CVE-2025-10725: Red Hat OpenShift AI Privilege Escalation

CVE-2025-10725 Overview

A critical privilege escalation vulnerability has been identified in Red Hat OpenShift AI Service. This flaw allows a low-privileged attacker with access to an authenticated account, such as a data scientist using a standard Jupyter notebook, to escalate their privileges to full cluster administrator. The vulnerability enables complete compromise of the cluster's confidentiality, integrity, and availability. Attackers can steal sensitive data, disrupt all services, and take control of the underlying infrastructure, leading to a total breach of the platform and all applications hosted on it.

Critical Impact

Low-privileged users can escalate to full cluster administrator, enabling complete platform takeover including data theft, service disruption, and infrastructure control.

Affected Products

• Red Hat OpenShift AI Service
• OpenDataHub Operator

Discovery Timeline

• 2025-09-30 - CVE-2025-10725 published to NVD
• 2025-12-24 - Last updated in NVD database

Technical Details for CVE-2025-10725

Vulnerability Analysis

This vulnerability is classified under CWE-266 (Incorrect Privilege Assignment), indicating a fundamental flaw in how privileges are assigned or managed within the Red Hat OpenShift AI Service platform. The vulnerability exists within the authentication and authorization mechanisms that govern user access to cluster resources.

The attack originates from a network-accessible vector and requires only low-level privileges to exploit. A standard authenticated user, such as a data scientist with access to Jupyter notebooks within the OpenShift AI environment, can leverage this flaw to bypass intended access controls and gain cluster administrator privileges. The scope-change characteristic means that the attacker can impact resources beyond the vulnerable component's security scope, affecting the entire cluster infrastructure.

The exploitation does not require user interaction, making it particularly dangerous in multi-tenant environments where various users share the same OpenShift AI infrastructure. Once an attacker achieves privilege escalation, they gain full control over the Kubernetes cluster, including the ability to access secrets, modify workloads, deploy malicious containers, and potentially pivot to other connected systems.

Root Cause

The root cause stems from incorrect privilege assignment (CWE-266) within the OpenDataHub Operator, which manages the OpenShift AI Service deployment. The flaw allows authenticated users to bypass role-based access control (RBAC) restrictions and assume elevated permissions that should be reserved for cluster administrators. This incorrect privilege assignment enables unauthorized access to sensitive cluster resources and administrative functions.

Attack Vector

The attack vector is network-based, requiring the attacker to have an authenticated session within the OpenShift AI environment. The exploitation scenario involves:

1. An attacker with low-privileged access (e.g., data scientist role) authenticates to the OpenShift AI platform
2. The attacker leverages the privilege escalation flaw through their standard interface (such as a Jupyter notebook session)
3. Through the vulnerability, the attacker bypasses RBAC controls and gains cluster-admin privileges
4. With full administrative access, the attacker can access all secrets, modify cluster configurations, deploy malicious workloads, and compromise the entire infrastructure

The attack does not require user interaction and can be executed remotely over the network, making detection challenging without proper monitoring in place.

Detection Methods for CVE-2025-10725

Indicators of Compromise

• Unexpected RBAC role bindings granting cluster-admin privileges to non-administrative users
• Unusual Kubernetes API server access patterns from data scientist or notebook user accounts
• Unauthorized access to cluster secrets or sensitive ConfigMaps
• Unexpected modifications to ClusterRole or ClusterRoleBinding resources

Detection Strategies

• Monitor Kubernetes audit logs for privilege escalation attempts and unusual role binding creations
• Implement alerts for any new cluster-admin role assignments outside of approved change windows
• Review OpenDataHub Operator logs for abnormal authentication or authorization events
• Track API server requests for sensitive operations originating from low-privileged user contexts

Monitoring Recommendations

• Enable comprehensive Kubernetes audit logging with focus on RBAC-related events
• Deploy runtime security monitoring to detect unauthorized privilege escalation attempts
• Implement behavioral analysis to identify anomalous user activity patterns within OpenShift AI
• Configure alerts for access to sensitive cluster resources from unexpected service accounts

How to Mitigate CVE-2025-10725

Immediate Actions Required

• Apply the latest security patches from Red Hat immediately (see advisories RHSA-2025:16981 through RHSA-2025:17501)
• Audit existing cluster role bindings to identify any unauthorized privilege escalations
• Review and restrict network access to the OpenShift AI management interfaces
• Implement additional authentication controls for administrative actions

Patch Information

Red Hat has released multiple security advisories addressing this vulnerability. Organizations should apply the appropriate patches based on their deployment:

• Red Hat Security Advisory RHSA-2025:16981
• Red Hat Security Advisory RHSA-2025:16982
• Red Hat Security Advisory RHSA-2025:16983
• Red Hat Security Advisory RHSA-2025:16984
• Red Hat Security Advisory RHSA-2025:17501

The fix has been implemented in the OpenDataHub Operator. See GitHub Pull Request #2571 and the associated commit for technical details.

Workarounds

• Restrict network access to OpenShift AI services to trusted networks only until patches can be applied
• Implement strict RBAC policies limiting the permissions available to data scientist accounts
• Enable additional monitoring and alerting for privilege escalation indicators
• Consider temporarily disabling Jupyter notebook access for non-essential users until the environment is patched

# Example: Review current cluster role bindings for unauthorized escalations
kubectl get clusterrolebindings -o json | jq '.items[] | select(.roleRef.name == "cluster-admin") | {name: .metadata.name, subjects: .subjects}'

# List all role bindings in OpenShift AI namespaces
oc get rolebindings -n redhat-ods-applications -o wide
oc get clusterrolebindings | grep -i opendatahub