gVisor

Apply the sandbox manifest with PVC

kubectl apply -k base

They can then check the status of the applied resource. Verify sandbox and pod are running:

kubectl get sandbox
kubectl get pod sandbox-example

kubectl wait --for=condition=Ready sandbox sandbox-example

Harden Agent Sandbox isolation using gVisor (Optional)

The Sandbox API provides lifecycle features that are useful for managing long running sandbox workloads on kubernetes. In real world scenarios, you may want to also provide workload isolation for running untrusted workloads inside a sandbox.

gVisor provides a virtualization layer between applications and the host operating system that creates a strong layer of isolation. It implements the kernel in userspace and minimizes the risk of a workload gaining access to the host machine.

This example demonstrates how to use Sandbox along with gVisor in order to utilize the lifecycle features of Sandbox in addition with the workload isolation features of gVisor.

Create a cluster with gVisor enabled

First, enable gVisor on your Kubernetes cluster. For examples of how to enable gVisor, see the gVisor documentation.

Create a Sandbox using the gVisor runtimeClassName

Apply the kustomize overlay to inject runtimeClassName: gvisor to the vscode-sandbox example and apply it to the cluster:

kubectl apply -k overlays/gvisor

Validate that the Pod with gVisor enabled is running:

$ kubectl wait --for=condition=Ready sandbox sandbox-example
$ kubectl get pods -o jsonpath=$'{range .items[*]}{.metadata.name}: {.spec.runtimeClassName}\n{end}'

Harden Agent Sandbox isolation using Kata Containers (Optional)

Prerequisites

• Host machine that supports nested virtualization

  You can verify that by running:

  cat /sys/module/kvm_intel/parameters/nested
  

  In case of AMD platform replace kvm_intel with kvm_amd The output must be “Y” or 1.

• minikube

• kubectl

Create minikube cluster

Note: At this moment, we use only containerd runtime, since it works without additional adjustments.

minikube start --vm-driver kvm2 --memory 8192  --container-runtime=containerd --bootstrapper=kubeadm

Install Kata Containers

Follow the instructions provided at Kata Containers Installation Guide

Create a Sandbox using the kata-qemu runtimeClassName

Apply the kustomize overlay to inject runtimeClassName: kata-qemu to the vscode-sandbox example and apply it to the cluster:

kubectl apply -k overlays/kata

Validate that the Pod with Kata container enabled is running:

$ kubectl wait --for=condition=Ready sandbox sandbox-example
$ kubectl get pods -o jsonpath=$'{range .items[*]}{.metadata.name}: {.spec.runtimeClassName}\n{end}'

Accesing vscode

Port forward the vscode server port.

 kubectl port-forward --address 0.0.0.0 pod/sandbox-example 13337

Connect to the vscode-server on a browser via http://localhost:13337 or :13337

If should ask for a password.

Getting vscode password

In a separate terminal connect to the pod and get the password.

kubectl exec  sandbox-example --  cat /root/.config/code-server/config.yaml 

Use the password and connect to vscode.

Use gemini-cli

Gemini cli is preinstalled. Open a teminal in vscode and use Gemini cli.