CVE Vulnerabilities

CVE-2026-49851

Uncontrolled Resource Consumption

Published: Jun 24, 2026 | Modified: Aug 28, 2026
CVSS 3.x
N/A
Source:
NVD
CVSS 2.x
RedHat/V2
RedHat/V3
7.5 IMPORTANT
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Ubuntu
MEDIUM
root.io logo minimus.io logo echo.ai logo

Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.3.0, Mistune is vulnerable to a CPU exhaustion DoS due to superlinear (approximately O(n²)) behavior in parse_link_text. When parsing Markdown containing many consecutive [ characters, parse_link_text repeatedly scans the input using a regex search inside a loop. Each iteration re-scans a large portion of the remaining string, resulting in quadratic-time behavior. An attacker-controlled Markdown input can therefore trigger excessive CPU usage with a very small payload. This vulnerability is fixed in 3.3.0.

Weakness

The product does not properly control the allocation and maintenance of a limited resource.

Affected Software

NameVendorStart VersionEnd Version
Red Hat OpenShift AI 3.4RedHatrhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9:1787073866*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-pipeline-runtime-minimal-cpu-py312-rhel9:1787073936*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9:1787073873*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-pipeline-runtime-pytorch-llmcompressor-cuda-py312-rhel9:1787073459*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-pipeline-runtime-pytorch-rocm-py312-rhel9:1787073611*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-pipeline-runtime-tensorflow-cuda-py312-rhel9:1787073451*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-pipeline-runtime-tensorflow-rocm-py312-rhel9:1787073451*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-th06-cpu-torch210-py312-rhel9:1787076778*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-th06-cuda130-torch210-py312-rhel9:1787077779*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-th06-rocm64-torch291-py312-rhel9:1787076481*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-datascience-cpu-py312-rhel9:1787074331*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-minimal-cpu-py312-rhel9:1787073913*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-minimal-cuda-py312-rhel9:1787074078*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-minimal-rocm-py312-rhel9:1787073929*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-pytorch-cuda-py312-rhel9:1787073605*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-pytorch-rocm-py312-rhel9:1787073546*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-tensorflow-cuda-py312-rhel9:1787073717*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-tensorflow-rocm-py312-rhel9:1787073713*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-trustyai-cpu-py312-rhel9:1787073593*
MistuneUbuntuquesting*

Potential Mitigations

  • Mitigation of resource exhaustion attacks requires that the target system either:

  • The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question.

  • The second solution is simply difficult to effectively institute – and even when properly done, it does not provide a full solution. It simply makes the attack require more resources on the part of the attacker.

References