CVE-2026-100843 in MONAI
Summary
by MITRE • 09/27/2026
MONAI versions before 1.6.0 contain a remote code execution vulnerability in the algo_from_pickle() function due to unsafe pickle.loads() deserialization in monai/auto3dseg/utils.py. Attackers can craft malicious pickle files that execute arbitrary system commands when deserialized by the vulnerable function.
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Analysis
by VulDB Data Team • 09/27/2026
The MONAI library, specifically versions prior to 1.6.0, contains a critical remote code execution vulnerability within its auto3D segmentation utilities. This flaw is located in the algo_from_pickle() function found in the monai/auto3dseg/utils.py module. The root cause of this security issue lies in the use of Python's standard pickle module for deserializing algorithm objects without adequate sanitization or validation mechanisms. In Python, the pickle format allows for arbitrary object instantiation during unpickling, which can be exploited by attackers to execute malicious code on the host system if they control the input data stream.
When an application utilizes algo_from_pickle() to load a serialized model or configuration file, it invokes pickle.loads() directly on the provided byte stream. If this stream originates from an untrusted source, such as a user-uploaded dataset in a medical imaging platform or a shared repository of pre-trained models, an attacker can craft a malicious pickle payload. Upon deserialization, Python executes specific opcodes that instantiate arbitrary classes and call their methods during initialization. This mechanism allows the execution of any system command available to the process running the MONAI application, effectively granting full remote code execution capabilities to the adversary without requiring authentication or prior access privileges beyond the ability to supply the input file.
The operational impact of this vulnerability is severe for organizations deploying automated medical imaging pipelines that rely on third-party models or user-contributed data. An attacker who successfully exploits this flaw can compromise the integrity and confidentiality of the underlying infrastructure, potentially leading to data exfiltration, lateral movement within the network, or complete system takeover. Given that MONAI is often used in high-performance computing environments for medical research, such a breach could disrupt critical diagnostic workflows and expose sensitive patient health information protected under regulations like HIPAA or GDPR. The vulnerability aligns with CWE-502, which describes deserialization of untrusted data, as well as the ATT&CK technique T1610, where attackers exploit binary code execution through injection into legitimate processes via malicious payloads disguised as standard file formats.
To mitigate this risk, organizations must immediately upgrade to MONAI version 1.6.0 or later, which addresses the unsafe deserialization practices in the affected module. In environments where upgrading is not immediately feasible, it is imperative to restrict access to any functions that accept pickle files from untrusted sources and implement strict input validation policies. Additionally, deploying application-level controls such as sandboxing or containerization can limit the blast radius of a potential exploitation attempt by isolating the deserialization process from critical system resources. Security teams should also audit existing pipelines for hardcoded reliance on pickle-based serialization and consider migrating to safer alternatives like JSON or YAML for configuration data where possible, reserving pickle only for trusted internal artifacts with integrity checks in place.