FKIE_CVE-2026-120544
Vulnerability from fkie_nvd - Published: 2026-11-03 05:18 - Updated: 2026-11-03 14:17
Severity
Summary
The Academy LMS – AI Course Builder, Quizzes, Certificates & eLearning plugin for WordPress is vulnerable to Directory Traversal in all versions up to, and including, 4.0.1 via the 'id' parameter. This makes it possible for authenticated attackers, with contributor-level access and above, to read the contents of arbitrary .html files on the server. The plugin appends a '.html' extension to the attacker-supplied path before reading it, so files with any other extension — including wp-config.php — are out of reach. The file's contents are returned in full in the REST JSON response's content field.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"defaultStatus": "unaffected",
"product": "Academy LMS \u2013 AI Course Builder, Quizzes, Certificates \u0026 eLearning",
"vendor": "kodezen",
"versions": [
{
"lessThanOrEqual": "4.0.1",
"status": "affected",
"version": "0",
"versionType": "semver"
}
]
}
],
"source": "security@wordfence.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "The Academy LMS \u2013 AI Course Builder, Quizzes, Certificates \u0026 eLearning plugin for WordPress is vulnerable to Directory Traversal in all versions up to, and including, 4.0.1 via the \u0027id\u0027 parameter. This makes it possible for authenticated attackers, with contributor-level access and above, to read the contents of arbitrary .html files on the server. The plugin appends a \u0027.html\u0027 extension to the attacker-supplied path before reading it, so files with any other extension \u2014 including wp-config.php \u2014 are out of reach. The file\u0027s contents are returned in full in the REST JSON response\u0027s content field."
}
],
"id": "CVE-2026-120544",
"lastModified": "2026-11-03T14:17:35.153",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 4.3,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 1.4,
"source": "security@wordfence.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-120544",
"options": [
{
"exploitation": "none"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-11-03T13:21:59.193506Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-11-03T05:18:32.427",
"references": [
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/academy/tags/4.0.1/includes/frontend/template/block.php#L12"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/academy/tags/4.0.1/includes/frontend/template/block.php#L267"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/academy/tags/4.0.1/includes/frontend/template/block.php#L30"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/academy/tags/4.0.1/includes/frontend/template/block.php#L50"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/changeset?reponame=\u0026old=3723396%40academy\u0026new=3723396%40academy"
},
{
"source": "security@wordfence.com",
"url": "https://www.wordfence.com/threat-intel/vulnerabilities/id/1f0a1439-d10c-4dfc-9a40-7319debe3c51?source=cve"
}
],
"sourceIdentifier": "security@wordfence.com",
"vulnStatus": "Deferred",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-22"
}
],
"source": "security@wordfence.com",
"type": "Secondary"
}
]
}
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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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.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.