Project-Monai: >> Monai >> 1.0.0 Security Vulnerabilities
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.
CVSS Score
8.5
EPSS Score
0.001
Published
2026-09-27
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 in the context of the application.
CVSS Score
8.8
EPSS Score
0.003
Published
2026-09-27
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 malicious bundle with crafted configuration containing arbitrary code that executes when a victim loads the bundle using monai.bundle.load() or monai.bundle.run().
CVSS Score
8.5
EPSS Score
0.002
Published
2026-09-27
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 (for example "(1).__class__.__bases__[0].__subclasses__()" or "int.__class__.__init__.__globals__") contain no ast.Name nodes and therefore bypass the allowlist. Because the shape value originates from bundle metadata consumed by _get_real_input_data and verify_net_in_out (reachable through the bundle 'verify_net_in_out' CLI flow), an attacker who can influence a bundle's metadata can escape the eval sandbox via object introspection chains and achieve code execution in this non-default flow.
CVSS Score
7.3
EPSS Score
0.001
Published
2026-09-27
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.
CVSS Score
8.5
EPSS Score
0.002
Published
2026-09-27
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 concatenated into a command string without quoting or validation and then passed to subprocess with shell=True, so shell metacharacters (e.g., ';' on Linux, '&' on Windows) are interpreted. If a victim loads and processes a crafted configuration file — for example by instantiating nnUNetV2Runner with the malicious YAML and invoking a training/validation job such as train_single_model() — arbitrary commands are executed with the privileges of the user running the job.
CVSS Score
8.6
EPSS Score
0.006
Published
2026-09-27
MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging. In versions up to and including 1.5.1, a Path Traversal (Zip Slip) vulnerability exists in MONAI's `_download_from_ngc_private()` function. The function uses `zipfile.ZipFile.extractall()` without path validation, while other similar download functions in the same codebase properly use the existing `safe_extract_member()` function. Commit 4014c8475626f20f158921ae0cf98ed259ae4d59 fixes this issue.
CVSS Score
5.3
EPSS Score
0.004
Published
2026-01-07