FKIE_CVE-2026-39415
Vulnerability from fkie_nvd - Published: 2026-04-08 21:16 - Updated: 2026-07-24 21:10
Severity
Summary
Frappe Learning Management System (LMS) is a learning system that helps users structure their content. Prior to 2.46.0, a vulnerability has been identified in Frappe Learning where quiz scores can be modified by students before submission. The application currently relies on client-side calculated scores, which can be altered using browser developer tools prior to sending the submission request. While this does not allow modification of other users’ data or privilege escalation, it compromises the integrity of quiz results and undermines academic reliability. This issue affects data integrity but does not expose confidential information or allow unauthorized access to other accounts. This vulnerability is fixed in 2.46.0.
References
{
"affected": [
{
"affectedData": [
{
"product": "lms",
"vendor": "frappe",
"versions": [
{
"status": "affected",
"version": "\u003c 2.46.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:frappe:learning:*:*:*:*:*:*:*:*",
"matchCriteriaId": "782E9AF3-F203-48EA-8DDF-20BA928EBEDA",
"versionEndExcluding": "2.46.0",
"versionStartIncluding": "2.0.0",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Frappe Learning Management System (LMS) is a learning system that helps users structure their content. Prior to 2.46.0, a vulnerability has been identified in Frappe Learning where quiz scores can be modified by students before submission. The application currently relies on client-side calculated scores, which can be altered using browser developer tools prior to sending the submission request. While this does not allow modification of other users\u2019 data or privilege escalation, it compromises the integrity of quiz results and undermines academic reliability. This issue affects data integrity but does not expose confidential information or allow unauthorized access to other accounts. This vulnerability is fixed in 2.46.0."
},
{
"lang": "es",
"value": "Frappe Learning Management System (LMS) es un sistema de aprendizaje que ayuda a los usuarios a estructurar su contenido. Antes de la versi\u00f3n 2.46.0, se ha identificado una vulnerabilidad en Frappe Learning donde las puntuaciones de los cuestionarios pueden ser modificadas por los estudiantes antes de la entrega. La aplicaci\u00f3n actualmente se basa en puntuaciones calculadas en el lado del cliente, las cuales pueden ser alteradas utilizando las herramientas de desarrollador del navegador antes de enviar la solicitud de entrega. Si bien esto no permite la modificaci\u00f3n de datos de otros usuarios o la escalada de privilegios, compromete la integridad de los resultados de los cuestionarios y socava la fiabilidad acad\u00e9mica. Este problema afecta la integridad de los datos, pero no expone informaci\u00f3n confidencial ni permite el acceso no autorizado a otras cuentas. Esta vulnerabilidad est\u00e1 corregida en la versi\u00f3n 2.46.0."
}
],
"id": "CVE-2026-39415",
"lastModified": "2026-07-24T21:10:00.143",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 4.3,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "LOW",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 1.4,
"source": "nvd@nist.gov",
"type": "Primary"
}
],
"cvssMetricV40": [
{
"cvssData": {
"Automatable": "NOT_DEFINED",
"Recovery": "NOT_DEFINED",
"Safety": "NOT_DEFINED",
"attackComplexity": "LOW",
"attackRequirements": "NONE",
"attackVector": "NETWORK",
"availabilityRequirement": "NOT_DEFINED",
"baseScore": 5.3,
"baseSeverity": "MEDIUM",
"confidentialityRequirement": "NOT_DEFINED",
"exploitMaturity": "NOT_DEFINED",
"integrityRequirement": "NOT_DEFINED",
"modifiedAttackComplexity": "NOT_DEFINED",
"modifiedAttackRequirements": "NOT_DEFINED",
"modifiedAttackVector": "NOT_DEFINED",
"modifiedPrivilegesRequired": "NOT_DEFINED",
"modifiedSubAvailabilityImpact": "NOT_DEFINED",
"modifiedSubConfidentialityImpact": "NOT_DEFINED",
"modifiedSubIntegrityImpact": "NOT_DEFINED",
"modifiedUserInteraction": "NOT_DEFINED",
"modifiedVulnAvailabilityImpact": "NOT_DEFINED",
"modifiedVulnConfidentialityImpact": "NOT_DEFINED",
"modifiedVulnIntegrityImpact": "NOT_DEFINED",
"privilegesRequired": "LOW",
"providerUrgency": "NOT_DEFINED",
"subAvailabilityImpact": "NONE",
"subConfidentialityImpact": "NONE",
"subIntegrityImpact": "NONE",
"userInteraction": "NONE",
"valueDensity": "NOT_DEFINED",
"vectorString": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/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:X",
"version": "4.0",
"vulnAvailabilityImpact": "NONE",
"vulnConfidentialityImpact": "NONE",
"vulnIntegrityImpact": "LOW",
"vulnerabilityResponseEffort": "NOT_DEFINED"
},
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-39415",
"options": [
{
"exploitation": "none"
},
{
"automatable": "yes"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-04-09T13:52:03.840230Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-04-08T21:16:59.033",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Vendor Advisory"
],
"url": "https://github.com/frappe/lms/security/advisories/GHSA-9573-68xq-hwrx"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-602"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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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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