Modern Large Language Model(LLM) systems are assembled from third-party artifacts—pre-trained weights, fine-tuning adapters, datasets, dependency packages, and container images—fetched through automated pipelines. This speed comes with supply-chain risks: compromised dependencies, malicious hub artifacts and unsafe deserialization, forged provenance, and backdoored models. A core gap is that training and release claims (e.g., data and code lineage, build environment, and security scanning results) are rarely cryptographically bound to the artifacts they describe, making enforcement inconsistent across teams and stages. We propose an attestation-aware promotion gate: before an artifact is admitted into trusted environments (training, fine-tuning, deployment), the gate verifies claim evidence, enforces safe-loading and static scanning policies, and applies secure-by-default deployment constraints. When organizations operate runtime security tooling, the same gate can optionally ingest standardized dynamic signals via plug-ins to reduce uncertainty for high-risk artifacts. We outline a practical claims-to-controls mapping and an evaluation blueprint using representative supply-chain scenarios and operational metrics (coverage, and decisions), charting a path toward a full research paper.