CVE-2024-25973
Published: 20 February 2024
Summary
CVE-2024-25973 is a medium-severity Improper Input Validation (CWE-20) vulnerability in Frentix Openolat. Its CVSS base score is 5.4 (Medium).
Operationally, ranked at the 41.3th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-23275
Vulnerability details
The Frentix GmbH OpenOlat LMS is affected by multiple stored Cross-Site Scripting (XSS) vulnerabilities. An attacker with rights to create or edit groups can create a course with a name that contains an XSS payload. Furthermore, attackers with the permissions…
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to create or rename a catalog (sub-category) can enter unfiltered input in the name field. In addition, attackers who are allowed to create curriculums can also enter unfiltered input in the name field. This allows an attacker to execute stored JavaScript code with the permissions of the victim in the context of the user's browser.
- 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.
Directly implements checks on information inputs to reject invalid data before processing.
Penetration testing submits XSS payloads to web applications, detecting cross-site scripting flaws for subsequent remediation.
Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.
Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.
Output validation against expected content can reject or sanitize script content in generated web pages, reducing XSS exploitability.
Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.