Poster: Automated Extraction of Protocol State Machines from 3GPP Specifications with Domain-Informed Prompts and LLM Ensembles

Mobile telecommunication networks are foundational to global infrastructure and increasingly support critical sectors such as manufacturing, transportation, and healthcare. The security and reliability of these networks are essential, yet depend heavily on accurate modeling of underlying protocols through state machines. While most prior work constructs such models manually from 3GPP specifications, this process is labor-intensive, error-prone, and difficult to maintain due to the complexity and frequent updates of the specifications. Recent efforts using natural language processing have shown promise, but remain limited in handling the scale and intricacy of cellular protocols. In this work, we propose SpecGPT, a novel framework that leverages large language models (LLMs) to automatically extract protocol state machines from 3 GPP documents. SpecGPT segments specifications into semantically meaningful paragraphs and constructs domain-informed prompts to guide model interpretation. We evaluate SpecGPT on three representative 5 G protocols (NAS, NGAP, and PFCP) using manually annotated ground truth, and show that it outperforms existing approaches, demonstrating the effectiveness of LLMs for protocol modeling at scale.