CVE-2025-58756 | MONAI's unsafe torch usage may lead to arbitrary code execution

Exp

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.

Published: 2025-09-09 Last update: 2025-09-19 Assigner: [email protected] Source: [email protected]
NVD Status：Analyzed，CVE State: published
View at NVD, CVE.org, EUVD

CVE-2025-58756 is rated High Exploit Risk (80.4/100): CVSS High severity, with medium exploitation likelihood (EPSS 2.10%). 1 public exploit reference(s) are indexed (Exploit-DB). Public exploits are available—assess exposure, apply mitigations, and prioritize patching.

Risk is dynamic; we continuously reassess and refresh what is shown on this page as upstream context changes.

Public exploit references (Exploit-DB) for CVE-2025-58756

EDB-ID Source Kind Published Link
— nvd_ref exploit_tag Exploit-DB ↗

Exploit prediction scoring system (EPSS) score for CVE-2025-58756

EPSS lead: Daily EPSS estimates relative likelihood of exploitation; percentile ranks this CVE among scored vulnerabilities (higher = more severe relative rank).

# Date Old EPSS score New EPSS score Delta (New - Old)
1 2026-05-25 1.38% 2.10% +0.72%
2 2026-05-14 1.66% 1.38% -0.28%
3 2026-04-14 — 1.66% —

Full EPSS history (12 records total)

Common vulnerability scoring system (CVSS) metrics for CVE-2025-58756

CVSS metrics for this CVE.

Base score Version Severity Vector Exploitability Impact Score source
8.8 3.1 HIGH
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H Click to expand
Attack vector (AV:N)
Could be attacked over the internet or any normal routed network—not just someone sitting at the machine.
Attack complexity (AC:L)
Once they can reach the bug, pulling it off is straightforward—no weird race conditions or rare setup.
Privileges required (PR:L)
A normal user session is enough; they don’t have to be admin.
User interaction (UI:N)
Nobody has to click “OK” or open a trap file; it can work without a victim helping.
Scope (S:U)
Damage stays in the same “trust bubble” as the broken component—no big spill into unrelated systems.
Confidentiality (C:H)
Serious risk that confidential data gets exposed in a big way.
Integrity (I:H)
They could widely tamper with or forge data—trust in the data is badly hurt.
Availability (A:H)
Could take the service down hard or make it unusable for people who depend on it.
 2.8 5.9 [email protected]

Weakness enumeration for CVE-2025-58756

GitHub Security Advisory for CVE-2025-58756

GHSA-6vm5-6jv9-rjpj · Severity: high · Ecosystem: pip — MONAI: Unsafe torch usage may lead to arbitrary code execution

Affected software / configurations for CVE-2025-58756

Vendor Product Version Raw CPE
monai medical_open_network_for_ai <= 1.5.0 cpe:2.3:a:monai:medical_open_network_for_ai:*:*:*:*:*:*:*:*

References for CVE-2025-58756

cvelogic Threat Intelligence