CVE-2026-13717 in RHOAI MaaS
Summary
by MITRE • 08/11/2026
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.
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Analysis
by VulDB Data Team • 08/11/2026
This vulnerability resides within the Red Hat OpenShift AI MaaS Gateway component where improper configuration creates a critical security weakness that undermines the integrity and confidentiality of model serving operations. The flaw represents a severe misconfiguration issue that allows low-privilege users to gain unauthorized access to sensitive data flows within the machine learning infrastructure. The vulnerability stems from inadequate access controls and network segmentation mechanisms that should normally isolate different user contexts and protect sensitive information processing within the MaaS environment.
The technical implementation of this flaw enables a standard user to perform man-in-the-middle attacks against model serving traffic without requiring elevated privileges or specialized attack capabilities. This represents a classic privilege escalation vulnerability that operates at the network communication layer rather than at the application logic level. The gateway's configuration fails to properly validate or enforce access controls on incoming and outgoing requests, allowing unauthorized interception of data flows between clients and model services. This misconfiguration creates an attack surface where sensitive information including authentication tokens, user input prompts, and model outputs become accessible to unauthorized parties.
The operational impact of this vulnerability extends far beyond simple information disclosure, as it enables full data tampering capabilities that can compromise the integrity of machine learning workflows. Attackers could modify input parameters to influence model predictions, alter output responses to manipulate system behavior, or inject malicious content into the serving pipeline. The exposure of access keys and authentication tokens creates additional risks for credential compromise and lateral movement within the OpenShift environment. This vulnerability directly impacts the security posture of organizations relying on RHOAI for sensitive AI workloads where data confidentiality and integrity are paramount requirements.
Organizations should immediately implement network segmentation policies to isolate MaaS gateway components from untrusted networks and enforce strict access controls using RBAC mechanisms. Configuration management procedures must be enhanced to ensure proper gateway initialization and validation of security parameters during deployment. Network monitoring solutions should be deployed to detect anomalous traffic patterns that may indicate unauthorized interception attempts. The implementation of mutual TLS authentication between gateway components and model services can provide additional protection layers. Security teams should conduct thorough access control reviews and implement principle of least privilege configurations for all MaaS gateway components.
This vulnerability aligns with CWE-284 (Improper Access Control) and CWE-310 (Cryptographic Issues) categories, representing a fundamental breakdown in security controls that allows unauthorized data access and manipulation. From an ATT&CK framework perspective, this weakness maps to T1566 (Phishing) through credential compromise and T1071.004 (Application Layer Protocol: DNS) for potential exfiltration techniques. The vulnerability demonstrates the critical importance of proper configuration management in cloud-native environments where microservices and API gateways form the backbone of modern AI infrastructure. Organizations must prioritize secure configuration practices and implement automated compliance checking to prevent similar misconfigurations in other components of their machine learning platforms.
The remediation approach requires immediate attention to gateway configuration parameters, including enforcement of authentication requirements, proper certificate management, and implementation of network policies that restrict access based on user roles and service dependencies. Regular security assessments should include validation of gateway configurations against established security baselines and compliance frameworks such as NIST CSF or CIS Controls. The vulnerability highlights the need for comprehensive security training for DevOps teams responsible for managing OpenShift environments and emphasizes the importance of integrating security controls into continuous integration and deployment pipelines to prevent configuration drift that could lead to similar issues in production environments.