CVE-2026-46546 (GCVE-0-2026-46546)
Vulnerability from cvelistv5 – Published: 2026-06-09 23:54 – Updated: 2026-06-10 16:55
VLAI
EPSS
VEX
Title
Frappe LMS: HTML injection in user-controlled metadata
Summary
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 to navigate to an attacker-chosen URL. This issue has been patched in version 2.53.0.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
- CWE-74 - Improper Neutralization of Special Elements in Output Used by a Downstream Component ('Injection')
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/frappe/lms/security/advisories… | x_refsource_CONFIRM |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
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