CVE Vulnerabilities

CVE-2026-63209

Integer Overflow or Wraparound

Published: Sep 29, 2026 | Modified: Sep 30, 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

compress provides various compression algorithms. Prior to version 1.18.7, a signed integer overflow vulnerability in s2.NewDict() allows an attacker to bypass repeat index validation by supplying a dictionary with a uvarint-encoded repeat value exceeding MaxInt64. When Dict.Encode() is subsequently called, the overflowed negative repeat value causes an out-of-bounds memory access via unsafe.Pointer arithmetic, crashing the process with SIGSEGV. This issue has been patched in version 1.18.7.

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
Logging Subsystem for Red Hat OpenShift 6.0RedHatopenshift-logging/logging-loki-rhel9:1791500729*
Red Hat Advanced Cluster Management for Kubernetes 2.16RedHatrhacm2/lighthouse-coredns-rhel9:1790670231*
Red Hat Hardened ImagesRedHatnats-server2-12-main-2.12.15-0.1.hum1*
Red Hat Hardened ImagesRedHatk6v1-main-1.8.1-0.1.hum1*
Red Hat Hardened ImagesRedHatrekor-main-1.5.4-0.1.hum1*
Red Hat Hardened ImagesRedHatnats-server2-14-main-2.14.7-0.1.hum1*
Red Hat Hardened ImagesRedHatopentelemetry-collector-main-0.161.0-0.1.hum1*
Red Hat Hardened ImagesRedHatopentelemetry-collector-contrib-main-0.161.0-0.1.hum1*
Red Hat Hardened ImagesRedHatopentelemetry-collector-k8s-main-0.161.0-0.1.hum1*
Red Hat Hardened ImagesRedHathelm3-main-3.22.0-0.2.hum1*
Red Hat Hardened ImagesRedHathelm4-main-4.3.0-0.2.hum1*
Red Hat Hardened ImagesRedHatgrype-main-0.119.0-0.1.hum1*
Red Hat Hardened ImagesRedHatsyft-main-1.52.0-0.1.hum1*
Red Hat Hardened ImagesRedHatk6v2-main-2.3.0-0.1.hum1*
Red Hat Hardened ImagesRedHatbuildah-main-1.45.1-1.hum1*
Red Hat Hardened ImagesRedHatskopeo-main-1.24.1-1.hum1*
Red Hat Hardened ImagesRedHatpodman-main-6.1.2-1.hum1*
Red Hat Hardened ImagesRedHattempo2-10-main-2.10.8-0.4.hum1*
Red Hat Hardened ImagesRedHattempo3-0-main-3.0.3-0.4.hum1*
Red Hat Hardened ImagesRedHattrivy-main-0.74.0-0.4.hum1*
Red Hat Hardened ImagesRedHatspire1-15-main-1.15.3-0.2.hum1*
Red Hat Hardened ImagesRedHatloki3-7-main-3.7.8-0.3.hum1*
Red Hat Hardened ImagesRedHatgrafana12-4-main-12.4.10-0.9.hum1*
Red Hat Hardened ImagesRedHatgrafana13-1-main-13.1.6-0.4.hum1*
Red Hat Hardened ImagesRedHatgrafana13-2-main-13.2.1-0.8.hum1*
Red Hat Hardened ImagesRedHatloki3-6-main-3.6.17-0.3.hum1*
Red Hat OpenShift AI 2.25RedHatrhoai/odh-kueue-controller-rhel9:1791854046*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-model-metadata-collection-rhel9:1790958083*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-model-registry-job-async-upload-rhel9:1791870571*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-model-registry-rhel9:1791869365*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9:1791829804*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-pipeline-runtime-minimal-cpu-py312-rhel9:1791829069*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9:1791829182*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-pipeline-runtime-pytorch-llmcompressor-cuda-py312-rhel9:1791829658*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-pipeline-runtime-pytorch-rocm-py312-rhel9:1791868244*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-pipeline-runtime-tensorflow-cuda-py312-rhel9:1791828837*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-pipeline-runtime-tensorflow-rocm-py312-rhel9:1791868241*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-codeserver-datascience-cpu-py312-rhel9:1791788267*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-datascience-cpu-py312-rhel9:1791872084*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-minimal-cpu-py312-rhel9:1791870673*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-minimal-cuda-py312-rhel9:1791953196*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-minimal-rocm-py312-rhel9:1791869801*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-pytorch-cuda-py312-rhel9:1791829615*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-rhel9:1791832538*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-pytorch-rocm-py312-rhel9:1791868194*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-tensorflow-cuda-py312-rhel9:1791833225*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-tensorflow-rocm-py312-rhel9:1791868238*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-workbench-jupyter-trustyai-cpu-py312-rhel9:1791829530*
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*
Red Hat OpenShift distributed tracing 3.11RedHatrhosdt/tempo-jaeger-query-rhel9:1791452569*

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