Raw vector
CVSS:3.1/AV:N/AC:H/PR:H/UI:R/S:C/C:H/I:H/A:HSummary
CVE-2026-24836 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Dnnsoftware Dotnetnuke. Its CVSS base score is 7.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 13th 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-2026-24836 is a cross-site scripting (XSS) vulnerability (CWE-79) in DNN (formerly DotNetNuke), an open-source web content management platform (CMS) in the Microsoft ecosystem. The issue affects versions starting from 9.0.0 and prior to 9.13.10 and 10.2.0, where extensions could write rich text, including scripts, into log notes. These scripts would execute when the notes are displayed in the PersonaBar administrative interface. The vulnerability has a CVSS v3.1 base score of 7.6 (AV:N/AC:H/PR:H/UI:R/S:C/C:H/I:H/A:H).
Exploitation requires network access, high privileges (PR:H), high attack complexity (AC:H), and user interaction (UI:R). A high-privileged user, such as an administrator with access to extensions that write to log notes, can inject malicious scripts into the rich text. When another user with sufficient privileges views the PersonaBar and the affected log notes, the scripts execute in that user's browser context, potentially leading to high confidentiality, integrity, and availability impacts with a changed scope (S:C), such as session hijacking, data theft, or further system compromise.
The GitHub security advisory at https://github.com/dnnsoftware/Dnn.Platform/security/advisories/GHSA-2g5g-hcgh-q3rp details the issue and confirms that DNN versions 9.13.10 and 10.2.0 include fixes. Security practitioners should upgrade affected DNN installations to these patched versions to mitigate the vulnerability.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-4863
Vulnerability Data
DNN (formerly DotNetNuke) is an open-source web content management platform (CMS) in the Microsoft ecosystem. Starting in version 9.0.0 and prior to versions 9.13.10 and 10.2.0, extensions could write richtext in log notes which can include scripts that would run…
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in the PersonaBar when displayed. Versions 9.13.10 and 10.2.0 contain a fix for the issue.
- 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.