T-Mobile’s 0K PII Leak: How One High-Severity Bug Exposed Customer Payment Data—And How You Can Find The Next One + Video

Introduction:

In the hyper-competitive landscape of telecom bug bounty programs, uncovering a vulnerability that exposes Personally Identifiable Information (PII) and payment data is the equivalent of striking gold. The recent achievement by a researcher on T-Mobile’s BugCrowd program highlights a critical truth: even industry giants with massive security budgets struggle to secure complex web applications and APIs. This article dissects the technical anatomy of such a high-severity find, focusing on the pathways that lead to PII exposure—specifically broken access controls and API misconfigurations—and provides a practical roadmap for bug hunters to replicate this success.

Learning Objectives:

Understand the common API and web application vulnerabilities that lead to PII and payment information exposure.
Master the technical process of identifying, exploiting, and reporting IDOR (Insecure Direct Object References) and broken object-level authorization (BOLA) vulnerabilities.
Learn to utilize specific Linux and Windows command-line tools and scripts to automate the discovery of sensitive data leaks in modern web architectures.

You Should Know:

Identifying the Attack Surface: API Enumeration and Analysis

The core of any high-severity bug in a telecom environment lies in the API layer. Modern telecom apps manage user sessions, billing, and account settings through GraphQL or REST APIs. To find the vulnerability, we must first map the attack surface.

Start by intercepting traffic using a proxy like Burp Suite or OWASP ZAP. Focus on endpoints related to account, billing, payment, order, and user. Often, the “competitive” nature of these programs means developers implement client-side security checks but fail to enforce them server-side.

Step‑by‑step guide for API endpoint discovery:

Set up the proxy: Configure your browser to route traffic through Burp Suite. Ensure “Intercept is on.”
Navigate the target app: Log in to your test account. Click through every feature—update profile, view bills, change payment methods.
Analyze the history: In Burp, review the HTTP history. Look for `GET /api/v1/user/12345` or POST /graphql.

Clone endpoints with `gau` (Get All URLs): Use a Linux tool to fetch known URLs from various sources.

Linux command to gather URLs from AlienVault OTX, WaybackMachine, etc.
gau t-mobile.com --subs | grep -E '.(api|graphql|v1|v2)' > endpoints.txt

Filter for parameters: Use `gf` (Gf patterns) to filter for potential IDOR parameters.
```
cat endpoints.txt | gf idor
```
Exploiting Broken Object Level Authorization (BOLA) in GraphQL

The “High Severity Bug” mentioned in the post likely involved a GraphQL endpoint. GraphQL is popular in large-scale apps like T-Mobile because it allows the client to request specific data fields. However, a poorly configured GraphQL API often suffers from BOLA, where an authenticated user can access objects belonging to another user by manipulating an ID.

Step‑by‑step guide for testing GraphQL BOLA:

Introspection query: If introspection is enabled, the entire schema is exposed. Use this to find the `Query` and `Mutation` types.
```
Run this against the GraphQL endpoint
{
__schema {
types {
name
fields {
name
type {
name
}
}
}
}
}
```
Identify sensitive mutations: Look for `getUserPaymentInfo(userId: ID!)` or fetchBillingDetails(accountNumber: Int!).
Modify the request: Create an account (Account A). Use the API to fetch your own data. Capture the request and change the `userId` or `accountNumber` to that of another account (Account B) if you have a second test account. If you don’t have a second account, try incrementing or decrementing the numeric ID.

Automate with ffuf: Use a fuzzer to brute-force numeric IDs against a specific endpoint.

Windows/Linux: Fuzzing for IDOR on a REST endpoint
ffuf -u https://target.com/api/user/FUZZ/payment -w ids.txt -H "Authorization: Bearer YOUR_TOKEN"

Analyze responses: A status `200 OK` with a JSON payload containing payment card numbers (masked or partially unmasked), expiry dates, or billing addresses confirms the vulnerability.

3. Bypassing Parameter-Based Access Controls

Sometimes, developers rely solely on the `id` in the URL or body to determine ownership, neglecting to validate the session token against the requested resource. If you find an endpoint like `https://api.t-mobile.com/v1/billing/statements/{statement_id}`, the application must verify that the `statement_id` belongs to the user associated with the `Authorization` header.

Step‑by‑step guide for access control testing:

Capture concurrent requests: Log in with two different browsers (or browser profiles) representing User A and User B.
Swap identifiers: Take the `statement_id` from User B’s request and paste it into the request for User A. Send the request.
Check for privilege escalation: If you are logged in as a standard user and can see admin endpoints (/admin or /api/v1/admin/users), attempt to access them. If successful, that is a critical privilege escalation.

Linux/Windows command for header manipulation: Use `curl` to test this concept programmatically.

Linux command to test IDOR with curl
curl -X GET "https://target.com/api/account/100002" \
-H "Authorization: Bearer eyJhbGciOiJIUzI1NiIs..." \
-H "Content-Type: application/json" \
-w "\nHTTP Status: %{http_code}\n"

4. Extracting PII via JWT Misconfigurations

A common oversight in telecom apps is the use of weak JSON Web Tokens (JWTs). If the secret key is weak or the algorithm is set to none, an attacker can forge a token to impersonate any user, leading to massive PII exposure.

Step‑by‑step guide for JWT testing:

Decode the token: Use `jwt_tool` or the web-based `jwt.io` to inspect the token payload.
Check the `alg` field: If it’s set to none, you can modify the payload and remove the signature.

Brute-force the secret: If the token uses HS256, use `hashcat` or `john` to crack the secret.

Linux command to crack JWT secret using hashcat
hashcat -m 16500 -a 0 jwt_hash.txt rockyou.txt

Test for privilege escalation: Modify the `sub` (subject) or `user_id` field in the payload to a high-value target like `admin` or `100001` and re-sign the token if possible.

5. Cloud Storage Misconfigurations (S3 Buckets)

Given the scale of T-Mobile, payment information or logs are often stored in cloud storage solutions like AWS S3. A misconfigured bucket could allow listing of objects containing sensitive user data.

Step‑by‑step guide for bucket enumeration:

Look for URLs: In the source code of the web app or JavaScript files, look for references to s3.amazonaws.com, amazonaws.com, or custom domain buckets.
Test bucket listing: Use `awscli` (configured with any credentials or anonymously) to see if listing is enabled.
```
Linux/Windows command to list an S3 bucket anonymously
aws s3 ls s3://t-mobile-payment-logs --no-sign-request
```
Download data: If listing works, recursively download the contents to look for CSV files containing PII.
```
aws s3 cp s3://t-mobile-payment-logs ./downloaded_data --recursive --no-sign-request
```

What Undercode Say:

API Security is the New Perimeter: The T-Mobile high-severity find underscores that traditional network firewalls are irrelevant when APIs are exposed directly to the internet. Bug bounty hunters must prioritize API testing (REST/GraphQL) over front-end UI testing.
Automation + Manual Verification = Success: While tools like ffuf, gau, and `jwt_tool` accelerate the process, the “achievement” comes from understanding the business logic. The researcher likely combined automated parameter discovery with a manual understanding of how payment flows work, enabling them to spot the outlier in the API structure.

Analysis: The reported vulnerability—PII user payment information exposure—is almost always a result of a cascade of failures. It typically starts with a GraphQL resolver that fails to enforce authorization checks, combined with a front-end that sends user identifiers in a predictable format. For defensive teams, this is a wake-up call to implement strict server-side authorization checks regardless of client-side input. For offensive teams, the lesson is clear: focus on the “Billing” and “Settings” sections of any application. These areas, often considered “legacy” or complex, house the most sensitive data and are frequently protected by the weakest logic.

Prediction:

As telecoms like T-Mobile continue to consolidate services and roll out 5G capabilities, the API attack surface will expand exponentially. We will see a shift from simple IDORs to complex chain vulnerabilities combining GraphQL injections with server-side request forgery (SSRF) to pivot into internal payment processing networks. Consequently, bug bounty platforms will likely introduce specialized “API Security” tiers, forcing hunters to master protocol-level fuzzing and business logic abuse to claim top bounties.

▶️ Related Video (70% Match):

🎯Let’s Practice For Free:

IT/Security Reporter URL:

Reported By: Wan Ahmad – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

🔐JOIN OUR CYBER WORLD [ CVE News • HackMonitor • UndercodeNews ]

💬 Whatsapp | 💬 Telegram

📢 Follow UndercodeTesting & Stay Tuned:

𝕏 formerly Twitter 🐦 | @ Threads | 🔗 Linkedin | 🦋BlueSky

Listen to this Post