ced4568andClaude Sonnet 5
60fbf0c385
Pre-Phase-1 platform work, approved and tested earlier but not yet committed: - Terminal banner: "RootNode Academy Lab — node: k8s-lab", upstream KubeKosh attribution left untouched in footer/README/LICENSE. - RootNode curriculum mode: scenarios/rootnode-path.json (Foundation -> Workloads -> Networking -> Configuration -> Troubleshooting -> Real Ops -> Capstone), built from existing scenario content grouped by category, no new scenarios invented. New GET /api/curriculum/path endpoint. - Student roster + per-student progress: instructor-roster, pick-your-name login (no password), students/environments-adjacent progress table migrated to a (student_id, scenario_id) composite key so practice progress and exam sessions are isolated per student instead of one global bucket. New /api/roster, /api/login endpoints. - scenarios/data/*.json: added isolation_type metadata (shared_namespace vs ephemeral_cluster) from an audit of which scenarios execute cluster-scoped or node-level commands. This was Tier-1-isolation groundwork that got superseded same-day by the full-ephemeral-per-student architecture (see docs/LAB_ARCHITECTURE.md) — the field is kept as descriptive metadata only, per the note now in scenarios/SCHEMA.md. Not wasted: it's an accurate audit of which scenarios touch cluster-scoped resources, useful independent of isolation model. - .gitignore added (node_modules/, frontend/dist/, *.db, /data/, .env) — none existed before; without it, npm installs during testing would have landed in a commit. Known limitation still open: no frontend UI for any of this yet (login screen, roster-add form, curriculum view) — API-only. No auth on the roster-add endpoint. Co-Authored-By: Claude Sonnet 5 <[email protected]>
41 lines
2.2 KiB
JSON
41 lines
2.2 KiB
JSON
{
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"id": "cks-mcq-sandboxing",
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"title": "Container Runtime Sandboxing",
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"category": "System Hardening",
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"isolation_type": "shared_namespace",
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"difficulty": "Hard",
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"type": "mcq",
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"weight": 3,
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"description": "## Container Runtime Sandboxing\n\nA security team wants to run untrusted workloads with stronger isolation than the standard `runc` runtime — specifically, they want each container's system calls to be intercepted by a user-space kernel rather than reaching the host kernel directly.\n\nWhich solution achieves this, and how is it configured in Kubernetes?",
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"options": [
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{
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"id": "a",
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"text": "Seccomp with `RuntimeDefault` — restricts which syscalls are allowed via a BPF filter, but syscalls still reach the host kernel"
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},
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{
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"id": "b",
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"text": "AppArmor with a `deny` profile — enforces path-based MAC policies but does not intercept syscalls"
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},
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{
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"id": "c",
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"text": "gVisor (`runsc`) via a `RuntimeClass` — intercepts all syscalls in user-space with its own kernel implementation, isolating the host kernel from the container"
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},
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{
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"id": "d",
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"text": "A `PodDisruptionBudget` — prevents pod disruptions and provides workload isolation"
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}
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],
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"correct_option": "c",
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"explanation": "**gVisor** (`runsc`) is a user-space kernel from Google that intercepts all container system calls before they reach the host kernel, providing strong isolation. In Kubernetes it is configured via a **RuntimeClass** resource and referenced in a pod with `spec.runtimeClassName`. Kata Containers is a similar alternative using lightweight VMs. Seccomp filters syscalls but they still hit the host kernel. AppArmor enforces file-access policies, not syscall interception.",
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"hints": [
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{
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"title": "RuntimeClass and runtimeClassName",
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"body": "Create a `RuntimeClass` that maps a name to the container runtime handler (e.g., `handler: runsc`). Reference it in a pod with `spec.runtimeClassName: <name>`.",
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"command": "kubectl explain runtimeclass\nkubectl explain pod.spec.runtimeClassName"
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}
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],
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"setup_commands": [],
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"default_namespace": "default",
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"teardown_commands": []
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}
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