Abstract: Current research on the construction and governance of open-source large model ecosystems primarily focuses on macro-level top-level design, with a notable lack of micro-level risk identification and compliance strategies. In the open-source AI ecosystem, diverse actors increasingly participate in model deployment, creating an urgent need for theoretical guidance to support industry compliance practices.This study systematically reviews representative open-source licenses in the global large model domain, identifies intellectual property risks potentially triggered by license breaches, and employs an interdisciplinary "Law-Technology-Intelligence" analytical framework to propose countermeasures.The primary causes of license breach risks include insufficient clearance of rights-related information during the data preprocessing stage, lack of typological understanding of open-source license provisions during application management, misaligned risk recognition, and inadequate control over derivative outputs.At the data preprocessing (upstream) stage, enterprises should implement a full-cycle rights information clearance mechanism integrating technical filtering, responsibility clarification, and data compliance.At the model management (midstream) stage, organizations must account for the typological traits and risk profiles of permissive licenses,restrictive-permissive licenses, and copyleft licenses, and accordingly establish differentiated compliance governance systems.At the content output (downstream) stage, compliance management should encompass four dimensions: data handling, model training, generation control, and user supervision.