EUVD-2026-74038
GHSA-jw72-756q-9hfr
Severity by source
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
Network-accessible registry fetch (AV:N), no complexity (AC:L), low-privileged load operation (PR:L), victim must actively load the artifact (UI:R), no scope change, full system impact.
Primary rating from Vendor (6f8de1f0-f67e-45a6-b68f-98777fdb759c).
CVSS VectorVendor: 6f8de1f0-f67e-45a6-b68f-98777fdb759c
Lifecycle Timeline
2DescriptionCVE.org
Code execution can occur in versions of the MLflow platform running version 0.0.1 or newer, enabling a maliciously crafted model artifact to execute arbitrary code on an end user's system when loaded by the project.
AnalysisAI
Arbitrary code execution in MLflow (all versions ≥ 0.0.1) is achievable through a maliciously crafted model artifact that exploits the platform's model-loading pipeline, triggering code execution on the end user's system when the artifact is deserialized. An attacker with the ability to publish or distribute a poisoned model to a shared registry can fully compromise any data scientist or ML engineer who loads it, gaining complete control over their workstation or compute environment. …
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Attack ChainAIDerived
Hypothetical attack flow derived from CVE metadata
Vulnerability AssessmentAI
| Exploitation | The victim must actively call an MLflow model-loading API (e.g., mlflow.load_model(), mlflow.pyfunc.load_model(), or a flavor-specific loader) against an attacker-controlled artifact. … Additional conditions and limiting factors are described in the full assessment. |
| Risk Assessment | The CVSS 4.0 base score of 8.6 reflects high CIA impact on the vulnerable system with no subsequent-system impact recorded (SC:N/SI:N/SA:N). … Full risk analysis with EPSS, KEV, and SSVC signal comparison available after sign-in. |
| Exploit Scenario | Full exploit scenario with step-by-step reproduction available after sign-in. |
| Remediation | No vendor-released patch version is identified in the available data - the affected version range spans all releases, and the HiddenLayer advisory (https://www.hiddenlayer.com/sai-security-advisory/2026-09-mlflow) should be consulted for any coordinated fix timeline. … Detailed patch versions, workarounds, and compensating controls in full report. |
Recommended ActionAI
Within 24 hours: Inventory all MLflow deployments and model registries across your organization and notify all data science, ML engineering, and research teams of this vulnerability. …
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External POC / Exploit Code
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