FKIE_CVE-2026-64849
Vulnerability from fkie_nvd - Published: 2026-08-17 22:17 - Updated: 2026-08-20 19:16
Severity
Summary
MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. Prior to 3.15.0, the unauthenticated POST /api/2.0/mlflow/webhooks/{id}/test endpoint calls _validate_webhook_url() in mlflow/utils/validation.py only for the original URL while mlflow/webhooks/delivery.py follows redirects and re-resolves the hostname without pinning the validated address, allowing attackers to reach internal or cloud metadata services and receive response_status and response_body. This issue is fixed in version 3.15.0.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| lfprojects | mlflow | * |
{
"affected": [
{
"affectedData": [
{
"product": "mlflow",
"vendor": "mlflow",
"versions": [
{
"status": "affected",
"version": "\u003c 3.15.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cisaActionDue": "2026-09-02",
"cisaExploitAdd": "2026-08-19",
"cisaRequiredAction": "Apply mitigations in accordance with vendor instructions, ensuring compliance with CISA\u2019s BOD 26-04 Prioritizing Security Updates Based on Risk (see URL in Notes) guidance and CISA\u2019s \u201cForensics Triage Requirements\u201d (see URL in Notes). Follow applicable BOD 26-04 guidance for cloud services or discontinue use of the product if mitigations are unavailable. Stakeholders are responsible for evaluating each asset\u0027s internet exposure and ensuring adherence to BOD 26-04 patching guidelines.",
"cisaVulnerabilityName": "MLflow Server-Side Request Forgery Vulnerability",
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:lfprojects:mlflow:*:*:*:*:*:*:*:*",
"matchCriteriaId": "D762205E-C76F-495C-94C6-83DB1D4AF018",
"versionEndExcluding": "3.15.0",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. Prior to 3.15.0, the unauthenticated POST /api/2.0/mlflow/webhooks/{id}/test endpoint calls _validate_webhook_url() in mlflow/utils/validation.py only for the original URL while mlflow/webhooks/delivery.py follows redirects and re-resolves the hostname without pinning the validated address, allowing attackers to reach internal or cloud metadata services and receive response_status and response_body. This issue is fixed in version 3.15.0."
}
],
"id": "CVE-2026-64849",
"lastModified": "2026-08-20T19:16:57.867",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 9.3,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "CHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 4.7,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-64849",
"options": [
{
"exploitation": "active"
},
{
"automatable": "yes"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-08-20T03:55:29.820245Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-08-17T22:17:23.580",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Patch"
],
"url": "https://github.com/mlflow/mlflow/commit/ba949522477cbd5915aa55d29b0cfad7d5ddf939"
},
{
"source": "security-advisories@github.com",
"tags": [
"Exploit",
"Issue Tracking",
"Mitigation"
],
"url": "https://github.com/mlflow/mlflow/issues/24179"
},
{
"source": "security-advisories@github.com",
"tags": [
"Issue Tracking",
"Patch"
],
"url": "https://github.com/mlflow/mlflow/pull/24258"
},
{
"source": "security-advisories@github.com",
"tags": [
"Release Notes"
],
"url": "https://github.com/mlflow/mlflow/releases/tag/v3.15.0"
},
{
"source": "security-advisories@github.com",
"tags": [
"Exploit",
"Vendor Advisory"
],
"url": "https://github.com/mlflow/mlflow/security/advisories/GHSA-7gwp-5pfp-969j"
},
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"tags": [
"US Government Resource"
],
"url": "https://www.cisa.gov/known-exploited-vulnerabilities-catalog?field_cve=CVE-2026-64849"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-918"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
Loading…
Loading…
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.
Loading…
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.
Loading…
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.
Loading…