New method enhances LLMs for cybersecurity with less data

Researchers have developed a resource-efficient method called Domain-Adaptive Continuous Pretraining (DAP) to specialize Large Language Models (LLMs) for cybersecurity tasks. By using a curated 126-million-word corpus and a distributed FSDP pipeline, they adapted Llama-3.1-8B, DeepSeek-R1-Distill-Qwen-14B, and Llama-3.3-70B-Instruct models. The adapted Llama-3.3-70B-Ins-DAP model achieved state-of-the-art performance on three cybersecurity benchmarks using significantly less training data than comparable models. AI

IMPACT This research demonstrates a more efficient way to create specialized AI models for cybersecurity, potentially reducing computational costs and accelerating the development of AI assistants for threat analysis.

RANK_REASON The cluster contains an academic paper detailing a new methodology for adapting LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• DeepSeek-R1-Distill-Qwen-14B
• Foundation-Sec-8B
• Llama-3.1-8B
• Llama-3.3-70B-Ins-DAP
• Llama-3.3-70B-Instruct
• Llama-Primus-Base

• paper
• model release

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