CVE-2026-56121 (GCVE-0-2026-56121)
Vulnerability from cvelistv5 – Published: 2026-06-24 14:49 – Updated: 2026-08-28 12:04 X_Open Source
VLAI
EPSS
VEX
Title
Feast < 0.63.0 Unauthenticated RCE via ApplyFeatureView gRPC Deserialization
Summary
Feast before 0.63.0 contains an unsafe deserialization vulnerability that allows unauthenticated or unauthorized attackers to achieve remote code execution by sending a crafted gRPC request to the registry server. The user_defined_function.body field of an OnDemandFeatureView spec is decoded from base64 and passed to dill.loads() before any authorization check is performed, enabling attackers to embed a malicious serialized Python object with an arbitrary __reduce__ method to execute OS commands as the feast service account.
Severity
9.8 (Critical)
8.8 (High)
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-25 00:00 UTC
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
8 references
| URL | Tags |
|---|---|
| https://huntr.com/bounties/d64b8111-180b-46ba-afa… | technical-descriptionexploit |
| https://github.com/feast-dev/feast/releases/tag/v0.63.0 | release-notes |
| https://github.com/feast-dev/feast/commit/835cda8… | patch |
| https://www.vulncheck.com/advisories/feast-unauth… | third-party-advisory |
| https://access.redhat.com/security/cve/CVE-2026-56121 | vdb-entryx_refsource_REDHAT |
| https://bugzilla.redhat.com/show_bug.cgi?id=2492229 | issue-trackingx_refsource_REDHAT |
| https://security.access.redhat.com/data/csaf/v2/v… | x_sadp-csaf-vex |
| https://access.redhat.com/errata/RHSA-2026:60520 | vendor-advisoryx_refsource_REDHAT |
Impacted products
13 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| feast-dev | feast |
Affected:
0 , < 0.63.0
(semver)
|
guessed | |
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1787073866 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1787073873 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1787073459 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1787073611 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1787073451 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1787121387 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1787074331 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1787073605 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1787073546 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1787073717 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1787073713 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai
|
Date Public
2026-02-21 00:00
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"value": "Feast before 0.63.0 contains an unsafe deserialization vulnerability that allows unauthenticated or unauthorized attackers to achieve remote code execution by sending a crafted gRPC request to the registry server. The user_defined_function.body field of an OnDemandFeatureView spec is decoded from base64 and passed to dill.loads() before any authorization check is performed, enabling attackers to embed a malicious serialized Python object with an arbitrary __reduce__ method to execute OS commands as the feast service account."
}
],
"metrics": [
{
"cvssV4_0": {
"Automatable": "NOT_DEFINED",
"Recovery": "NOT_DEFINED",
"Safety": "NOT_DEFINED",
"attackComplexity": "LOW",
"attackRequirements": "NONE",
"attackVector": "NETWORK",
"baseScore": 9.3,
"baseSeverity": "CRITICAL",
"exploitMaturity": "NOT_DEFINED",
"privilegesRequired": "NONE",
"providerUrgency": "NOT_DEFINED",
"subAvailabilityImpact": "NONE",
"subConfidentialityImpact": "NONE",
"subIntegrityImpact": "NONE",
"userInteraction": "NONE",
"valueDensity": "NOT_DEFINED",
"vectorString": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N",
"version": "4.0",
"vulnAvailabilityImpact": "HIGH",
"vulnConfidentialityImpact": "HIGH",
"vulnIntegrityImpact": "HIGH",
"vulnerabilityResponseEffort": "NOT_DEFINED"
},
"format": "CVSS",
"scenarios": [
{
"lang": "en",
"value": "GENERAL"
}
]
},
{
"cvssV3_1": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 9.8,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"format": "CVSS",
"scenarios": [
{
"lang": "en",
"value": "GENERAL"
}
]
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-502",
"description": "Deserialization of Untrusted Data",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2026-07-14T21:34:05.976Z",
"orgId": "83251b91-4cc7-4094-a5c7-464a1b83ea10",
"shortName": "VulnCheck"
},
"references": [
{
"name": "Huntr Disclosure",
"tags": [
"technical-description",
"exploit"
],
"url": "https://huntr.com/bounties/d64b8111-180b-46ba-afa3-c877fda2ede6"
},
{
"name": "Release Notes",
"tags": [
"release-notes"
],
"url": "https://github.com/feast-dev/feast/releases/tag/v0.63.0"
},
{
"name": "Patch Commit",
"tags": [
"patch"
],
"url": "https://github.com/feast-dev/feast/commit/835cda8e2c1359f1f496ad72701dbd6a73bdb25a"
},
{
"tags": [
"third-party-advisory"
],
"url": "https://www.vulncheck.com/advisories/feast-unauthenticated-rce-via-applyfeatureview-grpc-deserialization"
}
],
"source": {
"discovery": "UNKNOWN"
},
"tags": [
"x_open-source"
],
"title": "Feast \u003c 0.63.0 Unauthenticated RCE via ApplyFeatureView gRPC Deserialization",
"x_generator": {
"engine": "vulncheck"
}
}
},
"cveMetadata": {
"assignerOrgId": "83251b91-4cc7-4094-a5c7-464a1b83ea10",
"assignerShortName": "VulnCheck",
"cveId": "CVE-2026-56121",
"datePublished": "2026-06-24T14:49:11.490Z",
"dateReserved": "2026-06-18T19:15:10.651Z",
"dateUpdated": "2026-08-28T12:04:16.268Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
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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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