CVE Vulnerabilities

CVE-2026-49476

Uncontrolled Resource Consumption

Published: Jul 14, 2026 | Modified: Jul 28, 2026
CVSS 3.x
N/A
Source:
NVD
CVSS 2.x
RedHat/V2
RedHat/V3
5.9 IMPORTANT
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
Ubuntu
MEDIUM
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Soup Sieve is a CSS selector library designed to be used with Beautiful Soup 4. Prior to 2.8.4, the CSS selector parser in soupsieve allocates unbounded memory when compiling large comma-separated selector lists, allowing an attacker who can supply a crafted selector string to soupsieve.compile() or Beautiful Soup .select() / .select_one() to allocate hundreds of megabytes of heap memory from a relatively small input and cause denial of service. This issue is fixed in version 2.8.4.

Weakness

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

Affected Software

NameVendorStart VersionEnd Version
Soup_sieveFacelessuser*2.8.4 (excluding)
Red Hat Hardened ImagesRedHatpython-rpds-py-main-2026.6.3-1.hum1*
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*

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