Threats Tagged 'cve-2026-18951'

Release of RHOAI 3.4.4 provides these changes:

A path traversal and arbitrary file overwrite vulnerability exists in Argo Workflows used within Red Hat OpenShift AI. This flaw allows an attacker with workflow submission privileges to submit a malicious archive containing symbolic links that bypass extraction directory restrictions, leading to overwriting files inside the workflow pod. Successful exploitation can result in arbitrary command execution during pod startup. The impact is limited to the compromised pod and does not extend to host-level compromise. No official fix or mitigation currently meets Red Hat's standards for deployment or stability.

Red Hat OpenShift AI version 3.4.3 addresses multiple security issues identified by CVE-2026-13717 and others. The advisory announces updated images for Red Hat OpenShift AI to mitigate these vulnerabilities. The update is critical and users are advised to upgrade their clusters following Red Hat's official documentation. No specific technical details or exploit information are provided in the advisory.

A flaw was found in the Red Hat OpenShift AI (RHOAI) overlay for the training operator. The RHOAI overlay incorrectly aggregates `trainjobs` management permissions into the native Kubernetes `edit ClusterRole`. This allows any user with `edit ClusterRole` permissions in a namespace to create, modify, and delete `TrainJobs`. When combined with a separate vulnerability (TRN-01) that permits arbitrary pod configurations, a remote attacker with namespace editor privileges could exploit this to escalate privileges, potentially leading to arbitrary code execution.

A flaw was found in the Red Hat OpenShift AI (RHOAI) overlay for the training operator. The RHOAI overlay incorrectly aggregates `trainjobs` management permissions into the native Kubernetes `edit ClusterRole`. This allows any user with `edit ClusterRole` permissions in a namespace to create, modify, and delete `TrainJobs`. When combined with a separate vulnerability (TRN-01) that permits arbitrary pod configurations, a remote attacker with namespace editor privileges could exploit this to escalate privileges, potentially leading to arbitrary code execution.

A Regular Expression Denial of Service (ReDoS) vulnerability (CVE-2026-15154) was found in guardrails-detectors, a component of Red Hat OpenShift AI. This flaw allows a remote attacker to send specially crafted regular expressions to the public detection API, causing catastrophic backtracking. The result is a worker process consuming 100% CPU indefinitely, leading to denial of service for the guardrails-mediated large language model (LLM) pipeline. The vulnerability affects specific versions of Red Hat OpenShift AI and has a medium severity rating with a CVSS score of 6.5. Updated images and a fixed version 2.25.10 are available from Red Hat to address this issue.

The RHTAS Operator can be used with OpenShift Container Platform 4.16, 4.17, 4.18, 4.19, 4.20 and 4.21

The fence-agents packages provide a collection of scripts for handling remote power management for cluster devices. They allow failed or unreachable nodes to be forcibly restarted and removed from the cluster. Security Fix(es): * urllib3: urllib3 Streaming API improperly handles highly compressed data (CVE-2025-66471) * urllib3: urllib3 vulnerable to decompression-bomb safeguard bypass when following HTTP redirects (streaming API) (CVE-2026-21441) For more details about the security issue(s), including the impact, a CVSS score, acknowledgments, and other related information, refer to the CVE page(s) listed in the References section.