What is the CVSS score of CVE-2025-58756?

CVE-2025-58756 has a CVSS 3.1 base score of 8.8 (High). CVSS vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H. EPSS exploitation probability: 1.2%.

Which versions of Monai are affected by CVE-2025-58756?

Organizations using versions face significant risk from CVE-2025-58756, a insecure deserialization vulnerability rated CVSS 8.8.. A patch is available.

Is there a public PoC exploit available for CVE-2025-58756?

Yes, a public proof-of-concept exploit is available for CVE-2025-58756. Prioritise patching immediately. EPSS: 1.2%.

How to fix or mitigate CVE-2025-58756?

Within 7 days: Identify all affected systems running versions and apply vendor patches promptly. Restrict deserialization to trusted data sources and implement integrity checks.

Monai CVE-2025-58756

HIGH

Deserialization of Untrusted Data (CWE-502)

2025-09-09 security-advisories@github.com

GHSA-6vm5-6jv9-rjpj

RCE Deserialization Monai Medical Open Network For Ai

8.8

CVSS 3.1 · GitHub Advisory

Severity by source

GitHub Advisory PRIMARY

8.8 HIGH

AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Primary rating from GitHub Advisory · only source for this CVE.

CVSS VectorGitHub Advisory

Attack Vector

Network

Attack Complexity

Low

Privileges Required

Low

User Interaction

None

Scope

Unchanged

Confidentiality

High

Integrity

High

Availability

High

Lifecycle Timeline

Patch released

Mar 31, 2026 - 21:13 nvd

Patch available

Analysis Generated

Mar 28, 2026 - 19:11 vuln.today

PoC Detected

Sep 19, 2025 - 15:26 vuln.today

Public exploit code

CVE Published

Sep 09, 2025 - 00:15 nvd

HIGH 8.8

Blast Radius

ecosystem impact

† from your stack dependencies † transitive graph · vuln.today resolves 4-path depth

2 pypi packages depend on monai (1 direct, 1 indirect)

Ecosystem-wide dependent count for version 1.5.1.

DescriptionGitHub Advisory

MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging. In versions up to and including 1.5.0, in model_dict = torch.load(full_path, map_location=torch.device(device), weights_only=True) in monai/bundle/scripts.py , weights_only=True is loaded securely. However, insecure loading methods still exist elsewhere in the project, such as when loading checkpoints. This is a common practice when users want to reduce training time and costs by loading pre-trained models downloaded from other platforms. Loading a checkpoint containing malicious content can trigger a deserialization vulnerability, leading to code execution. As of time of publication, no known fixed versions are available.

AnalysisAI

MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging. Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available and no vendor patch available.

Technical ContextAI

This vulnerability is classified as Deserialization of Untrusted Data (CWE-502), which allows attackers to execute arbitrary code through malicious serialized objects. MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging. In versions up to and including 1.5.0, in model_dict = torch.load(full_path, map_location=torch.device(device), weights_only=True) in monai/bundle/scripts.py , weights_only=True is loaded securely. However, insecure loading methods still exist elsewhere in the project, such as when loading checkpoints. This is a common practice when users want to reduce training time and costs by loading pre-trained models downloaded from other platforms. Loading a checkpoint containing malicious content can trigger a deserialization vulnerability, leading to code execution. As of time of publication, no known fixed versions are available. Affected products include: Monai Medical Open Network For Ai.

RemediationAI

No vendor patch is available at time of analysis. Monitor vendor advisories for updates. Avoid deserializing untrusted data. Use safe serialization formats (JSON). Implement integrity checks and type allowlists.