Raw vector
CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2026-25932 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Glpi-Project Glpi. Its CVSS base score is 7.2 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 20th 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-2026-25932 is a stored cross-site scripting (XSS) vulnerability, associated with CWE-79 and CWE-116, affecting GLPI, an open-source asset and IT management software package. The issue exists in versions from 0.60 up to but not including 10.0.24, where an authenticated technician user can inject and store a malicious XSS payload in supplier fields. It has a CVSS v3.1 base score of 7.2 (AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H), indicating high severity due to network accessibility, low attack complexity, and significant impacts on confidentiality, integrity, and availability.
An authenticated user with technician privileges can exploit this vulnerability by submitting a crafted XSS payload into supplier fields, which is then persistently stored and rendered for other users viewing the affected data. Successful exploitation allows the attacker to execute arbitrary JavaScript in the context of victims' browsers, potentially leading to session hijacking, data theft, or further compromise depending on the privileges of affected users. No user interaction beyond normal viewing of supplier information is required.
The GLPI project addressed this vulnerability in version 10.0.24. Security practitioners should upgrade to this patched release or later. Additional details are available in the official advisory at https://github.com/glpi-project/glpi/security/advisories/GHSA-m627-945g-x7xh.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-19245
Vulnerability Data
GLPI is a Free Asset and IT Management Software package. From 0.60 to before 10.0.24, an authenticated technician user can store an XSS payload in a supplier fields. This vulnerability is fixed in 10.0.24.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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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 standards explicitly require correct output encoding and escaping to preserve message structure.
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.
Application security requirements include explicit rules for safe output handling and encoding.