The proliferation of standalone non-public 5G networks (SNPNs) in industrial and enterprise environments has introduced complex security challenges arising from software-defined infrastructures, virtualized network functions, and service-based architectures. This paper presents an attack graph–based approach for systematically modeling and analyzing vulnerabilities across the user equipment (UE), radio access network (RAN), and core domains of 5G systems. Building on experimental research at the NIK-SOC Laboratory of Óbuda University, the study demonstrates how observed attack vectors - such as IMSI catching, replay attacks, GTP-U manipulation, and slicing misconfigurations - can be represented within formal graph structures to visualize multi-stage adversarial progressions and support risk-driven security assessment.The results show that traditional attack graph methodologies must be extended to reflect 5G-specific characteristics including network slicing, edge computing, and dynamic service exposure through the Service-Based Architecture (SBA). The paper concludes by outlining future development directions that integrate artificial intelligence, multimodal data correlation, real-time dynamic graph analysis, and Zero Trust architectural principles. Together, these enhancements aim to enable predictive and adaptive defense capabilities for next-generation standalone 5G networks.