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CVE-2025-58756VulnerabilityPublished

CVE-2025-58756: MONAI · Deserialization of Untrusted Data

Affects MONAISource CVE Database V5

0

Not known to be exploited · CVSS 8.8 High.

Data as of 26 Oct 2026, 06:26 UTC

CVSS 3.1 base score
8.8High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.7% chance of exploitation in 30 days – higher than 54% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 8 Sep 2025

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-26.

What to do

Affected software

ProductAffectedFixed in
Project-MONAI MONAIAffected
  • <=1.5.0
Fixed in–

AI-suggested

1. Avoid loading MONAI model checkpoints from untrusted or unauthenticated sources. Only use checkpoints obtained from verified, trusted repositories or internal sources. 2. Implement strict validation and integrity checks (e.g., digital signatures or cryptographic hashes) on all model checkpoint files before loading them. 3. Where possible, use the secure loading method with `weights_only=True` as demonstrated in monai/bundle/scripts.py to limit deserialization scope. 4. Run MONAI workloads in isolated, sandboxed environments or containers with minimal privileges to contain potential exploitation impact. 5. Monitor and audit usage of model loading functions to detect anomalous or unauthorized checkpoint loading activities. 6. Engage with the MONAI project community and maintain awareness of updates or patches addressing this vulnerability. 7. Consider network-level protections such as restricting access to model repositories and employing intrusion detection systems tuned for suspicious deserialization activities. 8. Educate developers and data scientists about the risks of deserializing untrusted data and enforce secure coding practices around model loading.

Description

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.

References

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