Raw vector
CVSS:4.0/AV:N/AC:L/AT:P/PR:L/UI:P/VC:N/VI:L/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2026-46546 is a low-severity Injection (CWE-74) vulnerability in Frappe Learning. Its CVSS base score is 2.1 (Low).
Operationally, exploitation aligns with the MITRE ATT&CK technique Content Injection (T1659); ranked at the 3th 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 SI-10 (Information Input Validation) and SC-8 (Transmission Confidentiality and Integrity) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-35912
Vulnerability Data
Frappe Learning Management System (LMS) is a learning system that helps users structure their content. Prior to version 2.53.0, an authenticated user could supply specially crafted content in certain user-editable fields that, when surfaced in page metadata, caused visitors' browsers…
more
to navigate to an attacker-chosen URL. This issue has been patched in version 2.53.0.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
CWE-74 injection in user-editable fields directly enables content injection (T1659) that forces browser navigation to attacker URLs.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
SI-10 requires validation of all user-supplied input before it is rendered in page metadata, directly blocking the crafted content that triggers the open redirect.
SC-8 mandates cryptographic protection of data in transit, reducing the risk that an attacker can exploit the redirect to intercept or manipulate the victim’s session.
SI-7 enables integrity checks on rendered content, providing a secondary means to detect unauthorized modifications that produce the malicious redirect.
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 require input validation and output encoding that prevent injection flaws.
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.
Security testing in development catches injection vulnerabilities before release.
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.
Logging supports detection of injection attempts but does not prevent the weakness.
Monitoring activities can identify active injection attacks after they occur.
Secure development life cycle mandates input validation and output encoding that directly prevent injection flaws.