Yo, welcome back, everyone, and Happy New Year!
While reviewing my notes from some red team research to improve my day job playbook, I stumbled upon a few old bug bounty reports I submitted over the past year. With some free time on hand, I thought it would be worthwhile to share one of those high-severity findings from a private program.
To give you some context, the target organization is a global leader in men’s and women’s fashion accessories, operating in over 150 countries worldwide. This highlights that even major organizations with vast resources can still have critical and high-severity vulnerabilities in their systems.
Identification
To kick things off, I began my usual reconnaissance on the target and identified an intriguing subdomain. From there, I started manually probing the target for additional insights. One of my go-to techniques is leveraging the Wayback Machine to uncover previously used URIs that might still be relevant to the application. Using this method, I discovered an /invoice-download endpoint that included what appeared to be a token as a parameter, along with a redirection URL.
To my surprise, navigating to the URL immediately worked, and I was able to retrieve a PDF invoice. The document contained sensitive personally identifiable information (PII), including the customer’s full name, address, items purchased, purchase order details, delivery information, and payment details. Below is a snippet of redacted information I identified.
Raising Impact
To increase the impact of my findings, I began exploring ways to enumerate additional customer invoices. Using Burp Suite, a web proxy tool, I analyzed the application’s behavior further and discovered JavaScript in the application’s response. This script contained a URL and an authorization token used to request and retrieve customer invoices.
Leveraging this information, I wrote a Python script to systematically test potential invoice numbers. The script allowed me to determine which invoice numbers were valid and retrieve their corresponding data, significantly broadening the scope of the vulnerability.
After analyzing the results of my Python script, I was able to retrieve and screenshot a few additional invoices as evidence. I promptly reported the vulnerability to the organization’s security team to ensure swift remediation.
Conclusion
Once I confirmed that I could retrieve over 20,000 invoices, I immediately reported the vulnerability to the organization’s security team. Thanks to their swift response, the issue was successfully mitigated.
I hope you enjoyed this blog post! I ended up having to create another X account because I was no longer able to login to my old one for some odd reason and X ended up flagging it. So if you enjoyed this post, feel free to follow my new account at — @jbooogie15
Again, thanks for taking the time to read the post.
Until next time, cheers!
Key Takeaways
- Stick to Your Methodology:
Everyone has their own approach to attacking and analyzing systems, and that’s perfectly fine. What works for me might not work for you, and vice versa. The key is to develop a methodology that suits your style, refine it over time, and trust the process. - Manual Testing is King:
Automated tools can only take you so far. The ability to manually probe targets, read and interpret JavaScript, and understand what an application is designed to do gives you an advantage. By creating a threat model for the system, you can attack it more effectively and uncover more severe vulnerabilities. - Vulnerabilities Exist Everywhere:
No matter how big or small the organization, vulnerabilities will always exist. It’s just a matter of when — and who — discovers them.
