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The virtualization and softwarization of 5G and NextG are critical enablersof the shift to flexibility, but they also present a potential attack surfacefor threats. However, current security research in communication systemsfocuses on specific aspects of security challenges and lacks a holisticperspective. To address this challenge, a novel systematic fuzzing approach isproposed to reveal, detect, and predict vulnerabilities with and without priorknowledge assumptions from attackers. It also serves as a digital twin platformfor system testing and defense simulation pipeline. Three fuzzing strategiesare proposed: Listen-and-Learn (LAL), Synchronize-and-Learn (SyAL), andSource-and-Learn (SoAL). The LAL strategy is a black-box fuzzing strategy usedto discover vulnerabilities without prior protocol knowledge, while the SyALstrategy, also a black-box fuzzing method, targets vulnerabilities moreaccurately with attacker-accessible user information and a novelprobability-based fuzzing approach. The white-box fuzzing strategy, SoAL, isthen employed to identify and explain vulnerabilities through fuzzing ofsignificant bits. Using the srsRAN 5G platform, the LAL strategy identifies 129RRC connection vulnerabilities with an average detection duration of 0.072s.Leveraging the probability-based fuzzing algorithm, the SyAL strategyoutperforms existing models in precision and recall, using significantly fewerfuzzing cases. SoAL detects three man-in-the-middle vulnerabilities stemmingfrom 5G protocol vulnerabilities. The proposed solution is scalable to otheropen-source and commercial 5G platforms and protocols beyond RRC. Extensiveexperimental results demonstrate that the proposed solution is an effective andefficient approach to validate 5G security; meanwhile, it serves as real-timevulnerability detection and proactive defense.

Systematic Meets Unintended: Prior Knowledge Adaptive 5G Vulnerability Detection via Multi-Fuzzing