CVE-2025-67849
Published: 03 February 2026
Summary
CVE-2025-67849 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Moodle Moodle. Its CVSS base score is 7.3 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique JavaScript (T1059.007); ranked at the 2.2th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Machine Learning Libraries.
Threat & Defense at a Glance
Threat & Defense Details
Likely Mitigating ControlsAI
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.
Penetration testing submits XSS payloads to web applications, detecting cross-site scripting flaws for subsequent remediation.
Validates web inputs to reject script-related content that could produce XSS.
Output validation against expected content can reject or sanitize script content in generated web pages, reducing XSS exploitability.
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Stored XSS via unsanitized JS/HTML injection directly enables arbitrary JavaScript execution (T1059.007) in victim browsers and session hijacking (T1185).
NVD Description
A flaw was found in Moodle. This cross-site scripting (XSS) vulnerability, caused by improper sanitization of AI prompt responses, allows attackers to inject malicious HTML or script into web pages. When other users view these compromised pages, their sessions could…
more
be stolen, or the user interface could be manipulated.
Deeper analysisAI
CVE-2025-67849 is a cross-site scripting (XSS) vulnerability in Moodle, published on 2026-02-03, caused by improper sanitization of AI prompt responses. This flaw, classified under CWE-79, allows attackers to inject malicious HTML or JavaScript into web pages viewed by other users. It carries a CVSS v3.1 base score of 7.3 (AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:N), indicating high severity due to its potential for significant confidentiality and integrity impacts.
The vulnerability can be exploited by low-privileged authenticated users over the network with low attack complexity, though it requires user interaction from victims. An attacker injects malicious payloads via AI prompt responses, which are then rendered unsanitized on pages accessed by other users. This enables session theft, user interface manipulation, or other client-side attacks in the victim's browser context.
Red Hat security advisories and related Bugzilla entries provide further details on affected versions and mitigation steps, accessible at https://access.redhat.com/security/cve/CVE-2025-67849 and https://bugzilla.redhat.com/show_bug.cgi?id=2423835.
Details
- CWE(s)
Affected Products
AI Security AnalysisAI
- AI Category
- Machine Learning Libraries
- Risk Domain
- N/A
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai