Chapter 3. Configure
You can configure confidential containers on bare-metal servers with the Intel® Trust Domain Extensions (TDX) and AMD SEV-SNP Trusted Execution Environments (TEEs).
Perform the following steps:
- Configure worker nodes so that TEEs are automatically detected.
- Intel® TDX: Configure the remote attestation infrastructure.
- Enable confidential containers.
Create initdata to initialize a pod with sensitive or workload-specific data at runtime.
ImportantDo not use the default permissive Kata Agent policy in a production environment. You must configure a restrictive policy, preferably by creating initdata.
As a minimum requirement, you must disable
ExecProcessRequestto prevent a cluster administrator from accessing sensitive data by running theoc execcommand on a confidential containers pod.- Add initdata to a pod manifest.
-
Create the
KataConfigCR. - Verify the attestation process.
- Configure your workload for confidential containers.
3.1. Auto-detecting TEEs Copy linkLink copied to clipboard!
You must label your worker nodes so that the OpenShift sandboxed containers Operator can detect the Trusted Execution Environments (TEEs).
You label the nodes by installing and configuring the Node Feature Discovery (NFD) Operator.
3.1.1. Creating a NodeFeatureDiscovery custom resource Copy linkLink copied to clipboard!
You create a NodeFeatureDiscovery custom resource (CR) to define the configuration parameters that the Node Feature Discovery (NFD) Operator checks to automatically detect your TEE.
Prerequisites
- You have installed the NFD Operator. For more information, see Node Feature Discovery Operator in the OpenShift Container Platform documentation.
Procedure
Create a
my-nfd.yamlmanifest file according to the following example:apiVersion: nfd.openshift.io/v1 kind: NodeFeatureDiscovery metadata: name: nfd-instance namespace: openshift-nfd spec: operand: image: registry.redhat.io/openshift4/ose-node-feature-discovery-rhel9:v4.21 imagePullPolicy: Always servicePort: 12000 workerConfig: configData: |Create the
NodeFeatureDiscoveryCR:$ oc create -f my-nfd.yaml
3.1.2. Creating the NodeFeatureRule custom resource Copy linkLink copied to clipboard!
Create a NodeFeatureRule custom resource for your Trusted Execution Environment (TEE).
Procedure
-
Create a custom resource manifest named
my-nodefeaturerule.yaml:
apiVersion: nfd.openshift.io/v1alpha1
kind: NodeFeatureRule
metadata:
name: consolidated-hardware-features
namespace: openshift-nfd
spec:
rules:
- name: "runtime.kata"
labels:
feature.node.kubernetes.io/runtime.kata: "true"
matchAny:
- matchFeatures:
- feature: cpu.cpuid
matchExpressions:
SSE42: { op: Exists }
VMX: { op: Exists }
- feature: kernel.loadedmodule
matchExpressions:
kvm: { op: Exists }
kvm_intel: { op: Exists }
- matchFeatures:
- feature: cpu.cpuid
matchExpressions:
SSE42: { op: Exists }
SVM: { op: Exists }
- feature: kernel.loadedmodule
matchExpressions:
kvm: { op: Exists }
kvm_amd: { op: Exists }
- name: "amd.sev-snp"
labels:
amd.feature.node.kubernetes.io/snp: "true"
extendedResources:
sev-snp.amd.com/esids: "@cpu.security.sev.encrypted_state_ids"
matchFeatures:
- feature: cpu.cpuid
matchExpressions:
SVM: { op: Exists }
- feature: cpu.security
matchExpressions:
sev.snp.enabled: { op: Exists }
- name: "intel.sgx"
labels:
intel.feature.node.kubernetes.io/sgx: "true"
extendedResources:
sgx.intel.com/epc: "@cpu.security.sgx.epc"
matchFeatures:
- feature: cpu.cpuid
matchExpressions:
SGX: { op: Exists }
SGXLC: { op: Exists }
- feature: cpu.security
matchExpressions:
sgx.enabled: { op: IsTrue }
- feature: kernel.config
matchExpressions:
X86_SGX: { op: Exists }
- name: "intel.tdx"
labels:
intel.feature.node.kubernetes.io/tdx: "true"
extendedResources:
tdx.intel.com/keys: "@cpu.security.tdx.total_keys"
matchFeatures:
- feature: cpu.cpuid
matchExpressions:
VMX: { op: Exists }
- feature: cpu.security
matchExpressions:
tdx.enabled: { op: Exists }
Create the
NodeFeatureRuleCR by running the following command:$ oc create -f my-nodefeaturerule.yamlNoteA relabeling delay of up to 1 minute might occur.
3.2. Deploying Intel TDX remote attestation Copy linkLink copied to clipboard!
Set up the Intel® remote attestation infrastructure to enable quote generation and attestation for Intel® Trust Domain Extensions (TDX) pod virtual machines. This infrastructure includes an in-cluster Provisioning Certificate Caching Service (PCCS), automatic per-node Provisioning Certification Key (PCK) Cert ID Retrieval Tool based platform (re-)registration, and a per-node Quote Generation Service (QGS).
The system does not back up the PCCS database automatically. Cluster administrators must implement a manual backup strategy for the database file located at /var/cache/pccs/ on the deployment node, typically a control plane node. If you do not have a valid backup, you must trigger an SGX Factory Reset in the BIOS to re-provision the required platform manifests.
Prerequisites
- You must deploy the Intel® remote attestation infrastructure to enable quote generation for Intel® Trust Domain Extensions (TDX) pod virtual machines.
- You have installed the Intel® device plugins Operator and created an instance of the Intel® Software Guard Extensions device plugin. For details, see Installing from the software catalog by using the web console in the OpenShift Container Platform documentation.
- The node on which you deploy PCCS has Internet access.
Procedure
Configure the remote attestation project:
Create the
intel-dcapnamespace by running the following command:$ oc create namespace intel-dcapSwitch to the
intel-dcapproject by running the following command:$ oc project intel-dcapCreate dedicated service accounts for PCCS and QGS by running the following commands:
$ oc create serviceaccount pccs-sa -n intel-dcap$ oc create serviceaccount qgs-sa -n intel-dcapGrant the privileged Security Context Constraint to the service accounts by running the following commands:
$ oc adm policy add-scc-to-user privileged -z pccs-sa -n intel-dcap$ oc adm policy add-scc-to-user privileged -z qgs-sa -n intel-dcap
Switch to the default project by running the following command:
$ oc project defaultSet the PCCS variables by running the following commands:
$ export PCCS_API_KEY="<API_KEY_VALUE>"To obtain the API key for the Intel® Software Guard Extensions and Intel® TDX Provisioning Certification Service, navigate to the Intel Trusted Services API portal, sign in, and subscribe to the Provisioning Certification Service. The API key is displayed on the Manage Subscriptions page.
$ export PCCS_USER_TOKEN="${PCCS_USER_TOKEN:-mytoken}"For details about PCCS tokens, see the Design Guide for Intel® SGX Provisioning Certificate Caching Service (Intel® SGX PCCS).
$ export PCCS_ADMIN_TOKEN="${PCCS_ADMIN_TOKEN:-mytoken}"$ export PCCS_NODE=$(oc get nodes \ -l 'node-role.kubernetes.io/control-plane=,node-role.kubernetes.io/master=' \ -o jsonpath='{.items[0].metadata.name}')Set the cluster proxy variable by running the appropriate command:
$ export CLUSTER_HTTPS_PROXY="$(oc get proxy/cluster \ -o jsonpath={.spec.httpsProxy})"$ export CLUSTER_NO_PROXY="$(oc get proxy/cluster \ -o jsonpath={.spec.noProxy})"Create the PCCS secrets:
Set the PCCS secrets variables by running the following commands:
$ export PCCS_USER_TOKEN_HASH=$(echo -n "$PCCS_USER_TOKEN" | sha512sum | tr -d '[:space:]-')$ export PCCS_ADMIN_TOKEN_HASH=$(echo -n "$PCCS_ADMIN_TOKEN" | sha512sum | tr -d '[:space:]-')$ export PCCS_PEM_CERT_PATH=$(mktemp -d)NoteThis directory is automatically deleted at reboot. To re-use the PCCS certificate and key, you must create a persistent directory.
Generate an RSA key pair and output the private key as a PCCS certificate by running the following command:
$ openssl req -x509 -sha256 -nodes -days 365 -newkey rsa:2048 \ -keyout $PCCS_PEM_CERT_PATH/private.pem \ -out $PCCS_PEM_CERT_PATH/certificate.pem \ -subj "/C=US/ST=Denial/L=Springfield/O=Dis/CN=www.example.com"Set the PCCS certificate variables by running the following commands:
$ export PCCS_PEM=$(cat "$PCCS_PEM_CERT_PATH"/private.pem | base64 | tr -d '\n')$ export PCCS_CERT=$(cat "$PCCS_PEM_CERT_PATH"/certificate.pem | base64 | tr -d '\n')Create the PCCS secrets by running the following command:
$ oc create secret generic pccs-secrets \ --namespace intel-dcap \ --from-literal=PCCS_API_KEY="$PCCS_API_KEY" \ --from-literal=PCCS_USER_TOKEN_HASH="$PCCS_USER_TOKEN_HASH" \ --from-literal=USER_TOKEN="$PCCS_USER_TOKEN" \ --from-literal=PCCS_ADMIN_TOKEN_HASH="$PCCS_ADMIN_TOKEN_HASH"
Create the PCCS by running the following command:
$ oc apply -f <(curl -sSf https://raw.githubusercontent.com/openshift/sandboxed-containers-operator/refs/tags/v1.12.0/scripts/install-helpers/baremetal-coco/intel-dcap/pccs.yaml.in|envsubst)Configure the PCCS deployment to use the dedicated service account by running the following command:
$ oc set serviceaccount deployment/pccs pccs-sa -n intel-dcapCreate the QGS by running the following command:
$ oc apply -f https://raw.githubusercontent.com/openshift/sandboxed-containers-operator/refs/tags/v1.12.0/scripts/install-helpers/baremetal-coco/intel-dcap/qgs.yamlConfigure the QGS DaemonSet to use the dedicated service account by running the following command:
$ oc set serviceaccount daemonset/tdx-qgs qgs-sa -n intel-dcap
3.3. Enabling confidential containers Copy linkLink copied to clipboard!
You enable confidential containers and specify the deployment mode by creating an osc-feature-gates config map.
The deployment mode determines how the Operator installs and configures the Kata runtime. This flexibility allows the Operator to work consistently in clusters with or without the Machine Config Operator (MCO).
MachineConfig-
For clusters that use the Machine Config Operator (MCO). If the
deploymentModekey is missing in the config map, the Operator defaults to theMachineConfigfor backward compatibility. DaemonSet-
For clusters without the MCO. The Operator uses a
DaemonSetto install kata-containers RPMs and manage CRI-O configuration by using host drop-in files. Installation progress is tracked through node labels (for example,installing,installed). DaemonSetFallback-
Enables conditional deployment based on the cluster environment. When set, the operator checks for the presence of the MCO. It uses
DaemonSetif theMachineConfigadd-on is unavailable and defaults toMachineConfigotherwise.
Procedure
Create a
my-feature-gate.yamlmanifest file:apiVersion: v1 kind: ConfigMap metadata: name: osc-feature-gates namespace: openshift-sandboxed-containers-operator data: confidential: "true" deploymentMode: <deployment_mode><deployment_mode>- Specify the deployment mode.
Create the config map by running the following command:
$ oc create -f my-feature-gate.yaml
3.4. Initializing pods at runtime by using initdata Copy linkLink copied to clipboard!
You can initialize a pod with workload-specific data at runtime by creating and applying initdata.
This approach enhances security by reducing the exposure of confidential information and improves flexibility by eliminating custom image builds. For example, initdata can include three configuration settings:
- An X.509 certificate for secure communication.
- A cryptographic key for authentication.
-
An optional Kata Agent
policy.regofile to enforce runtime behavior when overriding the default Kata Agent policy.
The initdata content configures the following components:
- Attestation Agent (AA), which verifies the trustworthiness of the pod by sending evidence for attestation.
- Confidential Data Hub (CDH), which manages secrets and secure data access within the pod VM.
- Kata Agent, which enforces runtime policies and manages the lifecycle of the containers inside the pod VM.
You create an initdata.toml file and convert it to a gzip-format Base64-encoded string.
You apply initdata to a confidential containers pod by adding an annotation to the pod manifest.
3.5. Configuring confidential containers for NVIDIA GPUs Copy linkLink copied to clipboard!
Configure confidential containers to use NVIDIA graphics processing units (GPUs). By configuring the required Operators and custom resources, you can provision both regular and confidential GPUs for your sandboxed workloads.
3.5.1. NVIDIA GPUs as trusted execution environments Copy linkLink copied to clipboard!
Use NVIDIA graphics processing units (GPUs) as a trusted execution environment (TEE) to provide hardware-based isolation for your confidential workloads. Leveraging NVIDIA GPUs within a TEE protects data and code in memory from unauthorized access or tampering, even from privileged users or the host operating system.
When you deploy confidential containers on bare-metal servers with NVIDIA GPU support, you must manually configure the MachineConfig with the required kernel arguments for GPU integration. After configuring the MachineConfig, verify that the kernel arguments are correctly applied to the machine config pool where Kata containers and GPU support are configured to run.
3.5.2. Create a MachineConfig for NVIDIA GPUs Copy linkLink copied to clipboard!
Enable Input-Output Memory Management Unit (IOMMU) kernel parameters on your worker nodes. This configuration helps you support GPU pass-through for your sandboxed containers.
Prerequisites
-
You have access to the cluster as a user with the
cluster-adminrole.
Procedure
Create a
gpu-machine-config.yamlmanifest file according to the following example:apiVersion: machineconfiguration.openshift.io/v1 kind: MachineConfig metadata: labels: machineconfiguration.openshift.io/role: worker name: 100-iommu-kernel-args spec: config: ignition: version: 3.2.0 kernelArguments: - amd_iommu=on - intel_iommu=onNoteIf using Single Node OpenShift (SNO), replace
workerwithmasterin themachineconfiguration.openshift.io/rolelabel.The nodes will reboot after applying this configuration.
Create the config map by running the following command:
$ oc create -f gpu-machine-config.yaml
Verification
Verify the kernel parameters are set by running the following commands:
$ oc debug node/<node_name>$ cat /proc/cmdline | grep iommu
3.5.3. Install the Node Feature Discovery Operator Copy linkLink copied to clipboard!
Install the Node Feature Discovery (NFD) Operator to detect hardware features and system configurations on your cluster nodes. This tool enables automatic labeling based on the detected features
Prerequisites
- You have installed OpenShift sandboxed containers Operator.
-
You have access to the cluster as a user with the
cluster-adminrole.
Procedure
- Install the Node Feature Discovery (NFD) Operator by following the OpenShift Container Platform documentation.
Verification
Verify the NFD Operator is active by running the following command:
$ oc get pods -n openshift-nfdExample output
NAME READY STATUS RESTARTS AGE nfd-controller-manager-5d8d9d9f8b-abcde 2/2 Running 0 2m
3.5.4. Create a node feature rule for NVIDIA GPUs Copy linkLink copied to clipboard!
Create a NodeFeatureRule custom resource to match NVIDIA kernel modules on your cluster. This custom resource enables the automatic labeling of nodes with compatible NVIDIA graphics processing units.
Prerequisites
-
You have created the
NodeFeatureDiscoverycustom resource. For more information, see Create the NodeFeatureDiscovery custom resource. -
You have access to the cluster as a user with the
cluster-adminrole.
Procedure
Create a
NodeFeatureRulecustom resource to match NVIDIA kernel modules by running the following command:apiVersion: nfd.openshift.io/v1alpha1 kind: NodeFeatureRule metadata: name: nvidia-kernel-modules spec: rules: - name: kernel-module-gdrdrv labels: nvidia.com/gdrcopy.capable: "true" matchFeatures: - feature: kernel.loadedmodule matchExpressions: gdrdrv: op: Exists - name: kernel-module-nvidia_fs labels: nvidia.com/gds.capable: "true" matchFeatures: - feature: kernel.loadedmodule matchExpressions: nvidia_fs: op: Exists - name: kernel-module-nvidia_peermem labels: nvidia.com/peermem.capable: "true" matchFeatures: - feature: kernel.loadedmodule matchExpressions: nvidia_peermem: op: ExistsCreate the
NodeFeatureRuleCR by running the following command:$ oc create -f my-nfd-gpu.yaml
Verification
Verify that the labels are applied to nodes by running the following command:
$ oc get nodes -o json | jq '.items[].metadata.labels | with_entries(select(.key | startswith("nvidia.com")))'
3.5.5. Install the NVIDIA GPU Operator Copy linkLink copied to clipboard!
You must install the NVIDIA GPU Operator to manage GPU resources in your cluster.
Prerequisites
-
You have created the
NodeFeatureRulecustom resource for NVIDIA GPUs. -
You have access to the cluster as a user with the
cluster-adminrole.
Procedure
- Install the NVIDIA GPU Operator version 26.3.0. For detailed installation instructions, see the NVIDIA GPU Operator documentation.
Verify that the required labels are present on your worker nodes by running the following command:
$ oc get nodes -o custom-columns=NAME:.metadata.name,LABELS:.metadata.labelsEnsure that nodes have the appropriate NVIDIA GPU labels. If labels are missing, add them manually by running the following command:
$ oc label node <node_name> nvidia.com/gpu.present=true
Verification
Verify that the GPU Operator pods are running by running the following command:
$ oc get pods -n nvidia-gpu-operatorNAME READY STATUS RESTARTS AGE gpu-operator-1234567890-abcde 1/1 Running 0 10m
3.5.6. Create the ClusterPolicy CR for NVIDIA GPUs Copy linkLink copied to clipboard!
Create a ClusterPolicy custom resource to configure the NVIDIA GPU Operator. This policy helps you correctly set up and manage the operator for use with OpenShift sandboxed containers.
Prerequisites
- You have installed the NVIDIA GPU Operator.
-
You have access to the cluster as a user with the
cluster-adminrole.
Procedure
Create a
my-cluster-policy-gpu.yamlmanifest file according to the following example:apiVersion: nvidia.com/v1 kind: ClusterPolicy metadata: name: gpu-cluster-policy spec: ccManager: defaultMode: "on" enabled: true cdi: default: false enabled: true nriPluginEnabled: false daemonsets: rollingUpdate: maxUnavailable: '1' updateStrategy: RollingUpdate dcgm: enabled: false dcgmExporter: config: name: '' enabled: false serviceMonitor: enabled: true devicePlugin: config: default: '' name: '' enabled: false mps: root: /run/nvidia/mps driver: certConfig: name: '' enabled: false kernelModuleConfig: name: '' kernelModuleType: auto licensingConfig: configMapName: '' nlsEnabled: true repoConfig: configMapName: '' upgradePolicy: autoUpgrade: true drain: deleteEmptyDir: false enable: false force: false timeoutSeconds: 300 maxParallelUpgrades: 1 maxUnavailable: 25% podDeletion: deleteEmptyDir: false force: false timeoutSeconds: 300 waitForCompletion: timeoutSeconds: 0 useNvidiaDriverCRD: false useOpenKernelModules: false virtualTopology: config: '' gdrcopy: enabled: false gds: enabled: false gfd: enabled: true kataManager: enabled: false mig: strategy: single migManager: enabled: false nodeStatusExporter: enabled: true operator: defaultRuntime: crio initContainer: {} runtimeClass: nvidia use_ocp_driver_toolkit: true kataSandboxDevicePlugin: enabled: true env: - name: P_GPU_ALIAS value: pgpu - name: NVSWITCH_ALIAS value: nvswitch sandboxWorkloads: defaultWorkload: vm-passthrough enabled: true mode: kata toolkit: enabled: false installDir: /usr/local/nvidia validator: plugin: env: - name: WITH_WORKLOAD value: 'false' vfioManager: enabled: true env: - name: BIND_NVSWITCHES value: 'true' vgpuDeviceManager: enabled: false vgpuManager: enabled: falseCreate the
ClusterPolicyCR by running the following command:$ oc create -f my-cluster-policy-gpu.yaml
Verification
Verify the GPU Operator setup by running the following command:
$ oc get pods -n nvidia-gpu-operatorNAME READY STATUS RESTARTS AGE gpu-operator-cb99f5757-djl7k 1/1 Running 2 16h nvidia-cc-manager-hjd6t 1/1 Running 5 (42m ago) 16h nvidia-kata-sandbox-device-plugin-daemonset-wn6bc 1/1 Running 2 16h nvidia-sandbox-validator-7cvx5 1/1 Running 0 70m nvidia-vfio-manager-zsmqn 1/1 Running 2 16hVerify the CC Manager DaemonSet by running the following command:
$ oc get daemonset -n nvidia-gpu-operator | grep cc-managernvidia-cc-manager 1 1 1 1 1 nvidia.com/gpu.deploy.cc-manager=true 7m43sCreate a sample pod to test the GPU allocation.
NoteYou must create the KataConfig custom resource before creating the GPU sample pod. See Creating the KataConfig custom resource.
For confidential GPUs, create the following pod:
apiVersion: v1 kind: Pod metadata: name: sample-gpu-pod annotations: io.katacontainers.config.hypervisor.default_memory: "32768" io.katacontainers.config.hypervisor.cc_init_data: "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" spec: runtimeClassName: kata-cc-nvidia-gpu restartPolicy: OnFailure containers: - name: gpu-cc-verifier image: quay.io/openshift_sandboxed_containers/gpu-verifier:ubi9 imagePullPolicy: IfNotPresent command: ["/bin/bash"] args: - -c - | /opt/cuda-samples/Samples/0_Introduction/vectorAdd/build/vectorAdd sleep 36000 resources: limits: nvidia.com/pgpu: 1 securityContext: privileged: falseNoteThe
io.katacontainers.config.hypervisor.cc_init_dataannotation includes a permissive kata-agent policy for verification purposes. The "exec" and "log" APIs use an embedded kata-agent policy that disables them. This configuration does not include a Key Broker Service (KBS) URL, which prevents issues in customer environments where the KBS URL might not align to the actual deployment.Verify the sample pod is running successfully by running the following command:
$ oc get podsNAME READY STATUS RESTARTS AGE sample-gpu-pod 1/1 Running 0 2mCheck the pod logs to verify GPU functionality by running the following command:
$ oc logs sample-gpu-pod[Vector addition of 50000 elements] Copy input data from the host memory to the CUDA device CUDA kernel launch with 196 blocks of 256 threads Copy output data from the CUDA device to the host memory Test PASSED Done
3.5.7. Required node labels for GPU runtime classes Copy linkLink copied to clipboard!
Apply specific labels to your worker nodes so you can use NVIDIA GPUs with OpenShift sandboxed containers. The NVIDIA GPU Operator typically adds these labels automatically when it detects compatible hardware configured for VFIO passthrough mode.
The required labels depend on whether you are deploying confidential GPUs.
- Labels for confidential GPUs
For confidential GPU workloads using the
kata-cc-nvidia-gpuruntime class, nodes must have the base Kata and GPU labels, plus additional labels for confidential computing and the Trusted Execution Environment (TEE). Nodes must have the following labels:Base Kata label:
-
feature.node.kubernetes.io/runtime.kata: "true"
-
Base GPU labels:
-
nvidia.com/gpu.present: "true" -
nvidia.com/gpu.deploy.vfio-manager: "true" -
nvidia.com/gpu.deploy.kata-sandbox-device-plugin: "true"
-
Confidential computing GPU labels:
-
nvidia.com/cc.mode.state: "on" -
nvidia.com/cc.ready.state: "true" -
nvidia.com/gpu.deploy.cc-manager: "true"
-
TEE label (one of the following):
-
intel.feature.node.kubernetes.io/tdx: "true" -
amd.feature.node.kubernetes.io/snp: "true"
-
3.6. Create the KataConfig custom resource Copy linkLink copied to clipboard!
You must create the KataConfig custom resource (CR) to install kata-cc as a runtime class on your worker nodes.
If you want to configure NVIDIA GPUs for confidential containers, complete the GPU configuration steps before creating the KataConfig CR. For details, see Configure confidential containers for NVIDIA GPUs.
Prerequisites
-
Creating the
KataConfigCR automatically reboots the worker nodes. The reboot can take from 10 to more than 60 minutes depending on your deployment size, hardware type, and other factors.
Procedure
Create an
example-kataconfig.yamlmanifest file according to the following example:apiVersion: kataconfiguration.openshift.io/v1 kind: KataConfig metadata: name: example-kataconfig spec: enablePeerPods: false checkNodeEligibility: true logLevel: info # kataConfigPoolSelector: # matchLabels: # <label_key>: '<label_value>'<label_key>: '<label_value>'-
Optional: If you have applied node labels to install
kata-ccon specific nodes, specify the key and value, for example,kata-cc: 'true'.
Create the
KataConfigCR by running the following command:$ oc create -f example-kataconfig.yamlThe new
KataConfigCR is created and installskata-ccas a runtime class on the worker nodes.Wait for the
kata-ccinstallation to complete and the worker nodes to reboot before verifying the installation.Monitor the installation progress by running the following command:
$ watch "oc describe kataconfig | sed -n /^Status:/,/^Events/p"When the status of all workers under
kataNodesisinstalledand the conditionInProgressisFalsewithout specifying a reason, thekata-ccis installed on the cluster.Verify the runtime classes by running the following command:
$ oc get runtimeclass+
NAME HANDLER AGE
kata kata 34m
{runtime} {runtime-handler} 152m
+ You can also see the default kata runtime class in addition to kata-cc.
3.6.1. The checkNodeEligibility parameter Copy linkLink copied to clipboard!
To manage node selection for your workloads, configure the checkNodeEligibility parameter in the KataConfig resource. This determines if runtime classes are created based on hardware labels or unconditionally. From 1.12.0, this applies to all standard and confidential container (CC) runtimes.
- When
checkNodeEligibilityis set to true The Operator performs the following actions:
- Node eligibility verification: The Operator verifies that nodes have the required hardware capabilities by using node labels before installing the Kata runtime.
Conditional runtime class creation: The Operator creates runtime classes only if nodes with the required labels exist in the cluster:
-
Standard runtime classes: The
kataorkata-nvidia-gpuruntime classes are created only if nodes with the required base and GPU labels exist. -
Confidential container runtime classes: The
kata-ccorkata-cc-nvidia-gpuruntime classes are created only if nodes with the required Trusted Execution Environment (TEE) labels (such as Intel® Trust Domain Extensions (TDX) or AMD SEV-SNP) and the corresponding confidential containers and GPU labels exist.
-
Standard runtime classes: The
- Dynamic runtime class management: If no nodes match the required labels, the corresponding runtime class is not created. This prevents workload scheduling failures by ensuring users cannot select a runtime that the cluster cannot support.
- When
checkNodeEligibilityis set to false (default) The Operator performs the following actions:
-
Unconditional creation for standard runtimes: The Operator always creates the
kataandkata-nvidia-gpuruntime classes, regardless of whether nodes currently have the required hardware labels. -
Identification-based creation for CC runtimes: For the
kata-ccandkata-cc-nvidia-gpuruntime classes, the Operator still depends on the TEE label for identification, but it does not verify the base or GPU labels during the installation phase. -
Manual scheduling: The Operator skips the detailed node label check during installation. The cluster will only schedule pods using these runtime classes if a node eventually matches the
nodeSelectordefined in the runtime class.
-
Unconditional creation for standard runtimes: The Operator always creates the
Additional resources
3.7. Create initdata Copy linkLink copied to clipboard!
You create initdata to securely initialize a pod with sensitive or workload-specific data at runtime, thus avoiding the need to embed this data in a virtual machine image. This approach provides additional security by reducing the risk of exposure of confidential information and eliminates the need for custom image builds.
Prerequisites
- You have installed the Node Feature Discovery (NFD) Operator. For more information, see Node Feature Discovery Operator in the OpenShift Container Platform documentation.
-
You have deleted the
kbs_certsetting if you configureinsecure_http = truein thekbs-configconfig map for Red Hat build of Trustee.
Procedure
Obtain the Red Hat build of Trustee URL by running the following command:
$ TRUSTEE_URL=$(oc get route kbs-service \ -n trustee-operator-system -o jsonpath='{.spec.host}') \ && echo $TRUSTEE_URLCreate the
initdata.tomlfile:algorithm = <algorithm> version = "0.1.0" [data] "aa.toml" = ''' [token_configs] [token_configs.coco_as] url = '<trustee_url>' [token_configs.kbs] url = '<trustee_url>' ''' "cdh.toml" = ''' socket = 'unix:///run/confidential-containers/cdh.sock' credentials = [] [kbc] name = 'cc_kbc' url = '<trustee_url>' kbs_cert = """ -----BEGIN CERTIFICATE----- <kbs_certificate> -----END CERTIFICATE----- """ [image] image_security_policy_uri = 'kbs:///default/<secret-policy-name>/<key> ''' "policy.rego" = ''' package agent_policy default AddARPNeighborsRequest := true default AddSwapRequest := true default CloseStdinRequest := true default CopyFileRequest := true default CreateContainerRequest := true default CreateSandboxRequest := true default DestroySandboxRequest := true default GetMetricsRequest := true default GetOOMEventRequest := true default GuestDetailsRequest := true default ListInterfacesRequest := true default ListRoutesRequest := true default MemHotplugByProbeRequest := true default OnlineCPUMemRequest := true default PauseContainerRequest := true default PullImageRequest := true default ReadStreamRequest := false default RemoveContainerRequest := true default RemoveStaleVirtiofsShareMountsRequest := true default ReseedRandomDevRequest := true default ResumeContainerRequest := true default SetGuestDateTimeRequest := true default SignalProcessRequest := true default StartContainerRequest := true default StartTracingRequest := true default StatsContainerRequest := true default StopTracingRequest := true default TtyWinResizeRequest := true default UpdateContainerRequest := true default UpdateEphemeralMountsRequest := true default UpdateInterfaceRequest := true default UpdateRoutesRequest := true default WaitProcessRequest := true default ExecProcessRequest := false default SetPolicyRequest := false default WriteStreamRequest := false default ExecProcessRequest := false '''- algorithm
-
Specify
sha256,sha384, orsha512. - url
- Specify Red Hat build of Trustee
- <kbs_certificate>
- Specify the Base64-encoded TLS certificate for the attestation agent.
- kbs_cert
-
Delete the
kbs_certsetting if you configureinsecure_http = truein thekbs-configconfig map for Red Hat build of Trustee. - image_security_policy_uri
-
Optional, only if you enabled the container image signature verification policy. Replace
<secret-policy-name>and<key>with the secret name and key, respectively specified in Creating the KbsConfig custom resource.
Convert the
initdata.tomlfile to a gzipped, Base64-encoded string in a text file by running the following command:$ cat initdata.toml | gzip | base64 -w0 > initdata.txtRecord this string to use in the pod manifest.
Calculate the hash of an
initdata.tomlfile and assign its value to thehashvariable by running the following command:$ hash=$(<algorithm> initdata.toml | cut -d' ' -f1)Assign 32 bytes of 0s to the
initial_pcrvariable by running the following command:$ initial_pcr=0000000000000000000000000000000000000000000000000000000000000000Calculate the SHA-256 hash of
hashandinitial_pcrand assign its value to thePCR8_HASHvariable by running the following command:$ PCR8_HASH=$(echo -n "$initial_pcr$hash" | xxd -r -p | sha256sum | cut -d' ' -f1) && echo $PCR8_HASHRecord the
PCR8_HASHvalue for the RVPS config map.
3.8. Applying initdata to a pod Copy linkLink copied to clipboard!
Prerequisite
- You have created an initdata string.
Procedure
Add the initdata string to the pod manifest and save the file as
my-pod.yaml:apiVersion: v1 kind: Pod metadata: name: ocp-cc-pod labels: app: ocp-cc-pod annotations: io.katacontainers.config.hypervisor.cc_init_data: <initdata_string> spec: runtimeClassName: kata-cc containers: - name: <container_name> image: registry.access.redhat.com/ubi9/ubi:latest command: - sleep - "36000" securityContext: privileged: false seccompProfile: type: RuntimeDefaultwhere
<initdata_string>-
Specify the gzipped, Base64-encoded initdata value in a pod annotation to override the global
INITDATAsetting in the peer pods config map. <container_name>- Specify a container name.
Create the pod by running the following command:
$ oc create -f my-pod.yaml
3.9. Verifying attestation Copy linkLink copied to clipboard!
You can verify the attestation process by creating a test pod to retrieve a specific resource from Red Hat build of Trustee.
This procedure is an example to verify that attestation is working. Do not write sensitive data to standard I/O, because the data can be captured by using a memory dump. Only data written to memory is encrypted.
Procedure
Create a
test-pod.yamlmanifest file:apiVersion: v1 kind: Pod metadata: name: ocp-cc-pod labels: app: ocp-cc-pod annotations: io.katacontainers.config.hypervisor.cc_init_data: "<initdata_string>" spec: runtimeClassName: kata-cc containers: - name: skr-openshift image: registry.access.redhat.com/ubi9/ubi:latest command: - sleep - "36000" securityContext: privileged: false seccompProfile: type: RuntimeDefault metadata: name: coco-test-pod labels: app: coco-test-pod annotations: io.katacontainers.config.hypervisor.cc_init_data: "<initdata_string>" spec: runtimeClassName: kata-cc containers: - name: test-container image: registry.access.redhat.com/ubi9/ubi:9.3 command: - sleep - "36000" securityContext: privileged: false seccompProfile: type: RuntimeDefaultwhere:
io.katacontainers.config.hypervisor.cc_init_data-
Optional: Specifies initdata in a pod annotation, which overrides the global
INITDATAsetting in the peer pods config map.
Create the pod by running the following command:
$ oc create -f test-pod.yamlLog in to the pod by running the following command:
$ oc exec -it ocp-cc-pod -- bashFetch the Red Hat build of Trustee resource by running the following command:
$ curl http://127.0.0.1:8006/cdh/resource/default/attestation-status/statusExample output
success #/
3.10. Configuring your workload Copy linkLink copied to clipboard!
You configure your workload for confidential containers by setting kata-cc as the runtime class for the following pod-templated objects:
-
Podobjects -
ReplicaSetobjects -
ReplicationControllerobjects -
StatefulSetobjects -
Deploymentobjects -
DeploymentConfigobjects
Do not deploy workloads in an Operator namespace. Create a dedicated namespace for these resources.
Prerequisites
-
You have created the
KataConfigcustom resource (CR).
Procedure
Add
spec.runtimeClassName: kata-ccto the manifest of each pod-templated workload object as in the following example:apiVersion: v1 kind: <object> # ... spec: runtimeClassName: kata-cc # ...Apply the changes to the workload object by running the following command:
$ oc apply -f <object.yaml>OpenShift Container Platform creates the workload object and begins scheduling it.
Verification
-
Inspect the
spec.runtimeClassNamefield of a pod-templated object. If the value iskata-cc, then the workload is running on confidential containers.
3.10.1. Encrypt the block volumes Copy linkLink copied to clipboard!
You must encrypt volumes inside the TEE to ensure data stays private. Rather than relying on host-level CSI drivers, you attach raw blocks, use an init container for Linux Unified Key Setup (LUKS) formatting, and mount to your app by using shared namespaces and hooks. This keeps data secure in use, in memory, and at rest.
Prerequisites
- You have installed the Container Storage Interface (CSI) driver configured for raw block volumes. For more information, see Understanding persistent storage.
- You have installed OpenShift sandboxed containers on a bare-metal server.
- You have configured an attestation service, such as Red Hat build of Trustee, to provide secrets like the encryption passphrase.
Procedure
Create a
storage-encrypted.yamlmanifest file for thePersistentVolumeClaimobject with thevolumeModeparameter set toBlock:apiVersion: v1 kind: PersistentVolumeClaim metadata: name: storage-encrypted spec: accessModes: - ReadWriteOnce volumeMode: Block resources: requests: storage: <size>Create the
PersistentVolumeClaimobject by running the following command:$ oc create -f storage-encrypted.yamlCreate an
encrypted-pod.yamlmanifest file with the complete pod specification:apiVersion: v1 kind: Pod metadata: annotations: io.katacontainers.config.hypervisor.cc_init_data: <init_data> name: storage-encrypted labels: app: storage-encrypted spec: runtimeClassName: kata-cc shareProcessNamespace: true initContainers: - name: format-disk image: registry.redhat.io/openshift-sandboxed-containers/osc-storage-helper:1.12.0 command: ["/usr/local/bin/luks-helper", "format-disk"] securityContext: privileged: true restartPolicy: Always env: - name: PASS valueFrom: secretKeyRef: name: <my_sealed_secret> key: <secret_key> volumeMounts: - name: storage-ipc mountPath: /dev/shm volumeDevices: - name: luks-block devicePath: /dev/block-device - name: check-ready image: registry.redhat.io/openshift-sandboxed-containers/osc-storage-helper:1.12.0 command: ["/usr/local/bin/luks-helper", "wait-ready"] securityContext: privileged: true volumeMounts: - name: storage-ipc mountPath: /dev/shm containers: - name: <container_name> image: <image_name> ports: - containerPort: 8888 env: - name: DATA_DIR value: <mount_point> lifecycle: postStart: exec: command: - /bin/sh - -c - | PID=$(cat /dev/shm/luks-helper.pid) chmod ug+w "$(dirname "$DATA_DIR")" ln -sfn "/proc/$PID/root/mnt/storage" "$DATA_DIR" securityContext: privileged: true volumeMounts: - name: storage-ipc mountPath: /dev/shm volumes: - name: luks-block persistentVolumeClaim: claimName: storage-encrypted - name: storage-ipc emptyDir: medium: Memorywhere:
<init_data>- Specifies the initdata for the runtime configuration.
<my_sealed_secret>- Specifies the name of the sealed secret that contains the LUKS encryption passphrase.
<secret_key>- Specifies the key within the sealed secret that contains the encryption passphrase.
<container_name>- Specifies the container name for your application container.
<image_name>- Specifies the image name.
<mount_point>- Specifies the mount point for encrypted storage inside your application container.
Create the pod by running the following command:
$ oc create -f encrypted-pod.yaml