CVE-2026-48705

MONAI: MONAILabel Server Crash and Silent Inference Corruption via Crafted DICOM (pydicom DS/IS Validation Bypass)

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 affine matrix without validation. An unauthenticated attacker who can upload a crafted DICOM to a default MONAILabel deployment can propagate NaN or infinity values through LoadImaged, Spacingd, model inference, and output writing, causing inference failures, persistent datastore poisoning, or geometrically corrupted results. This issue is fixed in version 1.6.1.

CVSS 3.1 · 9.3 Critical EPSS < 1% KEV · Not listed

Vendors

Project-monai

Products

Monai

CVE-2026-118684

MONAI before 1.6.1 Missing Integrity Verification via check_hash in Downloads

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.

CVSS 4.0 · 8.2 High EPSS < 1% KEV · Not listed

Vendors

Project-monai

Products

Monai

CVE-2026-119578

MONAI 1.6.0 before 1.6.1 Code Execution via Auto3DSeg algo_from_json()

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 a _target_ name that is imported and instantiated when victims call AutoRunner or import_bundle_algo_history, executing code under default configuration.

CVSS 4.0 · 8.5 High EPSS < 1% KEV · Not listed

Vendors

Project-monai

Products

Monai

CVE-2026-118683

MONAI 1.2.0 before 1.6.1 Code Execution via bundle configs/logging.conf

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.

CVSS 4.0 · 8.6 High EPSS < 1% KEV · Not listed

Vendors

Project-monai

Products

Monai

CVE-2026-109361

MONAI 1.2.0 before 1.6.1 Insecure Deserialization RCE via nnUNetV2Runner postprocessing_file Path

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.

CVSS 4.0 · 7 High EPSS < 1% KEV · Not listed

Vendors

Project-monai

Products

Monai

CVE-2026-109360

MONAI 0.4.0 before 1.6.1 RCE via SENet Pretrained Weights Over Plaintext HTTP

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.

CVSS 4.0 · 7.7 High EPSS < 1% KEV · Not listed

Vendors

Project-monai

Products

Monai

CVE-2026-100846

MONAI before 1.5.2 Remote Code Execution via Pickle Deserialization

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 4.0 · 8.8 High EPSS < 1% KEV · Not listed

Vendors

Project-monai

Products

Monai

CVE-2026-100841

MONAI through 1.6.0 PersistentDataset Remote Code Execution via Pickle Cache

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.

CVSS 4.0 · 8.5 High EPSS < 1% KEV · Not listed

Vendors

Project-monai

Products

Monai

CVE-2026-100842

MONAI through 1.6.0 _get_fake_spatial_shape eval() Sandbox Bypass via Attribute Chains

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 4.0 · 7.3 High EPSS < 1% KEV · Not listed

Vendors

Project-monai

Products

Monai

CVE-2026-100843

MONAI before 1.6.0 Remote Code Execution via algo_from_pickle

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 4.0 · 8.5 High EPSS < 1% KEV · Not listed

Vendors

Project-monai

Products

Monai

CVE-2026-100844

MONAI before 1.6.0 OS Command Injection via dataset_name_or_id

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 4.0 · 8.6 High EPSS < 1% KEV · Not listed

Vendors

Project-monai

Products

Monai

CVE-2026-100840

MONAI through 1.6.0 Remote Code Execution via bundle configuration

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 4.0 · 8.5 High EPSS < 1% KEV · Not listed

Vendors

Project-monai

Products

Monai

CVE-2026-100845

MONAI before 1.6.0 Remote Code Execution via NumpyReader

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 4.0 · 8.5 High EPSS < 1% KEV · Not listed

Vendors

Project-monai

Products

Monai

CVE-2026-21851

MONAI has Path Traversal (Zip Slip) in NGC Private Bundle Download

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 3.1 · 5.3 Medium EPSS < 1% KEV · Not listed

Vendors

Monai Project-monai

Products

Monai Monai