MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging.
CVE-2026-48705 is a critical-severity improper input validation vulnerability in MONAI from Project-MONAI, with a CVSS 3.1 base score of 9.3 out of 10. It can be exploited remotely over the network by an unauthenticated attacker, without any action from a user. Affected MONAI versions: < 1.6.1. The record does not name a fixed version, so check the vendor advisory. There is no confirmed exploitation so far. EPSS estimates a 0.51% chance of exploitation within 30 days, which is low compared with other CVEs.
Critical severity: a successful attack would have severe impact.
What to do
- Check which version of MONAI you run against the affected versions below, and install the vendor's latest release.
- Read the advisory on github.com for workarounds and exact fixed builds.
- Until you can update, restrict network access to the affected service so it is not reachable from untrusted networks.
Affected products and versions
| Vendor | Product | Affected versions | Fixed in |
|---|---|---|---|
| Project-MONAI | MONAI | < 1.6.1 | Not stated |
Version data comes from the organisation that assigned the CVE. Confirm against the vendor advisory before deciding you are unaffected.
How the severity score breaks down
- Attack vector
- NetworkCan be exploited remotely over a network, including the internet.
- Attack complexity
- LowNo special conditions are needed; the attack works reliably.
- Privileges required
- NoneNo account or login is needed.
- User interaction
- NoneNo action by a victim is required.
- Scope
- ChangedThe attack can reach beyond the vulnerable component into other systems.
- Confidentiality
- NoneNo data is disclosed.
- Integrity
- HighData or code can be modified at will.
- Availability
- LowPerformance or availability is reduced.
Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:N/I:H/A:L. Scored by CNA (github.com).
Official 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.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Improper Input Validation
The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.
How developers prevent this class of flaw
- Consider using language-theoretic security (LangSec) techniques that characterize inputs using a formal language and build recognizers for that language. This effectively requires parsing to be a distinct layer that effectively enforces a boundary between raw input and internal data representations, instead of allowing parser code to be scattered throughout the program, where it could be subject to errors or inconsistencies that create weaknesses.
- Use an input validation framework such as Struts or the OWASP ESAPI Validation API. Note that using a framework does not automatically address all input validation problems; be mindful of weaknesses that could arise from misusing the framework itself (CWE-1173).
- Understand all the potential areas where untrusted inputs can enter the product, including but not limited to: parameters or arguments, cookies, anything read from the network, environment variables, reverse DNS lookups, query results, request headers, URL components, e-mail, files, filenames, databases, and any external systems that provide data to the application. Remember that such inputs may be obtained indirectly through API calls.
Timeline
- CVE record published.
- CISA published its SSVC assessment (exploitation: poc, automatable: yes, technical impact: partial).
References
Patches and release notes
Other vulnerabilities in MONAI
- 7.8 High severity
- 8.8 High severity
- 7.3 High severity
- 7.5 High severity
- 8.8 High severity
- 7.8 High severity
Recent Improper Input Validation vulnerabilities
- 9.1 Critical severity
- 8.8 High severity
- 8.1 High severity
- 7.2 High severity
- 7.7 High severity
- 8.8 High severity
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