CVE-2025-58757: MONAI · Deserialization of Untrusted Data
Affects MONAISource CVE Database V5
Not known to be exploited · CVSS 8.8 High.
Data as of 26 Oct 2026, 06:26 UTC
- 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:HExploitation evidence
Disclosed: evidence found.
CVE published 8 Sep 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by 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
| Product | Affected | Fixed in |
|---|---|---|
| Project-MONAI MONAI | Affected
| 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.