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The effectiveness and efficiency of 5G software stack vulnerability andunintended behavior detection are essential for 5G assurance, especially forits applications in critical infrastructures. Scalability and automation arethe main challenges in testing approaches and cybersecurity research. In thispaper, we propose an innovative approach for automatically detectingvulnerabilities, unintended emergent behaviors, and performance degradation in5G stacks via run-time profiling documents corresponding to fuzz testing incode repositories. Piloting on srsRAN, we map the run-time profiling viaLogging Information (LogInfo) generated by fuzzing test to a high dimensionalmetric space first and then construct feature spaces based on their timestampinformation. Lastly, we further leverage machine learning-based classificationalgorithms, including Logistic Regression, K-Nearest Neighbors, and RandomForest to categorize the impacts on performance and security attributes. Theperformance of the proposed approach has high accuracy, ranging from $ 93.4 \%$ to $ 95.9 \% $, in detecting the fuzzing impacts. In addition, the proof ofconcept could identify and prioritize real-time vulnerabilities on 5Ginfrastructures and critical applications in various verticals.

NLP-based Cross-Layer 5G Vulnerabilities Detection via Fuzzing Generated Run-Time Profiling