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CVE Vulnerability Database

CVE-2025-9833: Online Farm Management System SQLi Flaw

CVE-2025-9833 is a SQL injection vulnerability in Donbermoy Online Farm Management System 1.0 affecting the login.php file. Attackers can exploit this remotely to compromise databases. This article covers technical details, affected versions, impact, and mitigation strategies.

Published:

CVE-2025-9833 Overview

A SQL Injection vulnerability has been identified in SourceCodester Online Farm Management System version 1.0. This vulnerability exists in the login functionality within the /Login/login.php file, where the uname parameter is susceptible to SQL injection attacks due to improper input validation. An attacker can exploit this vulnerability remotely without authentication to manipulate database queries, potentially compromising the confidentiality, integrity, and availability of the application's data.

Critical Impact

Unauthenticated attackers can remotely exploit this SQL injection vulnerability to bypass authentication, extract sensitive data, modify database contents, or potentially gain unauthorized access to the underlying system.

Affected Products

  • SourceCodester Online Farm Management System 1.0
  • Donbermoy Online Farm Management System

Discovery Timeline

  • September 2, 2025 - CVE-2025-9833 published to NVD
  • September 5, 2025 - Last updated in NVD database

Technical Details for CVE-2025-9833

Vulnerability Analysis

This vulnerability is classified under CWE-89 (SQL Injection) and CWE-74 (Improper Neutralization of Special Elements in Output Used by a Downstream Component). The flaw resides in the authentication mechanism of the Online Farm Management System, specifically in how user-supplied input to the uname parameter is processed.

When a user submits login credentials, the application fails to properly sanitize or parameterize the username input before incorporating it into SQL queries. This allows an attacker to inject malicious SQL statements that are executed by the database server, effectively breaking out of the intended query logic.

The exploit has been publicly disclosed, increasing the risk of widespread exploitation. According to VulDB #322180, the vulnerability can be triggered remotely without requiring any authentication or user interaction.

Root Cause

The root cause of this vulnerability is the lack of proper input validation and parameterized queries in the login functionality. The application directly concatenates user-supplied input from the uname parameter into SQL statements without sanitization, escaping, or the use of prepared statements. This programming practice violates secure coding principles and creates a direct path for SQL injection attacks.

Attack Vector

The attack can be initiated remotely over the network by sending a crafted HTTP request to the /Login/login.php endpoint. The attacker manipulates the uname parameter with SQL injection payloads that alter the intended query behavior.

A typical attack scenario involves:

  1. An attacker navigates to the login page of the Online Farm Management System
  2. Instead of providing a legitimate username, the attacker enters a SQL injection payload in the username field
  3. The malicious payload bypasses authentication logic or extracts data from the database
  4. The attacker gains unauthorized access or exfiltrates sensitive information

For detailed technical analysis and proof-of-concept information, refer to the GitHub Issue Discussion and the VulDB CTI Incident Report.

Detection Methods for CVE-2025-9833

Indicators of Compromise

  • Anomalous login attempts containing SQL syntax characters such as single quotes, double dashes, or UNION keywords in the username field
  • Database error messages in application logs indicating malformed SQL queries
  • Unexpected database queries executed against user authentication tables
  • Multiple failed login attempts from the same source IP with varying SQL injection patterns

Detection Strategies

  • Implement Web Application Firewall (WAF) rules to detect and block common SQL injection patterns in the uname parameter
  • Monitor HTTP request logs for payloads containing SQL keywords like SELECT, UNION, OR 1=1, and comment characters (--, #)
  • Deploy database activity monitoring to identify unusual query patterns or unauthorized data access
  • Configure intrusion detection systems (IDS) with signatures for SQL injection attack patterns targeting login forms

Monitoring Recommendations

  • Enable verbose logging for the /Login/login.php endpoint to capture all authentication attempts
  • Set up alerts for database errors related to SQL syntax issues in authentication modules
  • Monitor for unusual access patterns to the database, especially bulk data extraction
  • Implement real-time security event correlation to identify SQL injection attack sequences

How to Mitigate CVE-2025-9833

Immediate Actions Required

  • Restrict access to the Online Farm Management System login page to trusted IP ranges until patching is complete
  • Implement a Web Application Firewall with SQL injection protection rules
  • Review and audit all database access logs for signs of exploitation
  • Consider taking the application offline if it contains sensitive agricultural or user data

Patch Information

No official vendor patch has been announced for this vulnerability. Organizations using SourceCodester Online Farm Management System 1.0 should monitor SourceCodester for security updates. Given the nature of this platform (open-source code samples), users may need to apply manual code fixes.

For technical details and community discussion regarding this vulnerability, refer to the VulDB Submission Report.

Workarounds

  • Implement input validation on the uname parameter to allow only alphanumeric characters and expected special characters
  • Modify the login code to use parameterized queries or prepared statements instead of string concatenation
  • Deploy a reverse proxy or WAF with SQL injection detection capabilities in front of the application
  • Implement rate limiting on the login endpoint to slow down automated exploitation attempts

The recommended approach is to modify the vulnerable login code to use prepared statements. In PHP, this involves using PDO or MySQLi with parameterized queries:

php
# Recommended mitigation: Use prepared statements
# Replace direct query concatenation with parameterized queries
# Example using PDO:
$stmt = $pdo->prepare("SELECT * FROM users WHERE username = :username");
$stmt->bindParam(':username', $uname, PDO::PARAM_STR);
$stmt->execute();

Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.

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