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September 1, 2026

From Bug to Schema: Exploring Error-Based SQL Injection on an Authenticating Portal

Case Study Analysis of how I identified an Error-Based SQL Injection vulnerability in a production environment via (VDP).

By 0x7ipher

4 min read

Case Study Analysis of how I identified an Error-Based SQL Injection vulnerability in a production environment via (VDP).

Disclaimer: This paper is only intended for informative and educational use. The vulnerability described here in has been identified by a Vulnerability Disclosure Program (VDP) during a designated testing period. All sensitive information that includes identifiers, domain names, and any other organization-related information has been properly anonymized or substituted with generic terms to comply with non-disclosure requirements and target privacy. No data modification has occurred in the course of anonymizing the test data.

Step 1: Starting from Dorking (The Entry Point)

When performing a vulnerability assessment through a Vulnerability Disclosure Program (VDP), my first task was to map the attack surface of the target. To discover the unknown or unreported assets, I resorted to passive reconnaissance with the help of search engine optimization filters, famously called "Google Dorking".

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Step 2: Breaking the Query Logic with a Single Quote

SQL injection lifecycle always starts by identifying the spaces where inputs will interface directly with the database interpreter. In the process of normal mapping, I attacked the well-known input parameter space (Username field).

To test how the backend handled unexpected inputs, I entered a single quotation mark ('):

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The error showed that the application had used dynamic query generation by simply using strings (e.g., SELECT * FROM users WHERE user = 'INPUT'). Through entering an odd number of quotation marks, the interpreter would crash, giving an instant response that no input escaping whatsoever was implemented.

Step 3: Repairing the Syntax Structure

Before any extraction of information, the researcher must demonstrate his/her mastery of the query stream logic by correcting the faulty syntax logic.

This was done by commenting out the remainder of the developer's code using the SQL inline comment syntax ( - - ) as shown below:

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This verified the execution alignment. With the help of a close-quote along with a backend comment string, I was able to manage the state of operation of the web application database stream.

Step 4: Triggering Context Discovered via Data Conversions

Due to the fact that the database engine was generating detailed structural errors to the client's browser, I opted for the Error Based Extraction methodology.

This is achieved by making the database engine perform the query and fetch the text value and then attempt to change the text value to some other incompatible data type, such as INT. This causes the application to fail, and the result string value will appear inside the error message returned by the database.

Without inspecting the user's data, to confirm the system context, I accessed the environment configuration variables:

Input: ' AND 1=CONVERT(INT, @@version)--

This payload triggers a type conversion error in Microsoft SQL Server when attempting to convert its text version string to an integer. In case the application shows any database errors, the database version is disclosed in the error message.
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This same payload was replicated through environmental functions to validate the schema database name and the role privileges of the operational user without accessing any application logs.

Step 5: Proving Data Extraction Legally (Schema Mapping)

For the engagement rules for this particular VDP, it was necessary that the researches prove their ability to retrieve data for high impact triaging.

In order to do this responsibility ethically, I steered clear of using any queries to look for credential or account tables. Rather, I sent my data-type conversion payloads to the system's own system object indices (sysobjects and syscolumns) to demonstrate that I could read data structures out-of-band.

Enumerating Table Assets:

Input: ' AND 1=CONVERT(INT,(SELECT TOP 1 name FROM sysobjects WHERE xtype='U'))--

The payload causes an SQL Server type conversion exception by attempting to convert the first user-defined table name to an integer value. In case there are database exceptions shown in the application, the name of a database table is exposed through the error message.
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Enumerating Column Assets:

Using the obtained table name, I asked for structural attribute lists:

Input: ' AND 1=CONVERT(INT,(SELECT TOP 1 name FROM syscolumns WHERE id = OBJECT_ID('Target_Table')))--

The payload causes an SQL Server type conversion exception by attempting to convert the first user-defined table name to an integer value. In case there are database exceptions shown in the application, the name of a database table is exposed through the error messag
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Conclusion & Responsible Reporting

Here, I was sure that arbitrary read impact had been successfully tested to its fullest extent. I made screenshots of all steps done, structured the payloads, and identified the environmental context parameters.

I formatted the findings using the Common Vulnerability Scoring System (CVSS v3.1), determining an objective base score of 9.8 (Critical Severity) due to the unauthenticated public entry point and total confidentiality compromise.

Key Takeaway for Readers

In demonstrating the SQL Injection vulnerability impact in bug bounties, avoid extracting any data from the production database. It is only sufficient to reveal database names or system configuration defaults to get a Critical level validation without crossing any laws in Safe Harbor provisions.

Thank you for reading. I hope you enjoyed this story and found it helpful.😊

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