MONAILabel Server Crash and Silent Inference Corruption via Crafted DICOM (pydicom DS/IS Validation Bypass)
What fixes it
A fixed version on one release line is not the upgrade target for every line. Check your exact version and any vendor backports.
Description
Summary
An unauthenticated remote attacker can crash a running MONAILabel server or silently corrupt its medical AI inference results by uploading a single crafted DICOM file (~132 KB) via the REST API. The vulnerability stems from pydicom's lack of validation for Decimal String (DS) and Integer String (IS) DICOM Value Representations, which allows NaN and infinity values to pass through MONAI's PydicomReader and propagate through the entire spatial transform and ML inference pipeline.
No authentication is required for the default MONAILabel REST API. The attacker needs only network access to the server (default port 8000).
Severity: CVSS 3.1: 8.7 HIGH CVSS Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:N/I:H/A:L CWE: CWE-20 (Improper Input Validation) → CWE-682 (Incorrect Calculation)
Details
Root Cause — Layer 1: pydicom accepts invalid VR values
pydicom's DSfloat.__new__() calls Python's float(val), which silently accepts "NaN", "inf", and "-inf". The DICOM standard (PS3.5 Table 6.2-1) defines DS as digits 0-9, +, -, ., e only. NaN and inf are not valid DS values. The is_valid_ds() check only runs in RAISE validation mode, which is NOT the default. The default mode is WARN, which still returns the invalid value.
>>> from pydicom.valuerep import DSfloat
>>> DSfloat("NaN")
nan # Accepted silently — should be rejected
>>> DSfloat("inf")
inf # Accepted silently — should be rejectedThis affects ALL pydicom versions from 0.9.7 through 3.0.2 (current).
A related bypass exists in pydicom's IS type: IS("1e4") returns 10000 via an int(float(val)) fallback, accepting scientific notation that is not valid per the DICOM standard.
Root Cause — Layer 2: MONAI Core trusts pydicom output without validation
MONAI's PydicomReader._get_affine() builds a 4x4 affine transformation matrix from PixelSpacing, ImagePositionPatient, and ImageOrientationPatient with ZERO isfinite() or isnan() checks. NaN in PixelSpacing produces NaN in the affine diagonal, propagating through every spatial transform: PydicomReader._get_affine() → LoadImaged → Spacingd → Model → Writer
Root Cause — Layer 3: MONAILabel REST API accepts anything
MONAILabel's PUT /datastore/ endpoint accepts DICOM uploads with no authentication (default deployment), no validation of spatial metadata, and no bounds checking on any DICOM tag values. The malicious DICOM is stored permanently and made available for inference.
PoC
Environment Setup:
# Start MONAILabel (Docker)
docker pull projectmonai/monailabel:latest
docker run -d --name monailabel-poc -p 8000:8000 --memory=8g \
projectmonai/monailabel:latest monailabel start_server \
--app /opt/monai/sample-apps/radiology \
--studies /opt/monai/datasets/Task09_Spleen/imagesTr \
--conf models segmentation
# Verify server is running
curl http://localhost:8000/info/Step 1 — Establish baseline (legitimate data works):
curl -X POST "http://localhost:8000/infer/segmentation?image=spleen_40" \
-o baseline.nii.gz -w "HTTP %{http_code} - %{size_download} bytes"
# Result: HTTP 200, ~223,628 bytes valid segmentation maskStep 2 — Run the attack (NaN DICOM upload + inference):
pip install pydicom numpy requests python poc_monailabel_ds_bypass.py --server http://localhost:8000
The PoC script:
- Confirms pydicom accepts NaN/inf in DS fields
- 2. Creates three malicious DICOM files (~132 KB each) with NaN/inf in PixelSpacing and ImagePositionPatient
- 3. Uploads each via
PUT /datastore/(no auth required) — HTTP 200 accepted - 4. Triggers inference via
POST /infer/segmentation— HTTP 500, server crash
Demonstrated Results:
| Payload | Upload | Inference | Result | |---|---|---|---| | Baseline (spleen_40) | N/A | HTTP 200 | Valid 223 KB segmentation | | NaN PixelSpacing | HTTP 200 | HTTP 500 | RuntimeError crash (18.0s wasted compute) | | NaN PixelSpacing + IPP | HTTP 200 | HTTP 500 | RuntimeError crash (16.7s wasted compute) | | Inf PixelSpacing | HTTP 200 | HTTP 500 | RuntimeError crash (16.4s wasted compute) |
Server crash traceback:
monailabel/tasks/infer/basic_infer.py → self.writer(data) monailabel/transform/writer.py → write_itk(...) itk/ITKCommonBasePython.py → itkProcessObject_Update() RuntimeError: itkGDCMImageIO.cxx:1226: ITK ERROR: GDCMImageIO: A Floating point buffer was passed but the stored pixel type was not specified.
Each request burns ~16 seconds of server compute (full ML inference pipeline) before crashing. The NaN DICOM persists in the datastore — any future inference on that image ID crashes the server again.
Library-level confirmation: 17/17 test vectors confirmed against MONAI 1.5.2 and pydicom 3.0.2, including actual MONAI LoadImaged and Spacingd transforms accepting NaN geometry without error.
Impact
- Denial of Service: A single ~132 KB DICOM upload crashes the MONAILabel inference pipeline. All active inference requests from legitimate users are aborted. Repeatable indefinitely with minimal bandwidth.
- Silent Data Corruption: NaN values in spatial metadata propagate through the entire ML inference pipeline and produce geometrically incorrect segmentation masks. If the output writer does not crash, the result is returned with HTTP 200 and zero warnings.
- Persistent Datastore Poisoning: The malicious DICOM is stored permanently via
PUT /datastore/. Any future inference request on that image ID reproduces the crash or corruption, persisting across server restarts.
Affected MONAI Ecosystem (9 repositories):
- MONAI Core: PydicomReader, LoadImaged, Spacingd transforms
- - MONAILabel: REST API + DICOMweb datastore
- - MONAI Deploy App SDK: DICOMDataLoaderOperator
- - model-zoo, VISTA, GenerativeModels, tutorials, research-contributions
Affected DICOM Tags (any DS VR): PixelSpacing, ImagePositionPatient, SliceThickness, ImageOrientationPatient, RescaleSlope, WindowCenter/WindowWidth
Recommended Fix:
- Add
math.isfinite()validation inPydicomReader._get_affine()on all values from PixelSpacing, IPP, IOP. Raise ValueError for NaN/inf. - 2. Validate spatial metadata of uploaded DICOM in MONAILabel
PUT /datastore/. Return HTTP 400 for invalid geometry. - 3. Validate spatial metadata is finite in
LoadImaged/Spacingdbefore resampling. - 4. Coordinate with pydicom upstream to reject NaN/inf in DS fields in all validation modes.
Upstream: pydicom GHSA filed 2026-04-05.
Author: Paulino Calderon (paulino@calderonpale.com)
Text from GitHub Advisory Database
Affected versions 47
- monai, pip< 1.6.1GitHub
- monai, PyPIfrom first release, fixed in 1.6.1OSV
- monai, PyPI0.0.1OSV
- monai, PyPI0.1.0OSV
- monai, PyPI0.2.0OSV
- monai, PyPI0.3.0OSV
- monai, PyPI0.4.0OSV
- monai, PyPI0.5.0OSV
- monai, PyPI0.5.1OSV
- monai, PyPI0.5.2OSV
- monai, PyPI0.5.3OSV
- monai, PyPI0.6.0OSV
- monai, PyPI0.7.0OSV
- monai, PyPI0.8.0OSV
- monai, PyPI0.8.1OSV
- monai, PyPI0.9.0OSV
- monai, PyPI0.9.1OSV
- monai, PyPI1.0.0OSV
- monai, PyPI1.0.1OSV
- monai, PyPI1.1.0OSV
- monai, PyPI1.2.0OSV
- monai, PyPI1.3.0OSV
- monai, PyPI1.3.1OSV
- monai, PyPI1.3.2OSV
- monai, PyPI1.3.2rc1OSV
- monai, PyPI1.3.3rc1OSV
- monai, PyPI1.4.0OSV
- monai, PyPI1.4.0rc1OSV
- monai, PyPI1.4.0rc10OSV
- monai, PyPI1.4.0rc11OSV
- monai, PyPI1.4.0rc12OSV
- monai, PyPI1.4.0rc2OSV
- monai, PyPI1.4.0rc3OSV
- monai, PyPI1.4.0rc4OSV
- monai, PyPI1.4.0rc5OSV
- monai, PyPI1.4.0rc6OSV
- monai, PyPI1.4.0rc7OSV
- monai, PyPI1.4.0rc8OSV
- monai, PyPI1.4.0rc9OSV
- monai, PyPI1.4.1rc1OSV
- monai, PyPI1.5.0OSV
- monai, PyPI1.5.0rc1OSV
- monai, PyPI1.5.1OSV
- monai, PyPI1.5.2OSV
- monai, PyPI1.5.2rc1OSV
- monai, PyPI1.6.0OSV
- monai, PyPI1.6.1rc0OSV
References 4
- github.com/Project-MONAI/MONAI/commit/434c094a3e6b84cca5b6e81da51e0d18a0100607Fixvia GitHub
- github.com/Project-MONAI/MONAI/releases/tag/1.6.1Fixvia GitHub
- github.com/Project-MONAI/MONAI/security/advisories/GHSA-6hp3-vr39-rqw8Advisoryvia GitHub
- github.com/advisories/GHSA-6hp3-vr39-rqw8Advisoryvia GitHub