FKIE_CVE-2026-83526
Vulnerability from fkie_nvd - Published: 2026-10-10 06:16 - Updated: 2026-10-10 06:16
Severity
Summary
The FV Player 8 plugin for WordPress is vulnerable to Arbitrary File Upload in all versions up to, and including, 8.1.7 via the check_mimetype function. This is due to insufficient file type validation in check_mimetype(), which writes attacker-supplied remote file content to the public uploads directory before any MIME or extension check, combined with a missing capability check on new player creation. This makes it possible for authenticated attackers, with subscriber-level access and above, to upload files that may be executable, which makes remote code execution possible. This requires successfully exploiting a race condition.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"defaultStatus": "unaffected",
"product": "FV Player 8",
"vendor": "foliovision",
"versions": [
{
"lessThanOrEqual": "8.1.7",
"status": "affected",
"version": "0",
"versionType": "semver"
}
]
}
],
"source": "security@wordfence.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "The FV Player 8 plugin for WordPress is vulnerable to Arbitrary File Upload in all versions up to, and including, 8.1.7 via the check_mimetype function. This is due to insufficient file type validation in check_mimetype(), which writes attacker-supplied remote file content to the public uploads directory before any MIME or extension check, combined with a missing capability check on new player creation. This makes it possible for authenticated attackers, with subscriber-level access and above, to upload files that may be executable, which makes remote code execution possible. This requires successfully exploiting a race condition."
}
],
"id": "CVE-2026-83526",
"lastModified": "2026-10-10T06:16:43.340",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 8.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 5.9,
"source": "security@wordfence.com",
"type": "Primary"
}
]
},
"published": "2026-10-10T06:16:43.340",
"references": [
{
"source": "security@wordfence.com",
"url": "https://github.com/foliovision/fv-wordpress-flowplayer/releases/tag/8.1.8"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/fv-player/trunk/controller/editor.php#L36"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/fv-player/trunk/controller/frontend.php#L1211"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/fv-player/trunk/models/checker.php#L105"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/fv-player/trunk/models/checker.php#L148"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/fv-player/trunk/models/checker.php#L181"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/fv-player/trunk/models/db-video.php#L855"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/fv-player/trunk/models/db.php#L1102"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/fv-player/trunk/models/db.php#L1173"
},
{
"source": "security@wordfence.com",
"url": "https://plugins.trac.wordpress.org/browser/fv-player/trunk/models/db.php#L46"
},
{
"source": "security@wordfence.com",
"url": "https://www.wordfence.com/threat-intel/vulnerabilities/id/5c75cb7b-1ebe-411c-8664-848d741e40d4?source=cve"
}
],
"sourceIdentifier": "security@wordfence.com",
"vulnStatus": "Received",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-434"
}
],
"source": "security@wordfence.com",
"type": "Primary"
}
]
}
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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.
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.
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.
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