Model Cards are critical for AI transparency—but today they’re not standardized, often buried in README files or repos, and lack integration with the AI supply chain. This limits discoverability of key metadata like CVEs, SBOMs, evaluations, performance and intended use.

We present, OCI-compliant Model Card Discoverability Service that surfaces structured metadata from Model Cards attached to models using OCI referrers, without modifying model blobs. This enables separation of metadata from models, allowing trusted updates when new evaluations, CVEs, or attestations emerge — without republishing the model itself.

The system pulls and indexes Model Cards stored as OCI artifacts (e.g., via ORAS) and builds a searchable SQLite database. This enables users and automated systems to filter models by architecture, licensing, compliance benchmarks, and security attestations—without modifying the registry or model blob. It bridges the gap between open standards and registry-native workflows, enabling better governance, interoperability, and trust in AI deployments.