Vulnerabilities
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CVE Vulnerabilities

CVE-2026-56210

Out-of-bounds Read

Published: Jun 19, 2026 | Modified: Sep 24, 2026
CVSS 3.x
N/A
Source:
NVD
CVSS 2.x
RedHat/V2
RedHat/V3
7.1 IMPORTANT
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:L/I:N/A:H
Ubuntu
MEDIUM
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Additional information
NVDhttps://nvd.nist.gov/vuln/detail/CVE-2026-56210
CWEhttps://cwe.mitre.org/data/definitions/125.html

A heap-buffer-overflow read vulnerability was found in libaom, the reference AV1 codec implementation. A missing bounds check in the SVC (Scalable Video Coding) layer ID control function allows setting a spatial_layer_id exceeding the configured number of layers. This causes an out-of-bounds heap read of approximately 40,728 bytes when computing a layer context array index. An attacker who can influence SVC encoder parameters in a network-facing service could exploit this for information disclosure (heap content leak) or denial of service (segmentation fault from hitting unmapped memory).

Weakness

The product reads data past the end, or before the beginning, of the intended buffer.

Affected Software

NameVendorStart VersionEnd Version
Red Hat Enterprise Linux AI 3.3 for RHEL 9RedHataom-0:3.14.0-1.el9ai*
Red Hat Enterprise Linux AI 3.4 for RHEL 9RedHataom-0:3.14.0-1.el9ai*
Red Hat Enterprise Linux AI 3.5 for RHEL 9RedHataom-0:3.14.0-1.el9ai*
Red Hat AI Inference Server 3.2RedHatrhaiis/vllm-cuda-rhel9:1787860580*
Red Hat AI Inference Server 3.2RedHatrhaiis/model-opt-cuda-rhel9:1787772157*
Red Hat AI Inference Server 3.2RedHatrhaiis/vllm-rocm-rhel9:1787884873*
Red Hat AI Inference Server 3.3RedHatrhaiis/model-opt-cuda-rhel9:1789508287*
Red Hat AI Inference Server 3.3RedHatrhaiis/vllm-spyre-rhel9:1789502493*
Red Hat AI Inference Server 3.3RedHatrhaiis/vllm-rocm-rhel9:1789504371*
Red Hat AI Inference Server 3.3RedHatrhaiis/vllm-cuda-rhel9:1789582776*
Red Hat Hardened ImagesRedHataom-main-3.14.0-0.1.hum1*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-neuron-rhel9:1789137552*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-rocm-7.0-rhel9:1789137548*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-cuda-13.0-rhel9:1789137636*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-tpu-rhel9:1789137548*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-rocm-6.4-rhel9:1789137548*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-cpu-rhel9:1789137641*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-spyre-rhel9:1789137607*
Red Hat OpenShift AI 3.3RedHatrhai/base-image-cuda-12.9-rhel9:1789137636*
Red Hat OpenShift AI 3.4RedHatrhoai/odh-mlserver-rhel9:1786611800*
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 3.4RedHatrhai/base-image-tpu-rhel9:1788197680*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-cpu-rhel9:1788197530*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-gaudi-rhel9:1788197632*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-spyre-rhel9:1788197747*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-rocm-7.1-rhel9:1788197549*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-cuda-12.9-rhel9:1788199622*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-cuda-13.0-rhel9:1788197744*
Red Hat OpenShift AI 3.4RedHatrhai/base-image-rocm-6.4-rhel9:1788197677*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-neuron-rhel9:1788198404*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-cpu-rhel9:1788198407*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-tpu-rhel9:1788197492*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-gaudi-rhel9:1788197542*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-cuda-13.0-rhel9:1788197975*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-rubin-rhel9:1788197512*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-spyre-rhel9:1788197688*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-cuda-12.9-rhel9:1788197385*
Red Hat OpenShift AI 3.5RedHatrhai/base-image-rocm-7.14-rhel9:1788198016*
AomUbuntuesm-apps/jammy*
AomUbuntujammy*
AomUbuntunoble*
AomUbuntuquesting*
AomUbunturesolute*
AomUbuntuupstream*

Potential Mitigations

  • Assume all input is malicious. Use an “accept known good” input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.
  • When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, “boat” may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as “red” or “blue.”
  • Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code’s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
  • To reduce the likelihood of introducing an out-of-bounds read, ensure that you validate and ensure correct calculations for any length argument, buffer size calculation, or offset. Be especially careful of relying on a sentinel (i.e. special character such as NUL) in untrusted inputs.

References

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