CVE-2026-48705

MONAI from 1.6.0 before 1.6.1 contains a remote code execution vulnerability in Auto3DSeg algo_from_json(). Attackers can run Python code via a crafted algo_object.json in a work directory.

MONAI before 1.6.1 contains an insufficient integrity verification vulnerability in check_hash() that silently returns True when hash_val is None, with MD5 as the default hash type. Attackers performing man-in-the-middle or registry compromise can substitute bundles, datasets, or model archives downloaded without hashes, enabling malicious file delivery and potential code execution.

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 bundle whose logging.conf runs arbitrary Python at workflow construction when a victim loads or runs the bundle.

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_information.json can point it at a malicious pickle elsewhere on the filesystem to execute code when predict_ensemble_postprocessing runs.

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_url unpickles without weights_only=True, executing code when SEResNet50 or other SENet models are constructed with pretrained=True.

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 an application invokes algo_from_pickle on an attacker-supplied pickle file, an object defining __reduce__ is executed during deserialization, resulting in arbitrary code execution.

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 execute arbitrary code when loaded through MONAI's standard data pipeline.

MONAI before 1.6.0 is vulnerable to OS command injection in the nnUNetV2Runner component. User-controlled values from the YAML configuration file and CLI/kwargs arguments are concatenated into a command string without quoting or validation and then passed to subprocess with shell=True. If a victim loads a crafted configuration file, arbitrary commands are executed with the privileges of the user running the job.

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 commands when deserialized by the vulnerable function.

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' or 'n', before passing the string to eval(). Expressions built solely from constants and attribute, subscript, or call nodes contain no ast.Name nodes and bypass the allowlist. An attacker who can influence a bundle's metadata can escape the eval sandbox and achieve code execution.