Raw vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:C/C:H/I:H/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2025-26530 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 27th 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-2025-26530 is a reflected cross-site scripting (XSS) vulnerability, classified under CWE-79, affecting the question bank filter in Moodle due to insufficient sanitizing of inputs. Published on 2025-02-24, 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), indicating high severity with network accessibility but requiring high attack complexity and user interaction.
The vulnerability can be exploited by unauthenticated attackers over the network who craft malicious payloads targeting the question bank filter. Exploitation requires a user, such as an authenticated Moodle user or administrator, to interact with a specially crafted link or input, such as clicking a malicious URL. Successful exploitation enables high-impact consequences, including unauthorized access to confidential data, modification of system integrity, and denial of availability, with a changed scope that potentially affects the broader Moodle environment.
Moodle advisories reference a patch in git commit MDL-84146, available via the project's repository, which adds necessary sanitization to the question bank filter. Additional details are discussed in the Moodle forum thread at https://moodle.org/mod/forum/discuss.php?d=466146.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-4271
Vulnerability Data
The question bank filter required additional sanitizing to prevent a reflected 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.