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CVE Vulnerability Database

CVE-2025-8744: CesiumLab Web SQL Injection Vulnerability

CVE-2025-8744 is a critical SQL injection flaw in CesiumLab Web up to version 4.0 affecting the /lodmodels/ file. Attackers can exploit this remotely to compromise databases. This article covers technical details, affected versions, impact, and mitigation strategies.

Published:

CVE-2025-8744 Overview

A critical SQL injection vulnerability has been identified in CesiumLab Web versions up to 4.0. This vulnerability exists in the /lodmodels/ endpoint and allows remote attackers to manipulate the ID parameter to execute arbitrary SQL commands against the backend database. The attack can be initiated remotely without authentication, potentially leading to unauthorized data access, modification, or complete database compromise.

Critical Impact

Remote attackers can exploit this SQL injection vulnerability to extract sensitive data, modify database contents, or potentially gain further access to the underlying system through database-level attacks.

Affected Products

  • CesiumLab Web up to version 4.0
  • Systems utilizing the /lodmodels/ endpoint with vulnerable ID parameter handling

Discovery Timeline

  • 2025-08-09 - CVE-2025-8744 published to NVD
  • 2026-04-15 - Last updated in NVD database

Technical Details for CVE-2025-8744

Vulnerability Analysis

This vulnerability is classified under CWE-74 (Improper Neutralization of Special Elements in Output Used by a Downstream Component), commonly referred to as injection vulnerabilities. The flaw exists in the /lodmodels/ endpoint where the ID parameter is not properly sanitized before being used in database queries.

The vulnerability allows remote attackers to inject malicious SQL statements through the ID parameter. Since no authentication is required and the attack can be performed over the network, this represents a significant security risk for any publicly accessible CesiumLab Web instances. The exploit has been publicly disclosed, increasing the likelihood of exploitation attempts in the wild.

Root Cause

The root cause of this vulnerability is improper input validation and lack of parameterized queries in the /lodmodels/ endpoint. When user-supplied data in the ID parameter is directly concatenated into SQL queries without proper sanitization or the use of prepared statements, attackers can inject malicious SQL code that will be executed by the database engine.

Attack Vector

The attack vector is network-based, requiring no user interaction or prior authentication. An attacker can craft malicious HTTP requests to the /lodmodels/ endpoint, manipulating the ID parameter with SQL injection payloads. These payloads can be designed to:

  • Extract sensitive information from the database (data exfiltration)
  • Modify or delete existing records (data manipulation)
  • Bypass authentication mechanisms
  • Execute administrative operations on the database
  • Potentially escalate to command execution depending on database configuration

The vulnerability can be exploited by sending specially crafted requests containing SQL metacharacters and injection payloads in the ID parameter. Common techniques include UNION-based injection, blind SQL injection, and time-based injection methods to extract data or enumerate database structure.

Detection Methods for CVE-2025-8744

Indicators of Compromise

  • Unusual SQL error messages in application or web server logs originating from the /lodmodels/ endpoint
  • HTTP requests containing SQL metacharacters (single quotes, double dashes, UNION keywords) in the ID parameter
  • Abnormal database query patterns or unexpected query execution times
  • Unauthorized data access or unexplained changes to database records

Detection Strategies

  • Deploy Web Application Firewall (WAF) rules to detect and block SQL injection patterns targeting the /lodmodels/ endpoint
  • Implement database activity monitoring to identify suspicious queries or unusual data access patterns
  • Configure intrusion detection systems (IDS) to alert on SQL injection attack signatures in HTTP traffic
  • Enable detailed logging for the /lodmodels/ endpoint to capture all requests and parameter values

Monitoring Recommendations

  • Monitor application logs for SQL syntax errors or database exception messages
  • Set up alerts for requests to /lodmodels/ containing suspicious characters or known SQL injection patterns
  • Track database query performance metrics to identify potential time-based blind SQL injection attempts
  • Regularly audit database access logs for unauthorized queries or data exports

How to Mitigate CVE-2025-8744

Immediate Actions Required

  • Restrict network access to the /lodmodels/ endpoint until a patch is available
  • Deploy WAF rules to filter SQL injection attempts targeting the vulnerable parameter
  • Implement input validation to reject requests with SQL metacharacters in the ID parameter
  • Consider temporarily disabling the affected functionality if business operations permit

Patch Information

The vendor was contacted about this vulnerability but did not respond. As of the last update on 2026-04-15, no official patch has been released by the vendor. Organizations should implement compensating controls and monitor for vendor updates. For additional technical details, refer to the VulDB advisory.

Workarounds

  • Implement a reverse proxy or WAF in front of CesiumLab Web to filter malicious requests containing SQL injection payloads
  • Apply network-level access controls to limit access to the /lodmodels/ endpoint to trusted IP addresses only
  • Use database user permissions to restrict the application's database account to minimum required privileges
  • Enable prepared statements or parameterized queries at the application level if source code access is available
bash
# Example WAF rule configuration (ModSecurity)
# Block SQL injection attempts on the vulnerable endpoint
SecRule REQUEST_URI "@contains /lodmodels/" "id:100001,phase:2,deny,status:403,chain"
SecRule ARGS:ID "@detectSQLi" "t:none,t:urlDecodeUni"

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

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