CVE-2022-30493
📋 TL;DR
CVE-2022-30493 is a critical SQL injection vulnerability in oretnom23 Automotive Shop Management System v1.0 that allows remote attackers to extract database credentials and gain administrative access. This affects all deployments of this specific software version. Attackers can completely compromise the system through unauthenticated exploitation.
💻 Affected Systems
- oretnom23 Automotive Shop Management System
📦 What is this software?
Automotive Shop Management System by Automotive Shop Management System Project
⚠️ Risk & Real-World Impact
Worst Case
Complete system takeover with full database access, credential theft, privilege escalation to admin, and potential lateral movement to connected systems.
Likely Case
Database credential extraction leading to admin access, data exfiltration, and system compromise.
If Mitigated
Limited impact with proper input validation, WAF protection, and network segmentation preventing exploitation.
🎯 Exploit Status
Public exploit code exists on GitHub demonstrating blind SQL injection to extract credentials and gain admin access.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: Unknown
Vendor Advisory: No official vendor advisory found
Restart Required: No
Instructions:
1. Check for updated version from vendor. 2. If no patch available, implement workarounds. 3. Consider replacing with alternative software.
🔧 Temporary Workarounds
Web Application Firewall (WAF) Rules
allImplement WAF rules to block SQL injection patterns in product id parameter
# Example ModSecurity rule: SecRule ARGS:product_id "@detectSQLi" "id:1001,phase:2,deny,status:403"
Input Validation Filter
allAdd server-side validation to restrict product id parameter to numeric values only
# PHP example: if(!is_numeric($_GET['product_id'])) { die('Invalid input'); }
🧯 If You Can't Patch
- Isolate the system behind a reverse proxy with strict input validation
- Implement network segmentation to limit access to the vulnerable system
🔍 How to Verify
Check if Vulnerable:
Test the product id parameter with SQL injection payloads like: 1' AND '1'='1 or time-based blind SQL payloadsCheck Version:
Check application version in admin panel or source code filesVerify Fix Applied:
Verify that SQL injection payloads no longer work and return proper error handling📡 Detection & Monitoring
Log Indicators:
- Unusual SQL error messages in application logs
- Multiple failed login attempts followed by admin access
- Requests with SQL keywords in product id parameter
Network Indicators:
- Unusual database connection patterns from web server
- Large data exfiltration from database port
SIEM Query:
source="web_logs" AND ("product_id" AND ("UNION" OR "SELECT" OR "SLEEP" OR "WAITFOR"))