CVE-2025-14930: Hugging Face Transformers · Deserialization of Untrusted Data
Affects Hugging Face TransformersSource CVE Database V5
Not known to be exploited · CVSS 7.8 High.
Data as of 26 Oct 2026, 14:05 UTC
- EPSS
- 0.3% chance of exploitation in 30 days – higher than 25% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HExploitation evidence
Disclosed: evidence found.
CVE published 23 Dec 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 26 Oct 2026.
What to do
Affected software
| Product | Affected | Fixed in |
|---|---|---|
PyPItransformerspkg:pypi/transformersOther threats | Affected
| Fixed in– |
Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.
AI-suggested
Organizations should immediately audit their use of Hugging Face Transformers, specifically version 4.57.1, and plan to upgrade to a patched version once available. Until a patch is released, restrict the loading of model weights from untrusted or unauthenticated sources. Implement strict input validation and sanitization for any data used in model weight parsing. Employ sandboxing or containerization to isolate AI workloads, limiting the potential impact of code execution. Enhance user awareness training to reduce the risk of social engineering attacks that could trigger exploitation. Monitor network and system logs for unusual activity related to AI model loading or deserialization processes. Consider deploying application-layer controls to detect and block malicious serialized payloads. Finally, maintain up-to-date backups and incident response plans tailored to AI infrastructure compromise scenarios.
Description
Hugging Face Transformers GLM4 Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face Transformers. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the parsing of weights. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-28309.