Benchmarking LLMs for Threat Level Determination
The fast progress of large language models (LLMs) opens new opportunities in the management of cyber threat intelligence, but their reliability for operational tasks remains unclear. In this work, we benchmark LLMs on the task of threat level determination. First, we construct a curated dataset derived from publicly available MISP OSINT feeds. Next, we design a tailored prompt to systematically compare eight different LLMs under zero-shot conditions. Finally, we apply supervised fine-tuning on each model and perform a comparative analysis between baseline and fine-tuned versions. Our results show that zero-shot models achieve weak performance, with limited ability to correctly assign threat levels. Fine-tuned models, however, demonstrate substantial improvements, reaching F1 scores between 0.40 and 0.58 depending on the base architecture. Despite this progress, the performance is still low for practical deployment, highlighting the need for additional research on data quality, model adaptation, and domain-specific tuning.