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Fencer Vulns
high 7.8

CVE-2026-15679 json

Hugging Face PyTorch Image Models checkpoint Deserialization of Untrusted Data Remote Code Execution Vulnerability

published 2026-08-20 2 mo ago modified 2026-10-18 21 d ago also EUVD-2026-63459

cvss
7.8
version 3.0, CNA
cisa kev
Not listed
not in CISA's catalog
epss
0.4%
EPSS 0.4% chance of exploitation in 30 days, percentile 39%
public exploits
0
none known

Summary

Hugging Face PyTorch Image Models checkpoint Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face PyTorch Image Models. 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 checkpoints. 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-27987.

Vendor advisories

What the vendors themselves say about their products: affected, fixed or not affected, and what to do.

red hat product security vex CVE-2026-15679 updated cvss 8.8

timm: Hugging Face PyTorch Image Models: Remote Code Execution via Deserialization of Untrusted Data

  • Red Hat AI Inference Server rhaii/vllm-tpu-rhel9, rhaiis/vllm-cpu-rhel9, rhaiis/vllm-tpu-rhel9, rhaiis/vllm-cuda-rhel9, rhaiis/vllm-rocm-rhel9, rhaii/vllm-neuron-rhel9, +2 more
    affected: workaround, no fix available, no fix planned “Will not fix”
  • Red Hat Enterprise Linux AI (RHEL AI) 3 rhelai3/bootc-gaudi-rhel9
    affected: workaround, no fix planned “Will not fix”
  • Red Hat AI Inference Server 3.4 rhaii/vllm-cpu-rhel9 1791857301, rhaii/vllm-cuda-rhel9 1791857183, rhaii/vllm-rocm-rhel9 1791856725, rhaii/vllm-spyre-rhel9 1791858592
    fixed: vendor fix RHSA-2026:83112
  • Red Hat Enterprise Linux AI 3.4 rhelai3/bootc-cuda-rhel9 1792612985, rhelai3/bootc-rocm-rhel9 1792612921, rhelai3/bootc-aws-cuda-rhel9 1792680701, rhelai3/bootc-gcp-cuda-rhel9 1792680694, rhelai3/disk-image-cuda-rhel9 1792701796, rhelai3/bootc-azure-cuda-rhel9 1792680694, +1 more
    fixed: vendor fix RHSA-2026:90440
  • Red Hat OpenShift AI 2.25 rhoai/odh-feature-server-rhel9 1792148523
    fixed: vendor fix RHSA-2026:87410
  • Red Hat OpenShift AI 3.4 rhoai/odh-feature-server-rhel9 1791863220, rhoai/odh-openvino-model-server-rhel9 1790958108
    fixed: vendor fix RHSA-2026:84734
  • Red Hat OpenShift AI 3.5 rhoai/odh-th-torch-cpu-py312-rhel9 1791926611, rhoai/odh-th-torch-cuda-py312-rhel9 1791927642, rhoai/odh-th-torch-rocm-py312-rhel9 1791925861
    fixed: vendor fix RHSA-2026:86891
  • Red Hat OpenShift AI 2.25 rhoai/odh-rhel9-operator 1792570619, rhoai/odh-vllm-cpu-rhel9 1792018436, rhoai/odh-dashboard-rhel9 1791853525, rhoai/odh-modelmesh-rhel9 1790881700, rhoai/odh-operator-bundle 1792571069, rhoai/odh-vllm-cuda-rhel9 1791358976, +65 more Red Hat OpenShift AI 3.4 rhoai/odh-cli-rhel9 1791509689, rhoai/rhai-cli-rhel9 1791509689, rhoai/odh-automl-rhel9 1791535789, rhoai/odh-mlflow-rhel9 1791865967, rhoai/odh-autorag-rhel9 1791557539, rhoai/odh-trainer-rhel9 1791869783, +98 more Red Hat OpenShift AI 3.5 rhoai/odh-cli-rhel9 1791945905, rhoai/rhai-cli-rhel9 1791945905, rhoai/odh-automl-rhel9 1791916705, rhoai/odh-mlflow-rhel9 1791947857, rhoai/odh-autorag-rhel9 1791903266, rhoai/odh-trainer-rhel9 1791952138, +110 more
    not affected: vulnerable code not present

Red Hat CSAF VEX © Red Hat, Inc., from https://security.access.redhat.com/data/csaf/v2/vex/; changed: reduced to product statuses and remediations. CC BY 4.0 · csaf

CISA SSVC

Exploitation
none
Automatable
no
Technical impact
total
Assessed
2 mo ago

Exploit prediction

0.45% chance of exploitation in the next 30 days, higher than 39% of all scored CVEs (FIRST EPSS, 2026-11-08).

CVSS scores

assigned by version score vector
CNA 3.0 7.8 CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Affected products

  • Hugging Face PyTorch Image Models

Classification

Weakness
  • CWE-502 Deserialization of Untrusted Data

References

Sources

Fencer Vulns keeps one entry per vulnerability: the CVE id wins, then GHSA, and records that alias each other are merged, except that two CVEs always keep separate entries. The same data is available as JSON.