Large Language Models (LLMs) are increasingly embedded in production systems, creating complex development and deployment pipelines that resemble modern software supply chains. These pipelines span dataset ingestion, training workflows, model artifacts, distribution registries, inference infrastructure, and downstream application integration. While prior work has extensively examined model-centric threats such as extraction, inversion, and prompt injection, the security of the complete LLM lifecycle remains insufficiently addressed.