FKIE_CVE-2026-18947
Vulnerability from fkie_nvd - Published: 2026-08-10 21:17 - Updated: 2026-08-19 11:16
Severity
Summary
A flaw was found in Feast. An authorization bypass vulnerability exists in the /materialize and /materialize-incremental endpoints. By sending a specially crafted request that omits the feature_views field, an attacker can bypass intended permission checks. This allows an unauthenticated remote attacker, or any authenticated user, to trigger a full re-materialization of all feature views. The consequence is a Denial of Service (DoS) due to data corruption and significant resource consumption across all tenants.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"collectionURL": "https://catalog.redhat.com/software/containers/",
"cpes": [
"cpe:/a:redhat:openshift_ai:2.25::el9"
],
"defaultStatus": "affected",
"packageName": "rhoai/odh-feature-server-rhel9",
"product": "Red Hat OpenShift AI 2.25",
"vendor": "Red Hat",
"versions": [
{
"lessThan": "*",
"status": "unaffected",
"version": "1786110051",
"versionType": "rpm"
}
]
},
{
"collectionURL": "https://catalog.redhat.com/software/containers/",
"cpes": [
"cpe:/a:redhat:openshift_ai:3.3::el9"
],
"defaultStatus": "affected",
"packageName": "rhoai/odh-feature-server-rhel9",
"product": "Red Hat OpenShift AI 3.3",
"vendor": "Red Hat",
"versions": [
{
"lessThan": "*",
"status": "unaffected",
"version": "1786110033",
"versionType": "rpm"
}
]
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-pipeline-runtime-pytorch-llmcompressor-cuda-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-pipeline-runtime-pytorch-rocm-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-pipeline-runtime-tensorflow-cuda-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-pipeline-runtime-tensorflow-rocm-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-workbench-codeserver-datascience-cpu-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-workbench-jupyter-datascience-cpu-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-workbench-jupyter-pytorch-cuda-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-workbench-jupyter-pytorch-rocm-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-workbench-jupyter-tensorflow-cuda-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
},
{
"collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
"cpes": [
"cpe:/a:redhat:openshift_ai"
],
"defaultStatus": "unaffected",
"packageName": "rhoai/odh-workbench-jupyter-tensorflow-rocm-py312-rhel9",
"product": "Red Hat OpenShift AI (RHOAI)",
"vendor": "Red Hat"
}
],
"source": "secalert@redhat.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "A flaw was found in Feast. An authorization bypass vulnerability exists in the /materialize and /materialize-incremental endpoints. By sending a specially crafted request that omits the feature_views field, an attacker can bypass intended permission checks. This allows an unauthenticated remote attacker, or any authenticated user, to trigger a full re-materialization of all feature views. The consequence is a Denial of Service (DoS) due to data corruption and significant resource consumption across all tenants."
}
],
"id": "CVE-2026-18947",
"lastModified": "2026-08-19T11:16:46.447",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "LOW",
"baseScore": 8.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "CHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:N/I:H/A:L",
"version": "3.1"
},
"exploitabilityScore": 3.1,
"impactScore": 4.7,
"source": "secalert@redhat.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-18947",
"options": [
{
"exploitation": "none"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-08-11T15:28:17.828269Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-08-10T21:17:21.130",
"references": [
{
"source": "secalert@redhat.com",
"url": "https://access.redhat.com/errata/RHSA-2026:53261"
},
{
"source": "secalert@redhat.com",
"url": "https://access.redhat.com/errata/RHSA-2026:53263"
},
{
"source": "secalert@redhat.com",
"url": "https://access.redhat.com/security/cve/CVE-2026-18947"
},
{
"source": "secalert@redhat.com",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2511164"
}
],
"sourceIdentifier": "secalert@redhat.com",
"vulnStatus": "Awaiting Analysis",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-862"
}
],
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"type": "Secondary"
}
]
}
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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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