Improper input validation in Project-Monai Monai

CVE-2026-48705

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.

Vulnerability class: Drupalgeddon 2 (CVE-2018-7600)

Published · last modified .

CVSS v3 metric

CVSS v3 base score 9.3 (Critical). Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:N/I:H/A:L.

EPSS exploit prediction

No EPSS score from FIRST.org yet. New CVEs usually get one within a few days; no score means no signal yet, not low risk. How EPSS works.

Affected products

Weakness classification (CWE)

References

Frequently asked questions

What is CVE-2026-48705?
CVE-2026-48705 is a critical-severity vulnerability in Project-Monai Monai, classified under Improper Input Validation. CVSS score: 9.3/10. Published 2026-11-05.
How severe is CVE-2026-48705?
Critical severity. CVSS v3 base score is 9.3 out of 10.