Cognito authentication integration with Django using authorization code grant.

Note: Assumed knowledge of AWS Cognito backend configuration and underlying concepts, mostly it’s just the setup from an application integration perspective that is talked about here.

Recently we have been working on a Django project where a secure and flexible authentication system was required, as most of our existing structure is on AWS we chose Cognito as the backend.

Below are the steps we took to get this working and some insights learned on the way.

Django Warrant

The first attempt was using django_warrant, this is probably going to be the first thing that comes up when you google ‘how to django and cognito’.

Django_warrant works by injecting an authentication backend into django which does some magic that allows your username/password to be submitted and checked against a configured user pool, on success it authenticates you and if required creates a stub django user.

The basics of this were very easy to get working and integrated but had a few issues such as:

  • We still see username/password requests and have to send them on.
  • By default can only be configured for one user pool.
  • Does not support federated identity provider workflows.
  • Github project did not seem super active or updated.

Ultimately we chose not to use this module, however inspiration was taken from its source code to do some of the user handling stuff we implemented later on.

Custom authorization_code workflow implementation

This involves using the cognito hosted login form, which does both user pool and connected identity provider authentication (O365/Azure, Google, Facebook, Amazon) .

The form can be customised with HTML, CSS, images and put behind a custom URL, other aspects of the process and events can be changed and reacted upon using triggers and lambda.

Once you are authenticated in cognito it redirects you back to the page of your choosing (usually your applications login page or custom endpoint) with a set of tokens, using these tokens you then grab the authenticated users details and authenticate them within the context of your app.

The difference between authorization code grant and implicit grant are:

  • Implicit grant
    • Intended for client side authentication (javascript applications mostly)
    • Sends both the id_token (JWT) and acccess_token in the redirect response
    • Sends the tokens with an #anchor before them so it is not seen by the web server
    • https://your-app/login#id_token=n&auth_token=n
  • Authorization code grant
    • Intended for server side authentication
    • Sends a authorization code in the redirect response
    • Sends this as a normal GET parameter
    • https://your-app/login?code=n
    • Your application holds a preconfigured secret
    • Code + secret get turned into id_token token and access_token via oauth2/token endpoint

We chose to use the authorization code grant workflow, it takes a bit more effort to setup but is generally more secure and alleviates any hacky javascript shenanigans that would be needed to get implicit grant working with a django server based backend.

After these steps you can use boto3 or helpers to turn those tokens into a set of attributes (email, name, other custom attributes) kept by cognito. Then you simply hook this up to your internal user/session logic by matching them with your chosen attributes like email, username etc.

I was unable to find any specific library support to handle some aspects of this, like the token handling in python or the django integration so i have included some code which may be useful.

Code

This can be integrated into a view to get the user details from Cognito based on a token, this will be sitting at the redirect URL that cognito returns from.

import warrant
import cslib.aws

def tokenauth(request):
    authorization_code = request.GET.get("code")
    token_grabber = cslib.aws.CognitoToken(
        <client_id&gt;
        <client_secret&gt;
        <domain&gt;
        <redir&gt;
        <region&gt;?
    )

    id_token, access_token = token_grabber.get(authorization_code)

    if id_token and access_token:
        # This uses warrant (different than django_warrant)
        # A helper lib that wraps cognito
        # Plain boto3 can do this also.  
        cognito = warrant.Cognito(
            <user_pool_id&gt;
            <client_id&gt;
            id_token=id_token,
            access_token=access_token,
        )

        # Their lib is a bit broken, because we dont supply a username it wont
        # build a legit user object for us, so we reach into the cookie jar....
        # {'given_name': 'Joe', 'family_name': 'Smith', 'email': 'joe@jtwo.solutions'}
        data = cognito.get_user()._data
        return data
    else:
        return None

Class that handles the oauth/token2 workflow, this is mysteriously missing from the boto3 library which seems to handle everything else quite well…

from http.client import HTTPSConnection
from base64 import b64encode
import urllib.parse
import json

class CognitoToken(object):
    """
    Why you no do this boto3...
    """
    def __init__(self, client_id, client_secret, domain, redir, region="ap-southeast-2"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.redir = redir
        self.token_endpoint = "{0}.auth.{1}.amazoncognito.com".format(domain, region)
        self.token_path = "/oauth2/token"

    def get(self, authorization_code):
        headers = {
            "Authorization" : "Basic {0}".format(self._encode_auth()),
            "Content-type": "application/x-www-form-urlencoded",
        }

        query = urllib.parse.urlencode({
                "grant_type" : "authorization_code",
                "client_id" : self.client_id,
                "code" : authorization_code,
                "redirect_uri" : self.redir,
            }
        )

        con = HTTPSConnection(self.token_endpoint)
        con.request("POST", self.token_path, body=query, headers=headers)
        response = con.getresponse()

        if response.status == 200:
            respdata = str(response.read().decode('utf-8'))
            data = json.loads(respdata)
            return (data["id_token"], data["access_token"])

        return None, None

    def _encode_auth(self):
        # Auth is a base64 encoded client_id:secret
        string = "{0}:{1}".format(self.client_id, self.client_secret)
        return b64encode(bytes(string, "utf-8")).decode("ascii")

Further reading


Tagging EC2 EBS Volumes in Auto Scaling Groups

Tagging becomes a huge part of your life when in the public cloud. Metadata is thrown around like hotcakes, and why not. At cloudstep.io we preach the ways of the DevOps gods and especially infrastructure as code for repeatable and standardised deployments. This way everything is uniform and everything gets a TAG!

I ran into an issue recently where I would build an EC2 instance and capture the operating system into an AMI as part of a CloudFormation stack. This AMI would then be used as part of a launch configuration and subsequent auto scaling group. The original EC2 instance had every tag needed across all parts that make up the virtual machine including:

  • EBS root volume
  • EBS data volumes
  • Elastic Network Interfaces (ENI)
  • EC2 Instance itself

When deploying my auto scaling group all the user level tags I’d applied had been removed from the volumes and ENI. This caused a few issues:

  1. EBS volumes couldn’t be tagged for billing.
  2. EBS volumes couldn’t be snapped based on tag level policies in Lifecycle Manager.
  3. Objects didn’t have a ‘Name’ tag which made it hard in the console to understand which virtual machine instance the object belonged too.

There are two methods I derived to add my tags back that I’ll share with you. The tags needed to be added upon launch of the instance when the auto scaling group added a server. The methods I used were:

  1. The auto scaling group has a Launch Configuration where the ‘User data’ field runs a script block at startup.
  2. Initiate a Lambda whenever CloudTrail logged an API reference of a launch event of an instance using CloudWatch.

Tagging with the User Data property and PowerShell

User data is simply:

When you launch an instance in Amazon EC2, you have the option of passing user data to the instance that can be used to perform common automated configuration tasks and even run scripts after the instance starts. You can pass two types of user data to Amazon EC2: shell scripts and cloud-init directives.

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html

Try {
 # Use the metadata service to discover which instance the script is running on
 $InstanceId = (Invoke-WebRequest '169.254.169.254/latest/meta-data/instance-id').Content
 $AvailabilityZone = (Invoke-WebRequest '169.254.169.254/latest/meta-data/placement/availability-zone').Content
 $Region = $AvailabilityZone.Substring(0, $AvailabilityZone.Length -1)
 $mac = (Invoke-WebRequest '169.254.169.254/latest/meta-data/network/interfaces/macs/').content
 $URL = "169.254.169.254/latest/meta-data/network/interfaces/macs/"+$mac+"/interface-id"
 $eni = (Invoke-WebRequest $URL).content
# Get the list of volumes attached to this instance
 $BlockDeviceMappings = (Get-EC2Instance -Region $Region -Instance $InstanceId).Instances.BlockDeviceMappings
 $Tags = (Get-EC2Instance -Region $Region -Instance $InstanceId).Instances.tag

 }
Catch{Write-Host "Could not access the AWS API, are your credentials loaded?" -ForegroundColor Yellow}
$BlockDeviceMappings | ForEach-Object -Process {
        $volumeid = $_.ebs.volumeid # Retrieve current volume id for this BDM in the current instance
        # Set the current volume's tags
        $Tags | ForEach-Object -Process {
        If($_.Key -notlike "aws:*"){
        New-EC2Tag -Resources $volumeid -Tags @{ Key = $_.Key ; Value = $_.Value } # Add tag to volume
        }
        }
}
# Set the current nics tag
$Tags | ForEach-Object -Process {
  If($_.Key -notlike "aws:*"){
        New-EC2Tag -Resources $eni -Tags @{ Key = $_.Key ; Value = $_.Value } # Add tag to eni
  }
}


This script block is great and works a treat with newly created instances from an Amazon Marketplace AMI’s e.g. a vanilla Windows Server 2019 template. The launch configuration would apply the script as a part of the cfn-init function at startup. Unfortunately I’d already used the cfn-init function as part of the original image customisation and capture, the cfn-init would not re-run and didn’t execute this script block. So back to the drawing board in my scenario.

Tagging with CloudWatch and Lambda Function

The second solution was to create a Lambda function and trigger it using an Amazon CloudWatch Events rule. The Instance ID is parsed from the CloudWatch event in JSON to the Lambda function.

Here is the Lambda function that is written in python2.7 and leverages the boto3 and JSON modules.



from __future__ import print_function
import json
import boto3
def lambda_handler(event, context):
  print('Received event: ' + json.dumps(event, indent=2))
  ids = []
  try:
      ec2 = boto3.resource('ec2')
      items = event['detail']['responseElements']['instancesSet']['items']
      for item in items:
        ids.append(item['instanceId'])
      base = ec2.instances.filter(InstanceIds=ids)
      for instance in base:
        ec2tags = instance.tags
        tags = [n for n in ec2tags if not n["Key"].startswith("aws:") ]
        print('   original tags:', ec2tags)
        print('   applying tags:', tags)
        for volume in instance.volumes.all():
          print('    volume:', volume)
          if volume.tags != ec2tags:
            volume.create_tags(DryRun=False, Tags=tags)
        for eni in instance.network_interfaces:
          print('    eni:', eni)
          eni.create_tags(DryRun=False, Tags=tags)
      return True
  except Exception as e:
    print('Something went wrong: ' + str(e))
    return False