Raw vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:C/C:H/I:H/A:HSummary
CVE-2025-26529 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Moodle Moodle. Its CVSS base score is 8.3 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 40th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-26529 is a stored cross-site scripting (XSS) vulnerability, classified under CWE-79, affecting the Moodle learning management system. The flaw occurs in the site administration live log, where description information displayed to administrators lacked sufficient sanitization, enabling a stored XSS risk. It carries a CVSS v3.1 base score of 8.3 (AV:N/AC:H/PR:N/UI:R/S:C/C:H/I:H/A:H) and was published on 2025-02-24.
Attackers can exploit this vulnerability remotely over the network without requiring authentication privileges (PR:N), though it demands high attack complexity (AC:H) and user interaction (UI:R), such as an administrator viewing the affected log. Successful exploitation changes scope (S:C) and can lead to high impacts on confidentiality, integrity, and availability (C:H/I:H/A:H), potentially allowing attackers to execute arbitrary scripts in the victim's browser context.
Mitigation is provided through a patch in the Moodle Git repository, searchable under commit details for MDL-84145 at http://git.moodle.org/gw?p=moodle.git&a=search&h=HEAD&st=commit&s=MDL-84145. Additional discussion and context are available in the Moodle forum thread at https://moodle.org/mod/forum/discuss.php?d=466145.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-4274
Vulnerability Data
Description information displayed in the site administration live log required additional sanitizing to prevent a stored XSS risk.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.1.2V1.3.2
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover missing or incorrect input neutralization through targeted web-application tests.
Input validation directly enforces neutralization of untrusted data before it reaches web output generation.
Output filtering can catch or sanitize unneutralized script content before it is served to users.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly target introduction of XSS via coding standards/testing (mostly), yet the single broad outcome leaves many specific neutralization vectors unaddressed (partial).
Patching and EOL replacement can remediate known XSS instances in libraries or frameworks (partial) but do nothing to enforce input neutralization in application code (none).
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.
Knowledge exchange on emerging attack techniques and patches reduces the likelihood that cross-site scripting flaws remain unaddressed in deployed applications.
Operational indicators of compromise for web-application attacks can be incorporated into WAF or input-filtering rules, lowering the likelihood that unsanitized data reaches the browser.
Requiring language-specific secure-coding standards and automated scanning during the SDLC catches missing output encoding or improper neutralization of untrusted data before the software reaches production.
Secure-coding standards, SAST scans and removal of insecure code samples together eliminate the failure to neutralize script content that produces cross-site scripting flaws.
Webpage malware scanning and block-listing of known malicious sites reduce the likelihood that reflected or stored script payloads reach a user’s browser.