Raw vector
CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:C/C:H/I:H/A:NSummary
CVE-2025-13523 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Mattermost Confluence. Its CVSS base score is 7.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 9th 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-2025-13523 is a cross-site scripting vulnerability (CWE-79) affecting the Mattermost Confluence plugin in versions prior to 1.7.0. The flaw arises from the plugin's failure to properly escape user-controlled display names during HTML template rendering, enabling the injection of malicious content.
Authenticated Confluence users with low privileges can exploit this vulnerability by crafting a malicious display name and sharing a specially crafted OAuth2 connection link with victims. When a victim visits the link, the attacker's display name is rendered without sanitization, allowing arbitrary JavaScript execution in the victim's browser. The CVSS v3.1 base score is 7.7 (AV:N/AC:H/PR:L/UI:R/S:C/C:H/I:H/A:N), reflecting network accessibility, high attack complexity, required user interaction, and high impacts on confidentiality and integrity with changed scope.
Mattermost Advisory MMSA-2025-00557 provides details on the issue, with mitigation available via upgrade to Confluence plugin version 1.7.0 or later. Additional guidance is available at https://mattermost.com/security-updates.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-206888
Vulnerability Data
Mattermost Confluence plugin version <1.7.0 fails to properly escape user-controlled display names in HTML template rendering which allows authenticated Confluence users with malicious display names to execute arbitrary JavaScript in victim browsers via sending a specially crafted OAuth2 connection link…
more
that, when visited, renders the attacker's display name without proper sanitization. Mattermost Advisory ID: MMSA-2025-00557
- 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.