FKIE_CVE-2026-87016
Vulnerability from fkie_nvd - Published: 2026-09-09 22:18 - Updated: 2026-09-10 14:50
Severity
Summary
Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform. From 0.6.41 until 0.11.1, get_user_by_oauth_sub and get_user_by_scim_external_id in backend/open_webui/models/users.py used JSON contains matching that compiled to SQL LIKE substring matching on SQLite. An OAuth subject containing percent or underscore wildcard characters could resolve to a different stored identity, potentially selecting an administrator account and issuing the attacker that account's session; PostgreSQL deployments were not affected. This issue is fixed in version 0.11.1.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "open-webui",
"vendor": "open-webui",
"versions": [
{
"status": "affected",
"version": "\u003e= 0.6.41, \u003c 0.11.1"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform. From 0.6.41 until 0.11.1, get_user_by_oauth_sub and get_user_by_scim_external_id in backend/open_webui/models/users.py used JSON contains matching that compiled to SQL LIKE substring matching on SQLite. An OAuth subject containing percent or underscore wildcard characters could resolve to a different stored identity, potentially selecting an administrator account and issuing the attacker that account\u0027s session; PostgreSQL deployments were not affected. This issue is fixed in version 0.11.1."
}
],
"id": "CVE-2026-87016",
"lastModified": "2026-09-10T14:50:07.813",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 8.1,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 2.2,
"impactScore": 5.9,
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
},
"published": "2026-09-09T22:18:46.720",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/open-webui/open-webui/commit/73c1f5806aeb6345dad5de8f5aa26d1f3d0bef80"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/open-webui/open-webui/pull/28624"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/open-webui/open-webui/releases/tag/v0.11.1"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/open-webui/open-webui/security/advisories/GHSA-wpmr-8h3q-fwj7"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Undergoing Analysis",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-155"
},
{
"lang": "en",
"value": "CWE-287"
}
],
"source": "security-advisories@github.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.
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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