Lundi 19 octobre 2026

Red Hat Enterprise Linux AI 3.3 for RHEL 9 · Red Hat Enterprise Linux AI 3.4 for RHEL 9 · Red Hat Enterprise Linux AI 3.5 for RHEL 9 · Red Hat AI Inference Server 3.2

A remote code execution vulnerability was found in libaom, the reference AV1 codec implementation. Insufficient bounds validation in the AV1 encoder's SVC (Scalable Video Coding) layer ID control allows an attacker to supply crafted video frame pixels that overlap with internal encoder layer context structures. In fork-based video processing services, an attacker can use this to hijack the cyclic refresh map pointer, brute-force the process base address via a crash oracle, and redirect control flow to achieve arbitrary command execution. Exploitation requires the target service to use libaom with SVC encoding enabled and accept attacker-supplied video frames.

Red Hat Enterprise Linux AI 3.3 for RHEL 9 · Red Hat Enterprise Linux AI 3.4 for RHEL 9 · Red Hat Enterprise Linux AI 3.5 for RHEL 9 · Red Hat AI Inference Server 3.2

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).

Red Hat Enterprise Linux AI 3.3 for RHEL 9 · Red Hat Enterprise Linux AI 3.4 for RHEL 9 · Red Hat Enterprise Linux AI 3.5 for RHEL 9 · Red Hat AI Inference Server 3.2

An arbitrary address write 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 an attacker to inject an arbitrary pointer into the cyclic refresh map field via crafted image pixel values. The encoder then writes approximately 1,200 bytes at the attacker-controlled address. This is fully deterministic and does not require a separate information leak. An attacker who can supply frames to a network-facing libaom encoder with SVC enabled could exploit this for denial of service or potential code execution.

Red Hat OpenShift AI 2.25 · Red Hat OpenShift AI 3.3 · Red Hat OpenShift AI 3.4 · Red Hat OpenShift AI (RHOAI)

A flaw was found in Feast. An authorization bypass vulnerability exists in the /materialize and /materialize-incremental endpoints. By sending a specially crafted request that omits the feature_views field, an attacker can bypass intended permission checks. This allows an unauthenticated remote attacker, or any authenticated user, to trigger a full re-materialization of all feature views. The consequence is a Denial of Service (DoS) due to data corruption and significant resource consumption across all tenants.

Red Hat OpenShift AI 2.25 · Red Hat OpenShift AI 3.3 · Red Hat OpenShift AI 3.4 · Red Hat OpenShift AI (RHOAI)

A flaw was found in Feast and feast-operator. The default configuration for both the Feast SDK and the feast-operator is "no_auth," meaning no security manager is installed. This default allows unauthenticated and unauthorized access to feature-server, registry-server, and offline-server endpoints. A remote attacker, by exploiting this missing authentication, could achieve remote code execution (RCE) by storing a malicious User-Defined Function (UDF) on the feature-server, trigger a denial of service (DoS) by forcing re-materialization of all tenant features, and gain unauthorized access to cross-tenant data.

Red Hat OpenShift AI 3.4 · Red Hat OpenShift AI (RHOAI)

A flaw was found in the Red Hat OpenShift AI (RHOAI) MaaS Gateway. Improper configuration of the Gateway in a model-serving context allows a standard user with low privileges to intercept, read, log, and alter all MaaS model traffic. This includes sensitive information such as access keys, input prompts, and outputs, leading to significant information disclosure and data tampering.