CVE-2025-11368
Published: 21 November 2025
Summary
CVE-2025-11368 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Wordpress (inferred from references). Its CVSS base score is 5.3 (Medium).
Operationally, ranked in the top 26.0% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-198382
Vulnerability details
The LearnPress – WordPress LMS Plugin plugin for WordPress is vulnerable to Sensitive Information Disclosure in all versions up to, and including, 4.2.9.4. This is due to missing capability checks in the REST endpoint /wp-json/lp/v1/load_content_via_ajax which allows arbitrary callback execution…
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of admin-only template methods. This makes it possible for unauthenticated attackers to retrieve admin curriculum HTML, quiz questions with correct answers, course materials, and other sensitive educational content via the REST API endpoint granted they can supply valid numeric IDs.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.
Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.