CVE-2024-23810 Overview
A critical SQL injection vulnerability has been identified in Siemens SINEC NMS (Network Management System), affecting all versions prior to V2.0 SP1. The affected application is vulnerable to SQL injection attacks that could allow an unauthenticated remote attacker to execute arbitrary SQL queries on the server database. This vulnerability poses a severe risk to organizations using SINEC NMS for network management, as successful exploitation could lead to complete compromise of the database, including unauthorized data access, modification, or deletion.
Critical Impact
Unauthenticated remote attackers can execute arbitrary SQL queries on the server database, potentially leading to data theft, data manipulation, or complete system compromise.
Affected Products
- Siemens SINEC NMS (All versions prior to V2.0 SP1)
- Siemens SINEC NMS V2.0 (without SP1 applied)
Discovery Timeline
- 2024-02-13 - CVE-2024-23810 published to NVD
- 2024-11-21 - Last updated in NVD database
Technical Details for CVE-2024-23810
Vulnerability Analysis
This vulnerability is classified as CWE-89 (Improper Neutralization of Special Elements used in an SQL Command), commonly known as SQL Injection. The SINEC NMS application fails to properly sanitize user-supplied input before incorporating it into SQL queries executed against the backend database. This allows attackers to inject malicious SQL statements that are then executed by the database engine with the privileges of the application's database connection.
The network-accessible nature of this vulnerability, combined with the lack of authentication requirements and low attack complexity, makes it particularly dangerous. An attacker with network access to the SINEC NMS application can craft malicious requests containing SQL injection payloads to extract sensitive data, modify database contents, or potentially escalate their access to the underlying server.
Root Cause
The root cause of CVE-2024-23810 is improper input validation and the use of unsanitized user input in SQL query construction. The SINEC NMS application does not adequately filter or parameterize user-supplied data before including it in database queries, allowing attackers to break out of the intended query context and inject arbitrary SQL commands.
Attack Vector
The attack vector for this vulnerability is network-based and requires no authentication or user interaction. An attacker can exploit this vulnerability by sending specially crafted HTTP requests to the vulnerable SINEC NMS application endpoints. The malicious input containing SQL injection payloads is processed by the application and directly incorporated into database queries.
The vulnerability can be exploited through various SQL injection techniques, including UNION-based injection to extract data from other tables, Boolean-based blind injection to infer database contents, time-based blind injection for scenarios where direct output is not visible, and stacked queries to execute additional SQL statements such as INSERT, UPDATE, or DELETE operations.
For detailed technical information, refer to the Siemens Security Advisory SSA-943925.
Detection Methods for CVE-2024-23810
Indicators of Compromise
- Unusual database query patterns in SINEC NMS logs, including queries with SQL syntax characters like single quotes, UNION statements, or comment sequences
- Unexpected database errors or SQL syntax errors appearing in application logs
- Abnormal data access patterns, especially bulk data extraction or access to system tables
- Network traffic containing suspicious SQL-like payloads targeting SINEC NMS endpoints
Detection Strategies
- Deploy Web Application Firewall (WAF) rules to detect and block common SQL injection patterns in requests to SINEC NMS
- Implement database activity monitoring to detect anomalous query execution, unauthorized data access, or privilege escalation attempts
- Enable detailed logging on the SINEC NMS application and database server to capture request parameters and query execution
- Configure intrusion detection systems (IDS) to alert on network traffic containing SQL injection signatures targeting the SINEC NMS application
Monitoring Recommendations
- Monitor SINEC NMS application logs for HTTP requests containing SQL metacharacters and injection patterns
- Implement real-time alerting for database query failures or syntax errors that may indicate exploitation attempts
- Track database user activity and alert on queries accessing sensitive system tables or performing bulk data operations
- Establish baseline network traffic patterns and alert on deviations that may indicate data exfiltration
How to Mitigate CVE-2024-23810
Immediate Actions Required
- Upgrade Siemens SINEC NMS to version V2.0 SP1 or later immediately
- Restrict network access to the SINEC NMS application to trusted IP addresses and networks only
- Deploy a Web Application Firewall (WAF) with SQL injection protection rules in front of the SINEC NMS application
- Review database audit logs for any signs of historical exploitation and investigate anomalies
Patch Information
Siemens has released a security update addressing this vulnerability in SINEC NMS V2.0 SP1. Organizations should download and apply the update from the official Siemens support channels. For detailed patch information and download instructions, refer to the Siemens Security Advisory SSA-943925.
Workarounds
- Implement network segmentation to isolate SINEC NMS from untrusted networks and restrict access to administrative interfaces
- Deploy application-layer filtering using a WAF configured to block SQL injection attack patterns
- Apply the principle of least privilege to database accounts used by SINEC NMS to limit the impact of successful exploitation
- Monitor and log all access to SINEC NMS and set up alerts for suspicious activity patterns
# Network access restriction example (firewall configuration)
# Restrict SINEC NMS access to trusted management network only
iptables -A INPUT -p tcp --dport 443 -s 10.0.0.0/24 -j ACCEPT
iptables -A INPUT -p tcp --dport 443 -j DROP
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.

