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 versions from 0.4.0 up to but not including 1.6.1 contain a remote code execution vulnerability. This occurs because SENet pretrained weights are fetched over plaintext HTTP, allowing attackers to tamper with the .pth files. The vulnerability arises when load_state_dict_from_url unpickles these files without the weights_only=True parameter, enabling code execution during model construction 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 in the context of the application.
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 (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.
MONAI versions prior to 1.6.0 contain a high-severity remote code execution vulnerability due to unsafe deserialization of untrusted pickle data in the algo_from_pickle() function. This flaw allows attackers to craft malicious pickle files that execute arbitrary system commands when processed 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 (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.
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). Related cache helpers in monai/data/utils.py also call pickle.loads on cached content and derive cache keys with hashlib.md5. As a result, a local user with write access to a shared or world-writable cache_dir (e.g. /tmp/monai_cache, HPC scratch, ~/.cache/monai) can place a malicious pickle file that is deserialized the next time another user's MONAI pipeline reads the cache, resulting in arbitrary code execution in that user's context. All released versions of the monai pip package are affected; no patched version is available as of the advisory.
MONAI versions prior to 1.6.1 contain a remote code execution vulnerability due to improper neutralization of directives in dynamically evaluated code. This flaw exists in the bundle configuration engine, which resolves _target_ values to arbitrary importable callables without an allow list and evaluates $ expressions using Python's eval(). An attacker can craft a malicious bundle that executes arbitrary code when loaded or run via monai.bundle.load() or monai.bundle.run().
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.
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 deserializes them using `pickle.loads()` . This function also lacks any security measures. The deserialization may lead to code execution. As of time of publication, no known fixed versions are available.
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` is loaded securely. However, insecure loading methods still exist elsewhere in the project, such as when loading checkpoints. This is a common practice when users want to reduce training time and costs by loading pre-trained models downloaded from other platforms. Loading a checkpoint containing malicious content can trigger a deserialization vulnerability, leading to code execution. As of time of publication, no known fixed versions are available.
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 1.5.0, when the Zip file containing malicious content is decompressed, it overwrites the system files. In addition, the project allows the download of the zip content through the link, which increases the scope of exploitation of this vulnerability. As of time of publication, no known fixed versions are available.