Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:L/A:NCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2024-23817 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Dolibarr Dolibarr Erp\/Crm. Its CVSS base score is 7.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Junk Data (T1001.001); ranked at the 45th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-1139
Vulnerability Data
Dolibarr is an enterprise resource planning (ERP) and customer relationship management (CRM) software package. Version 18.0.4 has a HTML Injection vulnerability in the Home page of the Dolibarr Application. This vulnerability allows an attacker to inject arbitrary HTML tags and…
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manipulate the rendered content in the application's response. Specifically, I was able to successfully inject a new HTML tag into the returned document and, as a result, was able to comment out some part of the Dolibarr App Home page HTML code. This behavior can be exploited to perform various attacks like Cross-Site Scripting (XSS). To remediate the issue, validate and sanitize all user-supplied input, especially within HTML attributes, to prevent HTML injection attacks; and implement proper output encoding when rendering user-provided data to ensure it is treated as plain text rather than executable HTML.
- 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.
Secure engineering principles include mandatory output encoding and neutralization of HTML metacharacters to stop injection at the source.
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.
A.5.6 only promotes membership in groups to improve knowledge, receive alerts, and share information; it performs no action that stops XSS exploitation (or even the underlying neutralization defect) and is a pure dependency for other controls per the owner's labeling rule.