and practitioners, though it is incremental as it builds on existing architectures.
The paper tackled the limited adoption of LLMs in cybersecurity by developing Foundation-Sec-8B, a cybersecurity-focused LLM, which matches larger models like Llama 3.1-70B and GPT-4o-mini on specific cybersecurity tasks.
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.