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CVE Vulnerability Database

CVE-2025-7612: Anisha Mobile Shop SQLi Vulnerability

CVE-2025-7612 is a critical SQL injection vulnerability in Anisha Mobile Shop 1.0 affecting the login.php file. Attackers can exploit the email parameter remotely. This article covers technical details, impact, and mitigation.

Published:

CVE-2025-7612 Overview

A critical SQL injection vulnerability has been identified in code-projects Mobile Shop version 1.0. The vulnerability exists in the /login.php file, where improper handling of the email parameter allows attackers to inject malicious SQL commands. This flaw enables remote attackers to bypass authentication, access sensitive data, and potentially compromise the entire database backend without requiring any authentication.

Critical Impact

Remote attackers can exploit this SQL injection vulnerability in the login page to bypass authentication, extract sensitive customer and transaction data, and potentially gain full control of the database.

Affected Products

  • Anisha Mobile Shop 1.0
  • code-projects Mobile Shop /login.php endpoint
  • Web applications using the vulnerable Mobile Shop codebase

Discovery Timeline

  • 2025-07-14 - CVE-2025-7612 published to NVD
  • 2025-07-15 - Last updated in NVD database

Technical Details for CVE-2025-7612

Vulnerability Analysis

This SQL injection vulnerability (CWE-89) stems from improper input validation in the authentication mechanism of the Mobile Shop application. The /login.php endpoint fails to properly sanitize user-supplied input in the email parameter before incorporating it into SQL queries. This allows attackers to craft malicious payloads that alter the intended SQL query logic, potentially leading to authentication bypass, data exfiltration, or database manipulation.

The vulnerability is classified under both CWE-89 (Improper Neutralization of Special Elements used in an SQL Command) and CWE-74 (Improper Neutralization of Special Elements in Output Used by a Downstream Component), indicating a fundamental failure to sanitize user input before it reaches the database layer.

Root Cause

The root cause of this vulnerability is the direct incorporation of user-supplied input from the email parameter into SQL queries without proper parameterization or input sanitization. The application fails to implement prepared statements or parameterized queries, allowing special SQL characters and commands to be interpreted by the database engine rather than treated as literal string values.

Attack Vector

The attack can be initiated remotely over the network without requiring authentication. An attacker sends a crafted HTTP request to the /login.php endpoint with a malicious SQL payload in the email parameter. The vulnerability requires no special privileges or user interaction to exploit, making it highly accessible to attackers.

The manipulation of the email parameter allows attackers to inject SQL syntax that can modify query behavior. Common attack patterns include authentication bypass using tautologies (e.g., ' OR '1'='1), UNION-based attacks for data extraction, and time-based blind SQL injection for database enumeration. Technical details and proof-of-concept information have been documented in the GitHub CVE Issue and VulDB #316312.

Detection Methods for CVE-2025-7612

Indicators of Compromise

  • Unusual SQL error messages appearing in web server logs or responses from /login.php
  • Multiple login attempts with email values containing SQL metacharacters (quotes, semicolons, UNION statements)
  • Unexpected database queries or access patterns originating from the web application
  • Authentication logs showing successful logins without corresponding valid credential checks

Detection Strategies

  • Deploy Web Application Firewall (WAF) rules to detect SQL injection patterns in POST parameters to /login.php
  • Configure IDS/IPS signatures to alert on SQL injection payloads targeting the email parameter
  • Enable detailed logging for authentication endpoints and monitor for anomalous query patterns
  • Implement application-level logging to capture raw input values before processing

Monitoring Recommendations

  • Monitor HTTP POST requests to /login.php for suspicious patterns in the email field
  • Review database query logs for unusual syntax or unexpected UNION/SELECT statements
  • Set up alerts for authentication anomalies such as logins without proper credential validation
  • Track failed login attempts that generate database errors rather than application-level authentication failures

How to Mitigate CVE-2025-7612

Immediate Actions Required

  • Restrict access to the /login.php endpoint via network controls until patching is completed
  • Implement Web Application Firewall rules to block SQL injection attempts on the email parameter
  • Review authentication logs for evidence of prior exploitation attempts
  • Consider taking the affected application offline if sensitive data is at risk

Patch Information

No vendor patch has been officially released at the time of this advisory. Organizations using code-projects Mobile Shop 1.0 should contact the vendor or review the Code Projects Resource for updates. Given the nature of this vulnerability, implementing code-level fixes using prepared statements and parameterized queries is strongly recommended.

Workarounds

  • Implement input validation to whitelist allowed characters in the email field (alphanumeric, @, ., -, _)
  • Use prepared statements with parameterized queries for all database interactions in /login.php
  • Deploy a Web Application Firewall configured to block common SQL injection patterns
  • Implement rate limiting and account lockout mechanisms to slow brute-force and injection attempts
  • Sanitize all user input using appropriate escaping functions before database query construction
bash
# WAF rule example for ModSecurity to block SQL injection in email parameter
SecRule ARGS:email "@detectSQLi" \
  "id:1001,\
  phase:2,\
  deny,\
  status:403,\
  msg:'SQL Injection attempt detected in email parameter',\
  log,\
  auditlog"

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

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