FKIE_CVE-2026-88002
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 0.5.0 until 0.11.1, the message-chain reconstruction helper in backend/open_webui/utils/misc.py advanced through a chat history by map key but tracked visited entries using each message body's optional id field. An authenticated user could store id-less messages in a parent cycle and trigger a non-terminating walk that blocked the async event loop, grew memory until termination, and remained persistent across process restarts. 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= 0.5.0, \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 0.5.0 until 0.11.1, the message-chain reconstruction helper in backend/open_webui/utils/misc.py advanced through a chat history by map key but tracked visited entries using each message body\u0027s optional id field. An authenticated user could store id-less messages in a parent cycle and trigger a non-terminating walk that blocked the async event loop, grew memory until termination, and remained persistent across process restarts. This issue is fixed in version 0.11.1."
}
],
"id": "CVE-2026-88002",
"lastModified": "2026-09-10T14:50:07.813",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 6.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 3.6,
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
},
"published": "2026-09-09T22:18:49.080",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/open-webui/open-webui/commit/5c79ccc9e5c9efc2bc024d8f0b9757652ece929a"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/open-webui/open-webui/pull/28034"
},
{
"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-jqhh-cjmq-vmv6"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Undergoing Analysis",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-835"
}
],
"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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