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 0.7th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Other Platforms; in the LLM/Generative AI Risks risk domain.
The strongest mitigations our analysis identified are NIST 800-53 SI-10 (Information Input Validation) and SI-15 (Information Output Filtering).
Deeper analysis
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.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-206737
Vulnerability details
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.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other Platforms
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
Related Threats
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).
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Directly requires validation and sanitization of all inputs (including AI prompt responses) before they are rendered in web pages, blocking the unsanitized HTML/JS injection that defines this XSS flaw.
Mandates filtering of information outputs to remove or encode potentially malicious content, preventing the compromised AI responses from executing scripts in other users' browsers.
Provides mechanisms to detect and block malicious code (scripts) delivered via web content, offering secondary protection against the session-theft and UI-manipulation payloads enabled by this CVE.