Installation troubleshooting

1. The cluster doesn't connect

When attempting to connect to your Kubernetes cluster, you receive a connection error.


Run the Helm snippet again

The Helm command might not have run. Copy all the code in the cluster connection dialog and run it in your cluster.

Ensure the Kollector is running

The Kollector may not have started. The ARMO Platform flags a cluster as connected only after the Kollector microservice creates a connection. Run the following command:

kubectl -n kubescape get pods | grep kollector

The Kollector should be running:

NAME                          READY   STATUS    RESTARTS   AGE
kollector-0                   1/1     Running   0          1m38s

Connect to the Kollector

If the Kollector didn't connect to the ARMO Platform successfully, Run the following command:

kubectl -n kubescape logs -f $(kubectl -n kubescape get pods | grep kollector |  awk '{print $1}')

You should see the following logged row:

"msg":"connected successfully","URL":"wss://

2. Scanning results aren't available

After running the Helm install and seeing that the cluster is connected, the UI does not forward you to the results page


Refresh the page

Sometimes, the best solution is to simply refresh your webpage.

Ensure the Kubescape and kubevuln pods are running

You may find that one or more pods are not running after installation. Run the following:

kubectl -n kubescape get pods

Below you can see that only one pod is not running:

NAME                          READY   STATUS    RESTARTS   AGE
gateway-5c8f486dcf-hsbpq      1/1     Running   0          5m38s
kollector-0                   1/1     Running   0          5m38s
kubescape-744bc74f8c-z9drg    0/1     Pending   0          5m38s
kubevuln-7648c9c88f-6q7zd     0/1     Pending   0          5m38s
operator-5c86b8f97d-59nw5     1/1     Running   0          5m38s

Check the status of the pods that aren't running as follows:

kubectl -n kubescape describe pod <podname>

Below, the pod kubescape-744bc74f8c-z9drg has insufficient CPU in the Events section, at the bottom.

Name:         kubescape-744bc74f8c-z9drg
Namespace:    kubescape
Priority:     0
Node:         cluster-8260/
Start Time:   Tue, 07 Mar 2023 12:33:33 +0200
Labels:       app=kubescape
Annotations:  <none>
Status:       Running
Controlled By:  ReplicaSet/kubescape-5cff758d74
    Container ID:  docker://d6f8a3c613f2ea774e5f8458554b7d70a0b4694370ea6cb522ad5a96c777fdef
    Image ID:      docker-pullable://
    Port:          8080/TCP
    Host Port:     0/TCP
    State:          Running
      Started:      Tue, 07 Mar 2023 12:33:52 +0200
    Ready:          True
    Restart Count:  0
      cpu:     600m
      memory:  800Mi
      cpu:      250m
      memory:   400Mi                   
      /home/ks/.kubescape/config.json from kubescape-config-volume (rw,path="config.json")
      /home/ks/.kubescape/host-scanner.yaml from host-scanner-definition (rw,path="host-scanner-yaml")
      /var/run/secrets/ from kube-api-access-4fh8f (ro)
  Type              Status
  Initialized       True 
  Ready             True 
  ContainersReady   True 
  PodScheduled      True 
    Type:      ConfigMap (a volume populated by a ConfigMap)
    Name:      kubescape-config
    Optional:  false
    Type:      ConfigMap (a volume populated by a ConfigMap)
    Name:      host-scanner-definition
    Optional:  false
    Type:                    Projected (a volume that contains injected data from multiple sources)
    TokenExpirationSeconds:  3607
    ConfigMapName:           kube-root-ca.crt
    ConfigMapOptional:       <nil>
    DownwardAPI:             true
QoS Class:                   Burstable
Node-Selectors:              <none>
Tolerations:        op=Exists for 300s
                    op=Exists for 300s
  Type     Reason             Age                From                Message
  ----     ------             ----               ----                -------
  Warning  FailedScheduling   6m1s               default-scheduler   0/1 nodes are available: 1 Insufficient cpu. preemption: 0/1 nodes are available: 1 No preemption victims found for incoming pod.
  Warning  FailedScheduling   52s                default-scheduler   0/1 nodes are available: 1 Insufficient cpu. preemption: 0/1 nodes are available: 1 No preemption victims found for incoming pod.
  Normal   NotTriggerScaleUp  1s (x6 over 5m2s)  cluster-autoscaler  pod didn't trigger scale-up:

The cluster had a single node assigned. Increasing the node count addressed the problem and the remaining ARMO in-cluster pods automatically started once the additional nodes were available.

3. The Kubescape pod restarted

You may find that the Kubescape pod restarted, this means Kubescape probably did not complete the scan. Run the following command:

kubectl -n kubescape get pods | grep kubescape

You should see the following output:

NAME                          READY   STATUS    RESTARTS   AGE
kubescape-744bc74f8c-z9drg    1/1     Running   1          5m38s


Typically the restart is caused by memory issues.

Increase the memory request and CPU

We recommend increasing the memory request and CPU (by editing the Deployment):

kubectl -n kubescape edit deployment kubescape

After the kubescape pod is ready, restart the operator pod:

kubectl -n kubescape rollout restart deployment operator

Re-install the Helm chart

Re-install the Helm chart and manually set the requested memory/CPU (add this to the helm upgrade --install command):

--set kubescape.resources.requests.memory=800Mi \
--set kubescape.resources.limits.memory=1500Mi \
--set kubescape.resources.requests.cpu=500m \
--set kubescape.resources.limits.cpu=1000m

4. Missing Runtime Information

Certain functionalities, such as Network Policy generation and identification of relevant vulnerabilities, rely on data gathered from the application during its runtime.

If the anticipated information is unavailable, we suggest the following steps for users to troubleshoot on their own. Of course, don't hesitate to contact us through Slack or GitHub issues for further assistance.

4.1 Node Agent Monitoring

The component responsible for monitoring applications during runtime is the node-agent DaemonSet.

Verify that the DaemonSet is actively running on all your nodes.

In case the pods are running exclusively on specific nodes, ensure that you update the node-affinity settings to permit the node-agent to operate on all nodes where your applications are active and you intend to monitor.

4.2 Node Agent in Crash Loop

If the node-agent is consistently in a crash loop, there are several potential reasons. We recommend checking the logs, as they typically contain valuable information. Below are some common scenarios:

4.2.1 Unsupported Kernel Version

The node-agent relies on eBPF technology, which is exclusively implemented on Linux kernels. Consequently, this feature is only functional on Linux distributions, and the Linux kernel version on the node must be >= 5.4.

4.2.2 RunC Not Found

To observe system call activity, the node-agent relies on RunC. If the logs suggest that RunC is missing, make sure to include the path as an environment variable. For example, set it as follows: RUNTIME_PATH=/path/to/runc/on/the/node, or during the installation of the kubescape-operator, use: --set global.overrideRuntimePath=/path/to/runc/on/the/node.

An example of the log is provided below:

{"level":"fatal","ts":"2023-12-10T18:21:39Z","msg":"error starting the container watcher","error":"setting up container collection: initializing container collection: starting runc fanotify: no runc instance can be monitored with fanotify. The following paths were tested: /bin/runc,/usr/bin/runc,/usr/sbin/runc,/usr/local/bin/runc,/usr/local/sbin/runc,/usr/lib/cri-o-runc/sbin/runc,/run/torcx/unpack/docker/bin/runc,/usr/bin/crun. You can use the RUNC_PATH env variable to specify a custom path. If you are successful doing so, please open a PR to add your custom path to runcPaths"}

If you aren't sure where runc is located, you can run the following command on the node to find it:

find / -name runc 2>/dev/null

In case you are in an environment where you can't access the node, one solution is to run a privileged pod on the node and run the command from there. To create a privileged pod, run the following command:

 kubectl run --rm -i --tty busybox --image=busybox --restart=Never --overrides='{"apiVersion":"v1","spec":{"containers":[{"name":"busybox","image":"busybox","command":["/bin/sh"],"stdin":true,"tty":true,"securityContext":{"privileged":true},"volumeMounts":[{"mountPath":"/host","name":"host-root"}]}],"volumes":[{"name":"host-root","hostPath":{"path":"/","type":""}}]}}' -- /bin/sh
Bottlerocket users

AWS Bottlerocket nodes are especially well protected, but their configuration is somewhat unique. See a bit more about the problem here.

To be able to run Kubescape Operator with the eBPF node agent on these nodes, use the following configuration in Helm to properly set up runc:

helm install/upgrade ... --set global.overrideRuntimePath=/host/run/containerd/runc --set nodeAgent.seLinuxType=super_t --set nodeAgent.privileged=false

4.3 Restarting Workloads

Internally, the node-agent actively monitors containers from the inception of their life cycle. Our research indicates that over 90% of a container's unique file and network activity occurs within the initial few minutes of startup. Consequently, we opted to commence monitoring only from the container's startup.

In cases where the node-agent pod initiates after the container, the container won't get monitored and, as a result, won't possess any associated runtime information.

To ensure optimal utilization, please restart your workloads after installing the kubescape-operator. This ensures that you maximize the benefits of the monitoring process.

4.4 Data Availability Delay

The node-agent begins monitoring data before processing it, and this process typically takes a few minutes. If you've recently installed the kubescape-operator or restarted the workload, kindly allow a few minutes for the monitoring and processing to commence.

4.5 Other Considerations

Due to the intricacies of this solution, there are a couple of scenarios to be mindful of:

  1. Insufficient Workload Runtime:
    If the workload didn't run for a sufficient duration or if the node-agent underwent a restart, it might lead to the loss of monitored data. Consequently, insights for such workloads may not be available.

  2. Storage Component Issues:
    Another potential issue could arise if the storage component encounters problems, leading to its unavailability. This can result in delayed results and potential data loss. If you encounter such issues, please investigate the status of the storage component for resolution.