MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging. Prior to 1.6.1, PydicomReader._get_affine() trusts non-finite values in the DICOM PixelSpacing, ImagePositionPatient, and ImageOrientationPatient fields and composes them into an affine matrix without validation. An unauthenticated attacker who can upload a crafted DICOM to a default MONAILabel deployment can propagate NaN or infinity values through LoadImaged, Spacingd, model inference, and output writing, causing inference failures, persistent datastore poisoning, or geometrically corrupted results. This issue is fixed in version 1.6.1.

No CVSS v4.0

Attack Vector Network

Attack Complexity Low

Privileges Required None

Scope Changed

Confidentiality Impact None

Integrity Impact High

Availability Impact Low

User Interaction None

No CVSS v3.0

No CVSS v2

This CVE is not in the KEV list.

No EPSS score available.

Key SSVC decision points have not yet been added.

Default status is the baseline for the product, each version can override it (e.g. patched versions marked unaffected).

Vendor Product Default status Versions
Project-MONAI MONAI affected
Version Status Constraints
< 1.6.1 affected —

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Project Subscriptions

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Advisories
Source ID Title
Github GHSA Github GHSA GHSA-6hp3-vr39-rqw8 MONAILabel Server Crash and Silent Inference Corruption via Crafted DICOM (pydicom DS/IS Validation Bypass)
Fixes

Solution

No solution given by the vendor.

Workaround

No workaround given by the vendor.

References
Link Providers
https://github.com/Project-MONAI/MONAI/commit/434c094a3e6b84cca5b6e81da51e0d18a0100607 cve-icon
https://github.com/Project-MONAI/MONAI/pull/9087 cve-icon
https://github.com/Project-MONAI/MONAI/releases/tag/1.6.1 cve-icon
https://github.com/Project-MONAI/MONAI/security/advisories/GHSA-6hp3-vr39-rqw8 cve-icon
History

Thu, 05 Nov 2026 20:45:00 +0000

Type Values Removed Values Added
Description MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging. Prior to 1.6.1, PydicomReader._get_affine() trusts non-finite values in the DICOM PixelSpacing, ImagePositionPatient, and ImageOrientationPatient fields and composes them into an affine matrix without validation. An unauthenticated attacker who can upload a crafted DICOM to a default MONAILabel deployment can propagate NaN or infinity values through LoadImaged, Spacingd, model inference, and output writing, causing inference failures, persistent datastore poisoning, or geometrically corrupted results. This issue is fixed in version 1.6.1.
Title MONAI: MONAILabel Server Crash and Silent Inference Corruption via Crafted DICOM (pydicom DS/IS Validation Bypass)
Weaknesses CWE-20
CWE-682
References
Metrics cvssV3_1

{'score': 9.3, 'vector': 'CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:N/I:H/A:L'}

Projects

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cve-icon MITRE

Status: PUBLISHED

Assigner: GitHub_M

Published:

Updated: 2026-11-05T20:44:30.681Z

Reserved: 2026-05-22T18:47:27.754Z

Link: CVE-2026-48705

cve-icon Vulnrichment

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cve-icon NVD

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cve-icon Redhat

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cve-icon OpenCVE Enrichment

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Weaknesses