CVE-2026-48705

{"id": "CVE-2026-48705", "cveTags": [], "metrics": {"ssvcV203": [{"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "ssvcData": {"id": "CVE-2026-48705", "role": "CISA Coordinator", "options": [{"exploitation": "poc"}, {"automatable": "yes"}, {"technicalImpact": "partial"}], "version": "2.0.3", "timestamp": "2026-11-05T21:13:42.922873Z"}}], "cvssMetricV31": [{"type": "Secondary", "source": "security-advisories@github.com", "cvssData": {"scope": "CHANGED", "version": "3.1", "baseScore": 9.3, "attackVector": "NETWORK", "baseSeverity": "CRITICAL", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:N/I:H/A:L", "integrityImpact": "HIGH", "userInteraction": "NONE", "attackComplexity": "LOW", "availabilityImpact": "LOW", "privilegesRequired": "NONE", "confidentialityImpact": "NONE"}, "impactScore": 4.7, "exploitabilityScore": 3.9}]}, "affected": [{"source": "security-advisories@github.com", "affectedData": [{"vendor": "Project-MONAI", "product": "MONAI", "versions": [{"status": "affected", "version": "< 1.6.1"}]}]}], "published": "2026-11-05T21:18:50.270", "references": [{"url": "https://github.com/Project-MONAI/MONAI/commit/434c094a3e6b84cca5b6e81da51e0d18a0100607", "source": "security-advisories@github.com"}, {"url": "https://github.com/Project-MONAI/MONAI/pull/9087", "source": "security-advisories@github.com"}, {"url": "https://github.com/Project-MONAI/MONAI/releases/tag/1.6.1", "source": "security-advisories@github.com"}, {"url": "https://github.com/Project-MONAI/MONAI/security/advisories/GHSA-6hp3-vr39-rqw8", "source": "security-advisories@github.com"}, {"url": "https://github.com/Project-MONAI/MONAI/security/advisories/GHSA-6hp3-vr39-rqw8", "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0"}], "vulnStatus": "Deferred", "weaknesses": [{"type": "Secondary", "source": "security-advisories@github.com", "description": [{"lang": "en", "value": "CWE-20"}, {"lang": "en", "value": "CWE-682"}]}], "descriptions": [{"lang": "en", "value": "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."}], "lastModified": "2026-11-05T22:18:44.013", "sourceIdentifier": "security-advisories@github.com"}