FKIE_CVE-2026-115932
Vulnerability from fkie_nvd - Published: 2026-10-24 13:16 - Updated: 2026-10-24 13:16
Severity
Summary
When a post is published, Postiz treats a media path that is not a web address as a file on the server's disk, but it does not check that the file lies inside the upload directory. A registered user can store a crafted media path with a post, get it past the checks that normally restrict it, and have the publishing worker read an arbitrary file and send it as an attachment to a Discord server the user controls.
The same unconfined reads can be reached through two further routes. One tells the caller whether a file exists and can exhaust the backend's memory, the other delivers the file the same way as the main route. Self-registration is open by default, so the attacker needs no existing account.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"defaultStatus": "unaffected",
"packageName": "postiz-app",
"product": "postiz-app",
"repo": "https://github.com/gitroomhq/postiz-app",
"vendor": "GitroomHQ",
"versions": [
{
"lessThan": "2.25.3",
"status": "affected",
"version": "2.23.0",
"versionType": "semver"
}
]
}
],
"source": "4cdc9741-f887-419a-a2fd-acbbd2729276"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "When a post is published, Postiz treats a media path that is not a web address as a file on the server\u0027s disk, but it does not check that the file lies inside the upload directory. A registered user can store a crafted media path with a post, get it past the checks that normally restrict it, and have the publishing worker read an arbitrary file and send it as an attachment to a Discord server the user controls.\n\nThe same unconfined reads can be reached through two further routes. One tells the caller whether a file exists and can exhaust the backend\u0027s memory, the other delivers the file the same way as the main route. Self-registration is open by default, so the attacker needs no existing account."
}
],
"id": "CVE-2026-115932",
"lastModified": "2026-10-24T13:16:37.217",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 9.6,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "CHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.1,
"impactScore": 5.8,
"source": "4cdc9741-f887-419a-a2fd-acbbd2729276",
"type": "Secondary"
}
]
},
"published": "2026-10-24T13:16:37.217",
"references": [
{
"source": "4cdc9741-f887-419a-a2fd-acbbd2729276",
"url": "https://gadvisory.org/advisories/PSA-2026-E9AHFV"
},
{
"source": "4cdc9741-f887-419a-a2fd-acbbd2729276",
"url": "https://github.com/gitroomhq/postiz-app"
},
{
"source": "4cdc9741-f887-419a-a2fd-acbbd2729276",
"url": "https://github.com/gitroomhq/postiz-app/commit/12db9bd2f428b628a62809669c3d76297e07750a"
},
{
"source": "4cdc9741-f887-419a-a2fd-acbbd2729276",
"url": "https://github.com/gitroomhq/postiz-app/releases/tag/v2.25.3"
}
],
"sourceIdentifier": "4cdc9741-f887-419a-a2fd-acbbd2729276",
"vulnStatus": "Received",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-22"
},
{
"lang": "en",
"value": "CWE-74"
}
],
"source": "4cdc9741-f887-419a-a2fd-acbbd2729276",
"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
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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.