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Fencer Vulns
critical 9.8

CVE-2026-96639 json

Pillow multi-frame loaders can reuse stale native image storage after mode transitions

published 2026-10-21 18 d ago modified 2026-10-29 10 d ago also BIT-pillow-2026-96639 , EUVD-2026-107573 , GHSA-v9pv-5gc8-w2m6 , PYSEC-2026-4339

cvss
9.8
version 3.1, CNA
cisa kev
Not listed
not in CISA's catalog
epss
0.7%
EPSS 0.7% chance of exploitation in 30 days, percentile 51%
public exploits
0
none known

Summary

Pillow is a Python imaging library. From 11.0.0 until 13.0.0, multi-frame loaders can retain an ImagingCore allocation created for a narrow frame mode while a later frame of the same dimensions changes the logical mode and decoder tiles to wider RGB or CMYK data. ImageFile.load_prepare() reuses the stale native storage because _im is non-null without verifying that its mode and size still match. Crafted PSD, MPO, DCX, or MIC frame transitions can then write wider pixels into the narrow allocation, causing native out-of-bounds writes and crashes. The MPO path can also cause a native over-read and, in demonstrated runtime-specific profiles, command execution. This issue is fixed in version 13.0.0.

Patch / fix

  • pypi pillow 13.0.0
  • bitnami pillow 13.0.0
  • python pillow 13.0.0

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-2026-96639 updated

pillow: Pillow multi-frame loaders can reuse stale native image storage after mode transitions

  • Red Hat Exploit Intelligence exploit-intelligence/vulnerability-analysis-rhel9 Red Hat AI Inference Server rhaii/vllm-tpu-rhel9, rhaii/vllm-neuron-rhel9, rhaii-fast/vllm-cpu-rhel9, rhaii-fast/vllm-tpu-rhel9, rhaii-fast/vllm-cuda-rhel9, rhaii-fast/vllm-rocm-rhel9, +5 more Red Hat Enterprise Linux AI (RHEL AI) 3 rhelai3/bootc-cuda-rhel9, rhelai3/bootc-rocm-rhel9, rhelai3/bootc-gaudi-rhel9, rhelai3/bootc-aws-cuda-rhel9, rhelai3/bootc-gcp-cuda-rhel9, rhelai3/bootc-azure-cuda-rhel9, +1 more Red Hat OpenShift AI (RHOAI) rhai/guidellm-rhel9, rhoai/odh-automl-rhel9, rhoai/odh-autorag-rhel9, rhoai/odh-ogx-core-rhel9, rhoai/odh-vllm-cuda-rhel9, rhoai/odh-vllm-rocm-rhel9, +33 more Red Hat Satellite 6 python-pillow
    affected: no fix available
  • Red Hat AI Inference Server 3.5 rhaii/vllm-cpu-rhel9 1793444554, rhaii/vllm-cuda-rhel9 1793444600, rhaii/vllm-rocm-rhel9 1793444456, rhaii/vllm-gaudi-rhel9 1793444510, rhaii/vllm-spyre-rhel9 1793444409, rhaii/model-opt-cuda-rhel9 1793444339
    fixed: vendor fix RHSA-2026:94715
  • Red Hat Ansible Automation Platform 2 python-pillow, python3x-pillow, python3.11-pillow, python3.12-pillow
    not affected: vulnerable code not in execute path
  • Red Hat Enterprise Linux 8 python-pillow, python3-pillow, python3-pillow-tk Red Hat Enterprise Linux AI (RHEL AI) 3 rhelai3/disk-image-cuda-rhel9
    not affected: vulnerable code not present
  • Red Hat OpenShift AI (RHOAI) rhoai/odh-mlflow-rhel9, rhoai/odh-vllm-gaudi-rhel9, rhoai/odh-kserve-agent-rhel9, rhoai/odh-kserve-router-rhel9, rhoai/odh-kserve-controller-rhel9, rhoai/odh-th06-cpu-torch291-py312-rhel9, +1 more
    not affected: component 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
yes
Technical impact
total
Assessed
10 d ago

Exploit prediction

0.66% chance of exploitation in the next 30 days, higher than 51% of all scored CVEs (FIRST EPSS, 2026-11-08).

CVSS scores

assigned by version score vector
CNA 3.1 9.8 CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
GitHub 3.1 9.8 CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
OSV 3.1 9.8 CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Affected packages

ecosystem package affected fixed in
pypi pillow
>=11.0.0, <13.0.0
13.0.0
bitnami pillow
>=11.0.0, <13.0.0
13.0.0

Affected products

  • python-pillow Pillow

Classification

Weakness

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.

CPEs and fixed versions use Anchore's correction of NVD's data: Added CPE configurations because not yet analyzed by NVD.