# User access management on AWS Kubernetes cluster

#### First posted on: 2019/08/23

Categories: infrastructure

# Introduction

When implementing a solution for allowing users other than the cluster creator to access the cluster resources we are faced with two fairly old generic problems - authentication and authorization. There are various ways one can solve these problems. I will discuss one such solution in this post. It makes use of AWS Identity and access management (IAM) features. This in my humble opinion is the simplest and hopefully secure enough solution when it comes to EKS.

# Allowing nodes to join the cluster

Before we discuss human users (and services), I want to discuss how the nodes are able to talk to the master and join the cluster. One of the first things to do when you are setting up an EKS cluster is to setup a special ConfigMap - aws-auth in the kube-system namespace and add the IAM role ARNs to it. This allows the nodes to call home to the master and allow them to be part of the cluster. To make things concrete, here’s how the config map looks like:

apiVersion: v1
data:
mapRoles: |
- rolearn: arn:aws:iam::AWS-ACCOUN-ID:role/myrole
groups:
- system:bootsrappers
- system:nodes
kind: ConfigMap
name: aws-auth
namespace: kube-system



The mapRoles array lists all the IAM roles that we want to allow to authenticate successfully to the cluster. We add the role to the kubernetes groups system:bootstrappers and system:nodes. We have to add all the IAM roles of the nodes in our cluster to this ConfigMap. Once we apply this manifest, you should see the nodes are ready when you run kubectl get nodes again.

The cluster creator gets admin privileges by default. To add other admin users, we will have to update the above ConfigMap as follows:

apiVersion: v1
data:
mapRoles: |
- rolearn: arn:aws:iam::AWS-ACCOUN-ID:role/myrole
groups:
- system:bootsrappers
- system:nodes
mapUsers: |
groups:
- system:masters
kind: ConfigMap
name: aws-auth
namespace: kube-system


You have different teams working on different projects who need varying levels of access to the cluster resources. First of all, we want to have each project and environment in their own Kubernetes namespace - that’s how we define the perimeter and granularity of our permissions. Let’s assume:

1. Our project name is projectA
2. Our environments are qa, staging and production
3. Our namespaces are - projectA-qa, and projectA-staging and projectA-production

We can follow the approach for adding additional admin users above and list each user, assign them to different project groups in Kubernetes and then regulate access based on their group and Kubernetes role bindings. This is how it might look like.

First, we update the ConfigMap to add new entry per user in the mapUsers section as follows:

apiVersion: v1
data:
...
mapUsers: |
groups:
- system:basic-user
- projectA:qa

groups:
- system:basic-user
- projectA:qa
...
..


We add each user to the system:basic-user group which “Allows a user read-only access to basic information about themselves” and added them to two other projectA specific groups.

The above ConfigMap update coupled with the “right” kubeconfig and AWS CLI configuration will allow users, username1 and username2 to authenticate to the EKS cluster successfully. For completeness, a working kubeconfig will look as follows:

apiVersion: v1
current-context: k8s-cluster
clusters:
- cluster:
certificate-authority-data: <ca data>
server: <EKS endpoint>
name: k8s-cluster
contexts:
- context:
cluster: k8s-cluster
namespace: projectA-qa
kind: Config
users:
user:
exec:
apiVersion: client.authentication.k8s.io/v1alpha1
command: aws-iam-authenticator
args:
- token
- -i
- k8s-cluster


However, to allow them to access project A specific resource, we will first create a Role and then a RoleBinding to associate the projectA:qa group above with the role.

The manifest for the Role looks as follows:

kind: Role
name: projectA-qa-human-users
namespace: projectA-qa
rules:
- apiGroups:
- ""
resources:
- services
verbs:
- get
- apiGroups:
- extensions
- apps
resources:
- deployments
verbs:
- get
- apiGroups:
- batch
resources:
- cronjobs
- jobs
verbs:
- get
- apiGroups:
- ""
resources:
- pods
verbs:
- get
- list
- apiGroups:
- ""
resources:
- pods/exec
verbs:
- create
- apiGroups:
- ""
resources:
- pods/log
verbs:
- get



The RoleBinding is as follows:

apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
name:  projectA-qa-human-users
namespace: projectA-qa

roleRef:
apiGroup: rbac.authorization.k8s.io
kind: Role
name: projectA-qa-human-users
subjects:
- apiGroup: rbac.authorization.k8s.io
kind: Group
name: projectA:qa
namespace: projectA-qa


The key bit of information that ties in an AWS user with a certain set of permissions in the cluster is the assignment to a group in the ConfigMap and the group assigment in the above role binding.

So:

                                                                    Role with certain permissions
/ \
|
AWS user/IAM role -> Assigned to a cluster group via ConfigMap -> Role binding associates a role to the group


With the above setup, you have successfully granted username1 access to your cluster and they are confined to the projectA-qa namespace where they can only exec into pods and view the pods’ logs. If you wanted to allow username1 access to other projectA’s environments or other projects’ environments, you would do the following:

• Update ConfigMap to assign username1 to the different groups
• Create Roles in your cluster corresponding to your different projects’ namespaces
• Create RoleBindings in your cluster corresponding the different groups and roles

For adding new users such as other team members on the same project or different project members, you would essentially repeat the process - add new user and assign them to groups.

For example:

apiVersion: v1
data:
...
mapUsers: |
groups:
- system:basic-user
- projectA:qa

groups:
- system:basic-user
- projectA:qa

groups:
- system:basic-user
- projectB:qa

groups:
- system:basic-user
- projectB:qa

..
...
..


An alternative to adding each individual user to the ConfigMap is to use IAM roles per project environment. So, to replicate the above using IAM roles, we would do the following:

apiVersion: v1
data:
mapRoles: |
- rolearn: arn:aws:iam::AWS-ACCOUN-ID:role/projectA-qa-humans
groups:
- system:basic-user
- projectA:qa
- rolearn: arn:aws:iam::AWS-ACCOUN-ID:role/projectB-qa-humans
groups:
- system:basic-user
- projectB:qa
...



We don’t add the individual user accounts any more. So, how do the individual users authenticate themselves to the cluster and then access relevant resources? We use the AssumeRole functionality to do so. An example kubeconfig will now look like:

apiVersion: v1
current-context: k8s-cluster
clusters:
- cluster:
certificate-authority-data: <ca data>
server: <EKS endpoint>
name: k8s-cluster
contexts:
- context:
cluster: k8s-cluster
namespace: projectA-qa
kind: Config
users:
user:
exec:
apiVersion: client.authentication.k8s.io/v1alpha1
command: aws-iam-authenticator
args:
- token
- -i
- k8s-cluster
- -r
- arn:aws:iam::AWS-ACCOUN-ID:role/projectA-qa-humans



If we compare it to the previous kubeconfig, the change is additional two arguments to aws-iam-authenticator to the end. -r says that we want to assume a role when fetching the token that we use to authenticate to the cluster. The role we want to assume here is the role which we have added to the ConfigMap above instead of individual users. To allow users to assume this role, we will need to do a couple of things.

Allow the IAM role projectA-qa-humans to be assumed by everyone in the AWS account:

{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": {
"AWS": "arn:aws:iam::<AWS-ACCOUNT-ID>:root"
},
"Action": "sts:AssumeRole"
}
]
}


And then allow user’s accounts to assume this role via this policy:

{
"Version": "2012-10-17",
"Statement": {
"Effect": "Allow",
"Action": "sts:AssumeRole",
"Resource": "arn:aws:iam::<AWS-ACCOUNT-ID>:role/projectA-humans"
}
}


## Which AWS user performed this API operation

Let’s say we have rolled out the idea of using a single IAM role per project environment which the project’s team members use (via AssumeRole) to access and perform operations in the Kubernetes cluster. One question you will soon encounter is how do you identify which actual AWS user was performing the operation? Currently, aws-iam-authenticator doesn’t support this. However, we can write our own solution by reading Kubernetes logs and leveraging AWS CloudTrail.

### API server audit logs

The specific EKS log stream we are interested in is kube-apiserver-audit. Entires in this log stream are similar to:

{
"kind": "Event",
"apiVersion": "audit.k8s.io/v1beta1",
"creationTimestamp": "2019-08-22T00:13:15Z"
},
"level": "Request",
"timestamp": "2019-08-22T00:13:15Z",
"stage": "ResponseComplete",
"requestURI": "/api/v1/namespaces/projectA-qa/pods?limit=500",
"verb": "list",
"user": {
"uid": "heptio-authenticator-aws:<AWS-ACCOUNT-ID>:AROAUBGCRZPAQIISY7KAL",
"groups": [
"system:basic-user",
"projectA:qa",
"system:authenticated"
]
},
"sourceIPs": [
"10.0.57.37"
],
"userAgent": "kubectl/v1.12.7 (linux/amd64) kubernetes/6f48297",
"objectRef": {
"resource": "pods",
"namespace": "projectA-qa",
"apiVersion": "v1"
},
"responseStatus": {
"code": 200
},
"stageTimestamp": "2019-08-22T00:13:15.589325Z",
"annotations": {
"authorization.k8s.io/decision": "allow",
"authorization.k8s.io/reason": "RBAC: allowed by RoleBinding \"projectA-qa-human-users/projectA-qa\" of Role \"projectA-qa-human-users\" to Group \"projectA:qa\""
}
}


Our main interest in the above log is the user object and it’s fields - uid and username. The username is composed of two parts - a hardcoded projectA-qa and a generated session name - 1566432791140199108. This was specified in the username field of the ConfigMap (username: projectA-{{SessionName}}). The uid field is set to "heptio-authenticator-aws:<AWS-ACCOUNT-ID>:AROAUBGCRZPAQIISY7KAL". The two key bits of data here that we will use to query CloudTrial are the strings AROAUBGCRZPAQIISY7KAL and 1566432791140199108.

### CloudTrail

A CloudTrail event whose EventName is AssumeRole has the following structure:

{
AccessKeyId: "AKKKLKLJLJLJLLHLHLHL",
CloudTrailEvent: "...",
EventName: "AssumeRole",
EventSource: "sts.amazonaws.com",
EventTime: 2019-08-22 00:13:12 +0000 UTC,
Resources: [
{
ResourceName: "AKHKHKLJLHLJLLHHLHLHL",
ResourceType: "AWS::IAM::AccessKey"
},
{
ResourceName: "1566432791140199108",
ResourceType: "AWS::STS::AssumedRole"
},
{
ResourceName: "AROAUBGCRZPAQIISY7KAL:1566432791140199108",
ResourceType: "AWS::STS::AssumedRole"
},
{
ResourceName: "arn:aws:sts::AWS-ACCOUNT-ID:assumed-role/projectA-qa-humans/1566432791140199108",
ResourceType: "AWS::STS::AssumedRole"
},
{
ResourceName: "arn:aws:iam::AWS-ACCOUNT-ID:role/projectA-qa-humans",
ResourceType: "AWS::IAM::Role"
}
],
}


In the above event, if you see the third entry in the Resources array, you can see that the ResourceName is basically composed of our two strings of interest from the kubeserver audit logs. Thus, if we search for CloudTrail AssumeRole events for this ResourceName, we will have our actual AWS user who performed a specific operation in the Username field.

You can write your own script for this. I implemented this in my hobby AWS CLI project yawsi.

The interface looks like:

$yawsi eks whois --uid heptio-authenticator-aws:<user-id>:AROAUBGCRZPAQIISY7KAL --username projectA-qa-1566432791140199108 --lookback 6  The --lookback parameter specifies the number of hours of CloudTrail events to look back to. ## Automating kubeconfig management for human users To allow human users to access the kubernetes cluster in a setup where we use a IAM role per project and environment, there are a few steps involved: • An AWS account • Setup the AWS account with the right permissions (described below) • Give them the EKS cluster endpoint and certificate authority data • Generate a kubeconfig context per project environment Once we have created the AWS account for an user with the right permissions, we can allow the users to configure their own kubeconfig files using a tool - this is better than emailing them configuration files or walking up to them. Let’s talk about the permisions which also allows us to look into the steps involved. The first thing the user needs to do is be able to query AWS for a specific cluster name. This gives us the certificate authority data and the cluster endpoint. However, if you are using a private EKS cluster, you will also need to account for this issue where the cluster endpoint DNS is not resolvable from outside the cluster. The solution I have decided to go forward is to create an /etc/hosts entry with the IP address which we find by query the network interfaces in AWS. Once we have got all the information we need to talk to the cluster, the remaining step is to generate the different project environment specific kubeconfig contexts. To generate the project environment specific kubeconfig contexts, we need to lookup the IAM role ARN that we want to assume while authenticating ourselves to the cluster. The conventions that I am currently following which I have referred to previously is that the IAM role which users assume are named as: <project name>-<environment>-humans. The following IAM policy gives all these permissions: { "Version": "2012-10-17", "Statement": [ { "Sid": "NI1", "Effect": "Allow", "Action": "ec2:DescribeNetworkInterfaces", "Resource": "*" }, { "Sid": "EKS1", "Effect": "Allow", "Action": [ "eks:ListUpdates", "eks:DescribeUpdate", "eks:DescribeCluster", "eks:ListClusters" ], "Resource": "*" }, { "Sid": "IAM1", "Effect": "Allow", "Action": [ "iam:GetRole" ], "Resource": "arn:aws:iam::AWS-ACCOUNT-ID:role/*-humans" } ] }  And ofcourse, we need to allow the user to assume the project environment specific role: { "Version": "2012-10-17", "Statement": { "Effect": "Allow", "Action": "sts:AssumeRole", "Resource": "arn:aws:iam::AWS-ACCOUNT-ID:role/projectA-qa-humans" } }  (Instead of managing these permissions for individual users, I am using AWS user groups and assigning users to relevant groups and managing policies at the group level). I have implemented this in the yawsi project. To create a kubeconfig context if you want to access the EKS cluster as a individual AWS user: $ yawsi eks create-kube-config --cluster-name <your-cluster-name>
Kubeconfig written

--------------------------/etc/hosts/ file entry ---------------------

<ip> <EKS cluster endpoint>


To create a kubeconfig context if you want to access the EKS cluster by assuming another role which follows the specified convention above:

\$ yawsi eks create-kube-config --cluster-name <your-cluster-name> --project projectA --environment qa
Kubeconfig written

--------------------------/etc/hosts/ file entry ---------------------

<ip> <EKS cluster endpoint>


Checkout the other eks related commands.

# Non human users

For non-human users, we can once again leverage IAM roles for authentication and groups and role bindings for authorization. I will discuss two scenarios which brings to light two different use cases.

## Deployment of applications

Let’s consider a scenario where we use Jenkins running outside the cluster to build and deploy applications to our kubernetes cluster. Simply because of the operations that Jenkins will need to perform on the cluster, it will need a very large set of permissions which will cross any project and environment specific permiters we have set in our cluster such as namespaces. Hence, if we assign an IAM role to the Jenkins build instances, add the role to the ConfigMap as above and assign the various groups to it, we will end up with almost admin level access to the cluster. We do want to avoid this scenario by making it slightly more complicated.

We wil use the IAM role for the authentication to the cluster. However, we will use separate service accounts per project environment and then use the corresponding credentials when performing operations on a specific project environment. A service account will only have permissions to perform operations in a specific namespace.

Let’s see an example of creating a service account, creating a role with permissions to perform operations one would usually need to perform deployments, and then creating a role binding with this service acccount:

apiVersion: v1
kind: ServiceAccount
name: jenkins-projectA
namespace: projectA-qa
---
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
name: jenkins-projectA
namespace: projectA-qa
rules:
- apiGroups:
- ""
resources:
- services
verbs:
- '*'
- apiGroups:
- extensions
- apps
resources:
- deployments
verbs:
- '*'
- apiGroups:
- batch
resources:
- cronjobs
- jobs
verbs:
- '*'
- apiGroups:
- ""
resources:
- pods
verbs:
- get
- list
- apiGroups:
- ""
resources:
- pods/log
verbs:
- get
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: RoleBinding
name: jenkins-projectA-role-binding
namespace: projectA-qa
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: Role
name: jenkins-projectA
subjects:
- kind: ServiceAccount
name: jenkins-projectA
namespace: projectA-qa


Once the above service account is created, we can then get the token corresponding to the service account and then use that when performing operations on the cluster for a project via kubectl --token <token>.

For completeness, the ConfigMap entry would like this:

apiVersion: v1
data:
mapRoles: |
- rolearn: arn:aws:iam::AWS-ACCOUNT-ID:role/Jenkins
groups:
- system:basic-user
...


Let’s consider a scenario where you want to run some software outside the cluster which will need to make API calls to the cluster to read various information - example, for monitoring. In this case, we can use an approach similar to we do for human non-admin users:

apiVersion: v1
data:
mapRoles: |
- rolearn: arn:aws:iam::AWS-ACCOUNT-ID:role/Monitoring
groups:
- monitoring


We augment this with a ClusterRole and ClusterRoleBinding as follows:

# Role defined here
....

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
name: monitoring
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: monitoring-role
subjects:
- apiGroup: rbac.authorization.k8s.io
kind: Group
name: monitoring


# Conclusion

In this post, I have discussed how we can leverage AWS Identity and Access Management features for authentication and authorization in an AWS EKS cluster setup. With the right amount of convention and automation, we can come up with a simple and easy to understand and reason approach. Time will tell how this scales though.