CVE-2024-4434 Overview
The LearnPress WordPress LMS Plugin, a popular learning management system solution for WordPress, contains a critical time-based SQL Injection vulnerability in versions up to and including 4.2.6.5. The vulnerability exists in the term_id parameter due to insufficient escaping of user-supplied input and lack of proper preparation on the existing SQL query. This security flaw allows unauthenticated attackers to inject malicious SQL queries into existing database queries, potentially extracting sensitive information from the WordPress database.
Critical Impact
Unauthenticated attackers can exploit this SQL Injection vulnerability to extract sensitive data from the database, including user credentials, personal information, and other confidential WordPress data without requiring any authentication.
Affected Products
- ThimPress LearnPress versions up to and including 4.2.6.5
- WordPress installations using vulnerable LearnPress plugin versions
- Learning management system deployments built on affected LearnPress versions
Discovery Timeline
- 2024-05-14 - CVE-2024-4434 published to NVD
- 2025-01-15 - Last updated in NVD database
Technical Details for CVE-2024-4434
Vulnerability Analysis
This vulnerability is classified as CWE-89 (SQL Injection), one of the most dangerous web application security flaws. The vulnerable code resides in the LearnPress database class file (class-lp-course-db.php), specifically at line 508 of the vulnerable version.
The time-based SQL injection technique allows attackers to infer information from the database by observing response time differences when injecting conditional time-delay SQL statements. Since no authentication is required to exploit this vulnerability, any remote attacker can target vulnerable WordPress installations running the affected LearnPress plugin versions.
Root Cause
The root cause of this vulnerability is the failure to properly sanitize and prepare user-supplied input in the term_id parameter before incorporating it into SQL queries. WordPress provides the $wpdb->prepare() function specifically to prevent SQL injection by properly escaping user input, but this was not adequately implemented in the affected code path. The lack of input validation combined with direct parameter inclusion in database queries creates the injection point.
Attack Vector
The attack vector is network-based and requires no authentication or user interaction. An attacker can craft malicious HTTP requests containing specially constructed term_id parameter values that include SQL injection payloads. By using time-based blind SQL injection techniques, attackers can extract database contents character by character by measuring response times. This allows complete database enumeration including WordPress user credentials, course data, student information, and other sensitive content stored in the database.
The exploitation process typically involves sending requests with payloads that cause the database to pause (using SQL SLEEP() or similar functions) when a condition is true, allowing attackers to infer data by timing the responses.
Detection Methods for CVE-2024-4434
Indicators of Compromise
- Unusual database query execution times or server response delays
- HTTP requests to LearnPress endpoints containing suspicious term_id parameter values with SQL syntax
- Web server logs showing requests with encoded SQL keywords like SLEEP, BENCHMARK, WAITFOR, or IF statements
- Increased database CPU usage or connection counts without corresponding legitimate traffic
Detection Strategies
- Implement Web Application Firewall (WAF) rules to detect SQL injection patterns in the term_id parameter
- Configure logging to capture and alert on requests containing common SQL injection signatures
- Deploy database activity monitoring to detect unusual query patterns or unauthorized data access
- Use SentinelOne Singularity Platform to monitor for post-exploitation behaviors following successful SQL injection attacks
Monitoring Recommendations
- Enable detailed WordPress and web server access logging with full request parameter capture
- Monitor database query logs for anomalous queries or extended execution times
- Set up alerting for repeated requests to LearnPress course endpoints with varying parameter values
- Implement network traffic analysis to detect automated SQL injection scanning tools
How to Mitigate CVE-2024-4434
Immediate Actions Required
- Update LearnPress plugin immediately to a patched version (higher than 4.2.6.5)
- If immediate update is not possible, temporarily disable the LearnPress plugin until patching can be completed
- Review web server logs for evidence of prior exploitation attempts
- Consider implementing a WAF rule to block requests with suspicious term_id parameter values
Patch Information
ThimPress has released a security patch to address this vulnerability. The fix can be verified in the WordPress LearnPress Changeset, which implements proper input sanitization and prepared statements for the affected parameter. Administrators should update to the latest available version of LearnPress through the WordPress plugin manager or by downloading directly from the WordPress plugin repository.
For detailed vulnerability information, refer to the Wordfence Vulnerability Report and the Security Analysis documentation.
Workarounds
- Deploy a Web Application Firewall with SQL injection detection rules targeting the term_id parameter
- Implement server-level input validation to reject requests containing SQL keywords or special characters in course-related parameters
- Restrict access to LearnPress course browsing functionality to authenticated users only as a temporary measure
- Consider using WordPress security plugins that provide virtual patching capabilities for known vulnerabilities
# Example .htaccess rule to block common SQL injection patterns
# Add to WordPress root .htaccess file as temporary mitigation
<IfModule mod_rewrite.c>
RewriteEngine On
RewriteCond %{QUERY_STRING} (term_id=.*(\%27|'|--|\%23|#|sleep|benchmark)) [NC]
RewriteRule .* - [F,L]
</IfModule>
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.


