CVE Vulnerabilities

CVE-2026-96749

Integer Overflow or Wraparound

Published: Sep 24, 2026 | Modified: Sep 26, 2026
CVSS 3.x
N/A
Source:
NVD
CVSS 2.x
RedHat/V2
RedHat/V3
6.2 MODERATE
CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Ubuntu
MEDIUM

An integer overflow in the BSON document encoding component of the MongoDB Python Drivers bundled native extension may occur when a single document is built from an unusually large amount of caller-supplied data. Size arithmetic is performed in a signed 32-bit type, and the guard meant to catch the overflow is written in a form whose behavior is not defined by the C language standard. A party with no privileges who can place a very large value into data that an application encodes may, depending on how the native extension was built, cause a write outside the bounds of an allocated buffer inside the applications own process.

Weakness

The product performs a calculation that can produce an integer overflow or wraparound when the logic assumes that the resulting value will always be larger than the original value. This occurs when an integer value is incremented to a value that is too large to store in the associated representation. When this occurs, the value may become a very small or negative number.

Affected Software

NameVendorStart VersionEnd Version
Red Hat OpenShift AI 2.25RedHatrhoai/odh-pipeline-runtime-pytorch-llmcompressor-cuda-py312-rhel9:1791867208*
Red Hat OpenShift AI 3.5RedHatrhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9:1791952873*
Red Hat OpenShift AI 3.5RedHatrhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9:1791816462*
Red Hat OpenShift AI 3.5RedHatrhoai/odh-pipeline-runtime-pytorch-llmcompressor-cuda-py312-rhel9:1791816487*
Red Hat OpenShift AI 3.5RedHatrhoai/odh-pipeline-runtime-pytorch-rocm-py312-rhel9:1791951049*
Red Hat OpenShift AI 3.5RedHatrhoai/odh-pipeline-runtime-tensorflow-cuda-py312-rhel9:1791951047*
Red Hat OpenShift AI 3.5RedHatrhoai/odh-pipeline-runtime-tensorflow-rocm-py312-rhel9:1791950760*
Red Hat OpenShift AI 3.5RedHatrhoai/odh-workbench-jupyter-datascience-cpu-py312-rhel9:1791903880*
Red Hat OpenShift AI 3.5RedHatrhoai/odh-workbench-jupyter-pytorch-cuda-py312-rhel9:1791817064*
Red Hat OpenShift AI 3.5RedHatrhoai/odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-rhel9:1791817844*
Red Hat OpenShift AI 3.5RedHatrhoai/odh-workbench-jupyter-pytorch-rocm-py312-rhel9:1791816223*
Red Hat OpenShift AI 3.5RedHatrhoai/odh-workbench-jupyter-tensorflow-cuda-py312-rhel9:1791899290*
Red Hat OpenShift AI 3.5RedHatrhoai/odh-workbench-jupyter-tensorflow-rocm-py312-rhel9:1791816175*
Red Hat OpenShift AI 3.5RedHatrhoai/odh-workbench-jupyter-trustyai-cpu-py312-rhel9:1791816316*

Potential Mitigations

  • Use a language that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid.
  • If possible, choose a language or compiler that performs automatic bounds checking.
  • Use a vetted library or framework that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid [REF-1482].
  • Use libraries or frameworks that make it easier to handle numbers without unexpected consequences.
  • Examples include safe integer handling packages such as SafeInt (C++) or IntegerLib (C or C++). [REF-106]
  • Perform input validation on any numeric input by ensuring that it is within the expected range. Enforce that the input meets both the minimum and maximum requirements for the expected range.
  • Use unsigned integers where possible. This makes it easier to perform validation for integer overflows. When signed integers are required, ensure that the range check includes minimum values as well as maximum values.
  • Understand the programming language’s underlying representation and how it interacts with numeric calculation (CWE-681). Pay close attention to byte size discrepancies, precision, signed/unsigned distinctions, truncation, conversion and casting between types, “not-a-number” calculations, and how the language handles numbers that are too large or too small for its underlying representation. [REF-7]
  • Also be careful to account for 32-bit, 64-bit, and other potential differences that may affect the numeric representation.

References