Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/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-2025-55289 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Chamilo Chamilo Lms. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 22th 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-2025-55289 is a stored cross-site scripting (XSS) vulnerability (CWE-79) in Chamilo, an open-source learning management system (LMS). It affects Chamilo LMS versions prior to 1.11.34, with the issue specifically noted in version 1.11.32. The vulnerability resides in the platform's social network and internal messaging features, where attackers can inject arbitrary JavaScript code. The flaw carries a CVSS v3.1 base score of 8.8 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H), indicating high severity due to its network accessibility, low attack complexity, and significant impacts on confidentiality, integrity, and availability.
A low-privileged authenticated attacker can exploit this vulnerability by injecting malicious JavaScript payloads into the social network or internal messaging components. When another authenticated user, including administrators, views the injected content, the payload executes in their browser within the context of the LMS. This enables full account takeover via session hijacking, execution of unauthorized actions using the victim's privileges, exfiltration of sensitive data, and potential self-propagation to other users through further messaging or social interactions.
The vulnerability has been patched in Chamilo LMS version 1.11.34. Administrators are advised to upgrade to this version or later to mitigate the issue. Additional details are available in the GitHub security advisory at https://github.com/chamilo/chamilo-lms/security/advisories/GHSA-cchj-3qmf-82j5 and the release notes at https://github.com/chamilo/chamilo-lms/releases/tag/v1.11.34.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-208338
Vulnerability Data
Chamilo is a learning management system. Prior to version 1.11.34, there is a stored XSS vulnerability in Chamilo LMS (Verison 1.11.32) allows an attacker to inject arbitrary JavaScript into the platform’s social network and internal messaging features. When viewed by…
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an authenticated user (including administrators), the payload executes in their browser within the LMS context. This enables full account takeover via session hijacking, unauthorized actions with the victim’s privileges, exfiltration of sensitive data, and potential self-propagation to other users. This issue has been patched in version 1.11.34.
- 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.