Mobile applications increasingly handle sensitive user data, requiring compliance with laws such as China’s Personal Information Protection Law (PIPL). With pervasive encryption, traditional payload inspection is infeasible. We introduce a multi-dimensional risk assessment framework that detects potential privacy violations using Transport Layer Security (TLS) flow-level features. At its core is the Contextual Violation Risk (CVR), a novel metric powered by Natural Language Inference (NLI) and reasoning-optimized Large Language Models (LLMs). CVR captures semantic mismatches between an application’s declared purpose and its actual server communications (e.g., a health app connecting to data-broker domains). The risk model integrates three complementary dimensions: Data Asymmetry Risk (DAR) for volume-based anomalies, Burst Transfer Risk (BTR) for temporal irregularities, and CVR for semantic compliance. Together, these enable classification of applications by privacy risk profile and potential regulatory concern. Applied to 350 Chinese mobile applications, the framework identified 31 (8.9%) with elevated risk profiles. Validation through stratified expert review of an average of 545 connection flows per application achieved 100% agreement. Weight assignments (CVR 40%, DAR 35%, BTR 25%) were derived from systematic analysis of 142 PIPL enforcement cases, ensuring alignment with regulatory priorities. The results demonstrate the feasibility of automated privacy risk assessment for encrypted mobile traffic and provide a foundation for scalable regulatory compliance monitoring.