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VulnerabilityPublished

CVE-2025-58757: 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, 14:05 UTC

CVSS 3.1 base score
8.8High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.6% chance of exploitation in 30 days – higher than 50% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/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 26 Oct 2026.

What to do

Affected software

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

AI-suggested

Since no official patches are currently available, European organizations should implement immediate compensating controls. First, restrict MONAI's exposure to untrusted or unauthenticated data sources by enforcing strict input validation and sanitization before any deserialization occurs. Implement network segmentation and firewall rules to limit access to MONAI instances only to trusted internal systems and users. Employ application-level whitelisting to ensure only authorized data formats and sources are processed. Consider running MONAI in isolated, sandboxed environments or containers with minimal privileges to contain potential exploitation impact. Monitor logs and network traffic for unusual deserialization attempts or unexpected execution patterns. Educate staff about the risks of processing untrusted data and enforce strict operational procedures for handling AI model inputs. Finally, maintain close communication with the MONAI project for updates on patches and apply them promptly once available.

Description

MONAI (Medical Open Network for AI) is an AI toolkit for health care imaging. In versions up to and including 1.5.0, the `pickle_operations` function in `monai/data/utils.py` automatically handles dictionary key-value pairs ending with a specific suffix and deserializes them using `pickle.loads()` . This function also lacks any security measures. The deserialization may lead to code execution. As of time of publication, no known fixed versions are available.

References

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