CVE-2025-32044
Published: 25 April 2025
Summary
CVE-2025-32044 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Moodle Moodle. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 33.9th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-12530
Vulnerability details
A flaw has been identified in Moodle where, on certain sites, unauthenticated users could retrieve sensitive user data—including names, contact information, and hashed passwords—via stack traces returned by specific API calls. Sites with PHP configured with zend.exception_ignore_args = 1 in…
more
the php.ini file are not affected by this vulnerability.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Unauthenticated exploitation of public-facing Moodle API leaks user account details (names, contact info) enabling account discovery (T1087) and hashed passwords enabling credential access (T1212) via public-facing app exploit (T1190).
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.