CVE-2026-18951 in OpenShift AI
Summary
by MITRE • 08/11/2026
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.
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Analysis
by VulDB Data Team • 08/11/2026
The vulnerability exists within the Red Hat OpenShift AI RHOAI overlay implementation for the training operator, representing a critical privilege escalation risk in Kubernetes cluster environments. This flaw stems from improper permission aggregation where the overlay incorrectly bundles trainjobs management capabilities into the standard Kubernetes edit ClusterRole, which should only grant limited administrative privileges to namespace resources. The technical misconfiguration creates an unintended access vector that undermines the principle of least privilege fundamental to secure multi-tenant cloud platforms.
The core technical flaw manifests as a role-based access control (RBAC) misconfiguration where the RHOAI overlay extends the edit ClusterRole with trainjobs management permissions, effectively granting users with namespace editor privileges elevated capabilities beyond their intended scope. This misalignment between expected and actual permissions creates a dangerous privilege escalation path that directly violates the security principle of separation of duties. The vulnerability is particularly concerning because it operates within the standard Kubernetes RBAC framework, making it difficult to detect through conventional security scanning tools that may not recognize this specific overlay behavior as anomalous.
When combined with a separate vulnerability TRN-01 that allows arbitrary pod configurations, the impact becomes exponentially more severe and represents a complete breakdown of cluster security boundaries. Attackers with merely namespace editor privileges can now leverage this misconfiguration to create malicious TrainJobs that spawn pods with arbitrary configurations, potentially leading to full cluster compromise through container escape techniques or by exploiting additional vulnerabilities in the underlying infrastructure. This combination creates a pathway for attackers to move laterally within the cluster and establish persistent access.
The operational impact extends beyond simple privilege escalation to encompass potential data exfiltration, service disruption, and complete cluster compromise. Organizations using RHOAI overlay deployments face significant risk of unauthorized access to sensitive machine learning workloads and training data, particularly in multi-tenant environments where different teams share the same cluster infrastructure. This vulnerability affects not only the immediate security posture but also undermines trust in automated deployment mechanisms and the overall integrity of AI/ML workflows within OpenShift environments.
Mitigation strategies should focus on immediate remediation through proper RBAC configuration that separates trainjobs management permissions from standard edit roles, implementing more granular access controls and regular audit procedures. Organizations should enforce strict segregation of duties for AI/ML operations and implement monitoring solutions that can detect unauthorized TrainJob creation attempts. The fix requires careful review of the overlay implementation to ensure permissions are properly scoped and that the edit ClusterRole maintains its intended scope while dedicated trainjobs management roles provide necessary elevated capabilities only to authorized personnel. This vulnerability demonstrates the critical importance of proper access control design in cloud-native environments and aligns with CWE-276 for improper privilege management and ATT&CK techniques related to privilege escalation through misconfigured permissions.
The broader implications highlight the complexity of managing security in sophisticated AI/ML platforms where traditional Kubernetes RBAC mechanisms may not adequately address platform-specific overlay behaviors. This vulnerability underscores the need for comprehensive security testing that includes platform-specific configurations and the importance of understanding how third-party operators interact with core Kubernetes security controls. Organizations should implement regular security assessments of their AI/ML deployment environments to identify similar misconfigurations that could create similar privilege escalation paths across different platform components.