Project-MONAI vulnerabilities
We track 15 vulnerabilities in Project-MONAI products. 15 are rated high or critical, and none are known to be exploited. The most recent was published on November 5, 2026.
Latest Project-MONAI vulnerabilities
- 9.3 Critical severity
MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging.
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 aff
- 7.8 High severity
MONAI: Use of Externally-Controlled Input to Select Classes or Code
MONAI from 1.6.0 before 1.6.1 contains a remote code execution vulnerability in Auto3DSeg algo_from_json() that allows attackers to run Python code via a crafted work directory's algo_object.json. Attackers can ship a malicious algorithm_templates module plus
- 8.8 High severity
MONAI: Improper Control of Generation of Code
MONAI from 1.2.0 before 1.6.1 contains a remote code execution vulnerability in ConfigWorkflow.__init__, which passes a bundle's configs/logging.conf to logging.config.fileConfig that eval()s handler class and args fields. Attackers can publish a malicious bun
- 7.3 High severity
MONAI: Deserialization of Untrusted Data
MONAI 1.2.0 before 1.6.1 contains an insecure deserialization vulnerability in nnUNetV2Runner that unpickles the postprocessing_file path read from inference_information.json without confining it to the results directory. Attackers who can modify inference_inf
- 7.5 High severity
MONAI: Deserialization of Untrusted Data
MONAI 0.4.0 before 1.6.1 contains a remote code execution vulnerability that allows on-path attackers to execute code by tampering with SENet pretrained weights fetched over plaintext HTTP. Attackers can substitute a crafted .pth file that load_state_dict_from
- 8.8 High severity
MONAI: Deserialization of Untrusted Data
MONAI before 1.5.2 contains a deserialization of untrusted data vulnerability in the algo_from_pickle function in monai/auto3dseg/utils.py. The function reads a .pkl file and passes its contents to pickle.loads without validating the data source or content. If
- 7.8 High severity
MONAI: Deserialization of Untrusted Data
MONAI before 1.6.0 contains an unsafe deserialization vulnerability in the NumpyReader class that unconditionally uses numpy.load with allow_pickle=True when loading .npy and .npz files. Attackers can craft malicious .npy files with pickle payloads that execut
- 7.8 High severity
MONAI: Improper Neutralization of Special Elements used in an OS Command
MONAI before 1.6.0 is vulnerable to OS command injection in the nnUNetV2Runner component (monai.apps.nnunet.nnunetv2_runner). User-controlled values taken from the YAML configuration file (notably dataset_name_or_id) and from CLI/kwargs arguments are concatena
- 7.8 High severity
MONAI: Deserialization of Untrusted Data
MONAI versions before 1.6.0 contain a remote code execution vulnerability in the algo_from_pickle() function due to unsafe pickle.loads() deserialization in monai/auto3dseg/utils.py. Attackers can craft malicious pickle files that execute arbitrary system comm
- 8.8 High severity
MONAI: Improper Neutralization of Directives in Dynamically Evaluated Code
MONAI through 1.6.0 contains an eval injection vulnerability in _get_fake_spatial_shape() in monai/bundle/scripts.py. The function validates shape expressions with a helper that walks the AST and only collects ast.Name nodes, rejecting any name other than 'p'
- 7.8 High severity
MONAI: Deserialization of Untrusted Data
In MONAI 1.6.0, PersistentDataset (monai/data/dataset.py) explicitly rejects the combination track_meta=True with weights_only=True, forcing users who cache MetaTensors (the default tensor type in MONAI >= 1.0) to run torch.load(hashfile, weights_only=False).
- 7.8 High severity
MONAI: Improper Neutralization of Directives in Dynamically Evaluated Code
MONAI through 1.6.0 contains a remote code execution vulnerability in the bundle configuration engine that resolves _target_ values to arbitrary importable callables without an allow list and passes $ expressions to Python eval(). Attackers can publish a malic
- 8.8 High severity
MONAI: Deserialization of Untrusted Data
MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging. In versions up to and including 1.5.0, the `pickle_operations` function in `monai/data/utils.py` automatically handles dictionary key-value pairs ending with a specific suffix and de
- 8.8 High severity
MONAI: Deserialization of Untrusted Data
MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging. In versions up to and including 1.5.0, in `model_dict = torch.load(full_path, map_location=torch.device(device), weights_only=True)` in monai/bundle/scripts.py , `weights_only=True`
- 8.8 High severity
MONAI: Improper Limitation of a Pathname to a Restricted Directory
MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging. The extractall function `zip_file.extractall(output_dir)` is used directly to process compressed files. It is used in many places in the project. In versions up to and including 1.5.