Automated Vulnerability Testing and Detection Digital Twin Framework for 5G Systems

Danielle Dauphinais, Michael Zylka, Harris Spahic, Farhan Shaik, Jingda Yang, Isabella Cruz, Jakob Gibson, Ying Wang

Abstract: Efficient and precise detection of vulnerabilities in 5G protocols and implementations is crucial for ensuring the security of its application in critical infrastructures. However, with the rapid evolution of 5G standards and the trend towards softwarization and virtualization, this remains a challenge. In this paper, we present an automated Fuzz Testing Digital Twin Framework that facilitates systematic vulnerability detection and assessment of unintended emergent behavior, while allowing for efficient fuzzing path navigation. Our framework utilizes assembly-level fuzzing as an acceleration engine and is demonstrated on the flagship 5G software stack: srsRAN. The introduced digital twin solution enables the simulation, verification, and connection to 5G testing and attack models in real-world scenarios. By identifying and analyzing vulnerabilities on the digital twin platform, we significantly improve the security and resilience of 5G systems, mitigate the risks of zero-day vulnerabilities, and provide comprehensive testing environments for current and newly released 5G systems.