Reverse Engineering a Web App: The Art of Unraveling Mystery Code

by
NightCafe

Congratulations! You’ve inherited a web app. No documentation, no source code — just a production environment hosted on AWS. If this sounds like the beginning of a tech thriller, you’re not wrong. Let’s dive in and reverse engineer this beast with a mix of humor, curiosity, and some seriously handy tools.

Step 1: Survey the Land (a.k.a. Panic, Then Explore)

Take a deep breath and start mapping out what you do have. Log into AWS and begin piecing together the puzzle:

  1. EC2 Instances — Check what’s running and grab the instance metadata. Is it running Linux? Windows? What’s installed?
  2. RDS — What kind of database are we dealing with? MySQL? PostgreSQL? Go snooping with a read-only connection.
  3. ElastiCache — Redis and Memcached? Look at configurations and key patterns.
  4. ELB — Is there one? Check its routing rules. If not, brace yourself for a single point of failure.

Step 2: If There’s No Source Code, Improvise

When you lack source code, the live environment is your Rosetta Stone. Here’s how to decode:

  • File System Archaeology: SSH into the EC2 instances and poke around. Look for app directories, configs, and logs. These are treasure troves of clues.
  • Database Schemas: Inspect the RDS schema. Table names often tell stories (like users, orders, or ominously named temp_data).
  • Traffic Analysis: Enable ELB access logs and study the traffic patterns. Where are requests going? What parameters are being passed?
  • Runtime Inspection: Use tools like lsof, netstat, and htop to see active processes, network connections, and resource usage.
Deep Dream Generator

Step 3: Bring Out the Big Guns (Tools You’ll Need)

  • AWS CLI and Console: Your best friends for inventory and configuration details.
  • Burp Suite: A powerful proxy for poking at live web traffic.
  • Wireshark: For deeper network dives.
  • Postman: Testing APIs like a pro.
  • Dockerize: If you spot Docker containers, try replicating them locally.
  • Static Analysis Tools: If you find any trace of source code, use tools like SonarQube to audit it.

Step 4: Call in the AI Cavalry

ChatGPT can assist in ways you might not expect:

  • Log Analysis: Paste in logs, and it can help decipher patterns and errors.
  • Code Reconstruction: Feed snippets of decompiled code to get explanations.
  • API Testing: Ask it for API request templates based on observed traffic.
  • Documentation: Generate config documentation or even hypothesized workflows.

Step 5: Ten Questions to Ask Yourself (or the App)

  1. What languages or frameworks are being used? Look for .php, .py, .js, or compiled binaries.
  2. What’s in /etc, /var/log, and /home? These directories are gold.
  3. Who’s using the app, and what’s their traffic pattern?
  4. What’s in the RDS? Any sensitive data to protect?
  5. Are there any hardcoded secrets or API keys?
  6. What’s the backup story? Snapshots? Manual dumps?
  7. Are there third-party integrations? APIs, plugins, or SDKs?
  8. Is the system patched and secure?
  9. Can you find monitoring tools? CloudWatch metrics? Custom dashboards?
  10. How do you safely test changes? Build a staging environment if one doesn’t exist.
ChatGPT

Step 6: Other Tips for the Brave

  • Tag Everything: Add AWS tags to resources as you understand them. Future-you will thank you.
  • Snapshot Everything: Before you tinker, back up the instances and RDS.
  • Build a Dependency Map: Diagram your findings. Tools like Lucidchart can help.
  • Document as You Go: Even if it’s just bullet points in a Markdown file.

A Simplified AI Art Prompt

“An impressionist painting of a single, glowing AWS EC2 instance under a starry night sky, surrounded by abstract representations of databases and caches, symbolizing a lone beacon of legacy technology.”

Grok

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