CVE-2026-16745 in OpenShift AI
Summary
by MITRE • 07/23/2026
A flaw was found in odh-dashboard, the web console component of Red Hat OpenShift AI (RHOAI). Due to incorrect network binding, a malicious actor within the cluster can bypass authentication and impersonate any user by providing an arbitrary access token. This allows an attacker to gain unauthorized access to the Kubernetes API, potentially leading to arbitrary code execution, privilege escalation, or information disclosure.
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Analysis
by VulDB Data Team • 07/23/2026
The vulnerability exists within the odh-dashboard component of Red Hat OpenShift AI, which serves as the web console interface for managing machine learning workloads within the OpenShift environment. This flaw represents a critical authentication bypass issue that fundamentally undermines the security posture of the platform. The root cause lies in improper network binding configurations that fail to adequately validate token authenticity and user identity before granting access to protected resources.
The technical implementation flaw stems from the dashboard's failure to properly enforce authorization boundaries when processing access tokens. When a malicious actor within the cluster provides an arbitrary access token, the system incorrectly accepts it without sufficient validation checks. This misconfiguration creates a path where unauthorized users can impersonate legitimate identities by simply crafting or obtaining valid-looking tokens. The vulnerability operates at the application layer and affects the core authentication mechanisms that should prevent unauthorized access to Kubernetes resources.
From an operational impact perspective, this flaw enables attackers to escalate privileges within the cluster environment without requiring external network access or complex exploitation techniques. The ability to impersonate any user means that even low-privilege attackers can gain access to sensitive data, modify configurations, or execute arbitrary code on cluster nodes. This represents a severe privilege escalation vulnerability that allows attackers to potentially compromise the entire Kubernetes infrastructure and access all resources managed by the platform.
The security implications extend beyond simple unauthorized access to include potential data exfiltration, lateral movement within the cluster, and complete system compromise. Attackers could leverage this vulnerability to establish persistent access, manipulate machine learning workloads, or use the compromised dashboard as a stepping stone for further attacks against other systems in the environment. This vulnerability directly impacts the principle of least privilege and undermines the security model that OpenShift AI relies upon for protecting sensitive data and computational resources.
Organizations should implement immediate mitigations including disabling unnecessary dashboard access, enforcing strict network policies to limit inter-cluster communication, and implementing additional authentication layers such as OAuth2 introspection endpoints. The vulnerability aligns with CWE-287, which addresses authentication failures in security systems, and maps to ATT&CK technique T1078 for valid accounts and T1566 for credential harvesting. Regular security audits should verify that all components properly validate tokens through secure channels and implement proper authorization checks before granting access to protected resources.