We already know how dangerous a compromised software dependency can be. AI applications quietly introduce a much larger supply chain.

A modern LLM application may depend on open-source packages, pretrained models, model weights, datasets, embedding models, adapters, plugins, external APIs and container images. Any of those components can become a security or integrity problem long before the application reaches production.

This session maps the attack surface of a modern AI application from source code to model runtime. We will examine practical risks around untrusted models, dependency compromise, model provenance, poisoned datasets, vulnerable components and AI-specific supply chain failures.

Through a controlled demonstration, we will follow a seemingly harmless AI dependency from ingestion to execution and identify where traditional software supply chain controls stop being sufficient.

We will then build a practical AI supply chain security checklist covering provenance, dependency inventory, model integrity, SBOMs, verification, access controls and monitoring.

The goal is simple: if your application has a software bill of materials, what should you have when part of your application is a model, dataset or AI component?