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Mohammad Waseem
Mohammad Waseem

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Automating Authentication Flows in Kubernetes: A Lead QA Engineer’s Experience Without Documentation

#kubernetes #qa #automation

Introduction

Automating authentication (auth) flows is a critical aspect of modern CI/CD pipelines, especially in complex microservices environments orchestrated with Kubernetes. As a Lead QA Engineer stepping into automation without comprehensive documentation, I faced a unique set of challenges that required a strategic approach to reverse-engineer existing processes and develop reliable, scalable tests.

Understanding the Environment

The first step was to establish a clear understanding of the current auth flow architecture. Without proper documentation, I relied heavily on inspecting Kubernetes resources, API endpoints, and network traffic. By inspecting existing manifests, ConfigMaps, and Secrets, I identified critical components like OAuth providers, JWT tokens, and session management services.

# Example of inspecting Kubernetes secrets
kubectl get secrets -n auth

# Output may include tokens, OAuth client secrets, or certificate info
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Simultaneously, I used tools like kubectl logs and kubectl describe to trace the flow of auth requests through various microservices.

kubectl logs -n auth deployment/auth-service

# Following the logs helps identify request handling and error points
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Reverse Engineering the Auth Flow

With a collection of data points, I mapped out the flow: from initial login request, redirect to OAuth provider, token exchange, JWT issuance, and session storage. My focus was on discovering endpoints, request formats, and response handling.

I also used curl and Postman to manually test these endpoints, observing headers, payloads, and response statuses.

curl -X POST https://auth.example.com/token \
  -H "Content-Type: application/json" \
  -d '{"username":"testuser","password":"pass"}'

# Result: access token and refresh token
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Developing the Automation Strategy

Next, I crafted a plan to automate these flows. This involved creating scripts that could simulate user login, token refresh, and logout processes, ensuring the tests could run end-to-end.

Sample Python snippet using requests:

import requests

def authenticate(username, password):
    url = "https://auth.example.com/token"
    payload = {"username": username, "password": password}
    response = requests.post(url, json=payload)
    if response.ok:
        return response.json()['access_token']
    else:
        raise Exception("Auth failed")

# Automated login flow
token = authenticate("testuser", "pass")
print(f"Received Token: {token}")
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Automating within Kubernetes

To integrate this into Kubernetes, I created a Job manifest that runs the automation scripts periodically or on-demand.

apiVersion: batch/v1
kind: Job
metadata:
  name: auth-flow-automation
spec:
  template:
    spec:
      containers:
      - name: auth-automator
        image: myrepo/auth-automator:latest
        args: ["python", "automation.py"]
      restartPolicy: Never
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This approach facilitates CI integration, enabling regular testing of auth flows.

Conclusion

Without proper documentation, reverse-engineering auth processes in Kubernetes demands a mix of network analysis, manual testing, and systematic mapping. Automating these flows ensures the reliability of authentication systems, especially as they evolve or scale. Key to success was leveraging Kubernetes native tools, scripting, and continuous verification. This experience underscores the importance of maintaining up-to-date documentation and adopting observability best practices to streamline future automation efforts.

🛠️ QA Tip

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