Title: Discussion 1 — Containers, Trust, and Untrusted Models
Course: AIINFRA 101: Applied Infrastructure & Containerization > Section: Week 01 — Why Containers for AI: VMs, Images, and the Docker Model

💬 Discussion 1 — Containers, Trust, and Untrusted Models (20 pts)

We learned this week that containers share the host kernel, which means a kernel vulnerability or container-escape bug can potentially affect every container on that host — while VMs, because they run their own separate guest kernel, are far more isolated from one another. Suppose you're building an AI inference cluster that runs models uploaded by third parties you don't fully trust (think: a public model-hosting platform). If a kernel vulnerability could affect all containers on a host, when would you still choose containers over VMs for this kind of untrusted workload, and what compensating security controls would you add to make that choice safer? Consider things like resource limits, read-only filesystems, network policies, sandboxed runtimes, or a hybrid approach. Post your initial response (150+ words), then respond to at least two classmates — push back, build on their idea, or add a control they didn't mention.