CVE Database›Document archive›CVE-2026-112399

CVE-2026-112399

pillow: pillow: Denial of service via crafted APNG image

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Severity

Important

Red Hat Product Security

7.5

CVSS 3.1

Review impact separately from score.

Published · October 20, 2026

Updated · November 4, 2026

Description

A flaw was found in Pillow. A remote attacker can trigger a Denial of Service (DoS) by providing a specially crafted Animated Portable Network Graphics (APNG) image. Pillow allocates memory for the full canvas dimensions before validating safety size limits during initial file opening. Processing such an image can exhaust available system memory and cause the application to crash.

Statement

This vulnerability is rated as an Important severity for Red Hat products because an unauthenticated remote attacker can cause a denial of service through memory exhaustion without requiring user interaction or elevated privileges. Red Hat solutions such as OpenShift AI and Ansible Automation Platform integrate Pillow to process arbitrary user or automated media uploads. Because full-canvas memory allocation occurs during initial file header ingestion prior to decompression safety limits being enforced, applications cannot rely on standard dimension safeguards to prevent worker termination.

CVSS score applicability

The CVSS score(s) listed for this vulnerability do not reflect the associated product's status, and are included for informational purposes to better understand the severity of this vulnerability.

Mitigation

--- CME Mitigation Guidance (2026-10-20) --- Disclaimer: The following mitigation solutions are provided as a courtesy, pending the application of an official patch. This content was generated utilizing AI tools in conjunction with data from https://cmetaxonomy.org. Customers are advised to independently evaluate and test these temporary mitigations within their own infrastructure.

The highest-value action is to enforce decompression bomb validation checks prior to full-canvas memory allocation during initial image parsing in Pillow: upgrade to Pillow 13.0.0 (or the fixed Python Pillow/Python3.11 Pillow/Rhoai/Odh Openvino Model Server Rhel/Rhelai3/Bootc Cuda Rhel/Rhelai3/Bootc Gaudi Rhel/Rhelai3/Bootc Rocm Rhel/Rhelai3/Disk Image Cuda Rhel/Python3X Pillow/Rhaiis/Vllm Spyre Rhel/Rhoai/Odh Kserve Storage Initializer Rhel/Rhoai/Odh Kserve Agent Rhel/Rhoai/Odh Kserve Controller Rhel/Rhoai/Odh Kserve Router Rhel/Rhoai/Odh Feature Server Rhel/Rhoai/Odh Trustyai Nemo Guardrails Server Rhel/Rhaiis/Vllm Tpu Rhel/Rhoai/Odh Training Cuda128 Torch29 Py312 Rhel/Rhoai/Odh Training Rocm64 Torch29 Py312 Rhel/Rhaiis/Vllm Cpu Rhel/Rhaiis/Vllm Neuron Rhel/Ansible Automation Platform 25/Lightspeed Chatbot Rhel/Openshift Lightspeed/Lightspeed Ocp Rag Rhel/Rhoai/Odh Th06 Cpu Torch291 Py312 Rhel/Rhoai/Odh Th06 Cuda130 Torch291 Py312 Rhel/Rhoai/Odh Vllm Gaudi Rhel/Python3.12 Pillow/Rhaii/Vllm Gaudi Rhel/Rhaii/Vllm Neuron Rhel/Rhaii/Vllm Tpu Rhel/Lightspeed Core/Lightspeed Stack Rhel/Rhoai/Odh Kserve Autogluon Server Rhel/Rhai Early Access/Docling Sdk Cuda Rhel/Rhai Early Access/Docling Serve Cuda Rhel/Rhoai/Odh Llm D Kv Cache Rhel/Rhoai/Odh Th06 Cpu Torch210 Py312 Rhel/Rhoai/Odh Th06 Cuda130 Torch210 Py312 Rhel/Rhoai/Odh Th06 Rocm64 Torch291 Py312 Rhel/Rhoai/Odh Workbench Jupyter Pytorch Llmcompressor Cuda Py312 Rhel/Ask Sre/Rhel Cla/Rlsapi Rhel/Rhaiis/Model Opt Cuda Rhel/Rhaiis/Vllm Cuda Rhel/Rhaiis/Vllm Rocm Rhel/Rhaii/Model Opt Cuda Rhel/Rhaii/Vllm Cpu Rhel/Rhaii/Vllm Cuda Rhel/Rhaii/Vllm Rocm Rhel/Rhaii/Vllm Spyre Rhel/Ansible Automation Platform/Automation Dashboard Rhel/Ansible Automation Platform 26/Hub Rhel/Ansible Automation Platform 26/Lightspeed Chatbot Rhel/Ansible Automation Platform 27/Hub Rhel/Ansible Automation Platform 27/Lightspeed Chatbot Rhel/Galaxy Ng/Automation Hub Container/Aap Rag Content/Ansible Chatbot Stack/Koku Report Emailer/Exploit Intelligence/Vulnerability Analysis Rhel/Insights Host Inventory/Google Lightspeed Agent/Lscore Deploy/Lightspeed Core/Rag Tool Cpu Rhel/Lightspeed Core/Rag Tool Cuda 12.9 Rhel/Lightspeed Services Ocp/Ocp Rules Rhel/Openshift Lightspeed Tech Preview/Lightspeed Rag Tool Rhel/Openshift Lightspeed/Lightspeed Service Api Rhel/Rhoai/Odh Caikit Tgis Serving Rhel/Rhoai/Odh Llama Stack Core Rhel/Rhoai/Odh Pipeline Runtime Pytorch Llmcompressor Cuda Py312 Rhel/Rhoai/Odh Automl Rhel/Rhoai/Odh Autorag Rhel/Rhoai/Odh Mlflow Rhel/Rhoai/Odh Pipeline Runtime Datascience Cpu Py312 Rhel/Rhoai/Odh Pipeline Runtime Pytorch Cuda Py312 Rhel/Rhoai/Odh Pipeline Runtime Pytorch Rocm Py312 Rhel/Rhoai/Odh Pipeline Runtime Tensorflow Cuda Py312 Rhel/Rhoai/Odh Pipeline Runtime Tensorflow Rocm Py312 Rhel/Rhoai/Odh Trustyai Garak Lls Provider Dsp Rhel/Rhoai/Odh Workbench Codeserver Datascience Cpu Py312 Rhel/Rhoai/Odh Workbench Jupyter Datascience Cpu Py312 Rhel/Rhoai/Odh Workbench Jupyter Pytorch Cuda Py312 Rhel/Rhoai/Odh Workbench Jupyter Pytorch Rocm Py312 Rhel/Rhoai/Odh Workbench Jupyter Tensorflow Cuda Py312 Rhel/Rhoai/Odh Workbench Jupyter Tensorflow Rocm Py312 Rhel/Rhoai/Odh Workbench Jupyter Trustyai Cpu Py312 Rhel/Rhai/Guidellm Rhel/Rhoai/Odh Ogx Core Rhel/Rhoai/Odh Th Torch Cpu Py312 Rhel/Rhoai/Odh Th Torch Cuda Py312 Rhel/Rhoai/Odh Th Torch Rocm Py312 Rhel/Quay/Quay Service Tool/Quay/Quay Rhel/Rhelai3/Bootc Aws Cuda Rhel/Rhelai3/Bootc Azure Cuda Rhel/Rhelai3/Bootc Azure Rocm Rhel/Rhelai3/Bootc Gcp Cuda Rhel builds), which call Image._decompression_bomb_check(self.size) immediately after opening each frame to raise DecompressionBombError before allocating frame buffers, closing all 2 injection vectors (crafted APNG image with OP_BACKGROUND disposal, crafted APNG image with first-frame OP_PREVIOUS disposal). Until patched, this is a concern on Applications on any platform opening untrusted image files with Pillow Image.open, so Restrict ingestion of untrusted APNG files and validate image header metadata before passing input to Image.open. As a compensating measure, Monitor memory pressure events and PSI metrics to detect abnormal allocation spikes from oversized canvas sizing, Enforce process address space limits using ulimit or prlimit RLIMIT_AS to restrict maximum memory consumption during parsing, Configure systemd service watchdogs and automatic restarts to restore availability if process termination occurs, and Apply cgroups v2 memory constraints to isolate container or service memory exhaustion and protect host availability. These controls limit blast radius and contain exploitation, but they do not fix the underlying uncontrolled memory allocation defect — apply the upstream patch to fully remediate.

Highest-impact CME: CME-1010 cgroup Memory Pressure Monitoring (memory.events / PSI) (medium)

CVSS / CWE

CVSS 3.1 · 7.5
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

CWE-770 · Allocation of Resources Without Limits or Throttling