Automated Model-Based Fuzzing for 5G O-RAN

摘要

The evolution of 5G and future-G technologies has led to the development of an open, distributed, and multi-vendor architecture known as Open Radio Access Network (O-RAN). While O-RAN offers greater flexibility and stimulates innovation, it also introduces potential issues such as bugs, inconsistencies, and vulnerabilities. In this paper, we present a novel model-based fuzzing system, ARCANE, to address these challenges. Unlike existing fuzzing techniques that struggle with the complexity and dynamism of O-RAN, ARCANE distinguishes itself by employing a smart, model-based approach. It first analyzes O-RAN specifications using a Large Language Model (LLM) and incorporates a unique method that integrates passive model learning to refine the model. With this refined model, ARCANE designs an O-RAN-aware, model-based fuzzing scheme that maintains high efficiency by adhering to O-RAN's specific semantics and syntax. We implement ARCANE and evaluate it on the OAI5G. It finds 9 root bugs from three categories, all having serious security implications. ARCANE has shown superior efficiency and coverage compared to state-of-the-arts.