CVE-2026-18948 in OpenShift AI
Summary
by MITRE • 08/11/2026
A flaw was found in Feast. The system improperly deserializes user-defined functions (UDFs) stored in its registry, which are serialized using the 'dill' library. This allows a remote attacker to store a malicious UDF, leading to unauthenticated arbitrary code execution on the feature server in default configurations. An authenticated attacker can also achieve arbitrary code execution on the registry server by bypassing authorization checks during deserialization. This vulnerability can result in cross-tenant data access and lateral movement within the system.
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Analysis
by VulDB Data Team • 08/11/2026
This vulnerability exists within the Feast machine learning feature store system where improper deserialization of user-defined functions creates a critical security flaw that can be exploited remotely. The issue stems from how Feast handles serialized UDFs stored in its registry, specifically using the dill library for serialization purposes. When these UDFs are later deserialized without proper validation or sandboxing measures, they create an arbitrary code execution vector that can be leveraged by attackers to gain unauthorized access to the system. The vulnerability is particularly concerning because it operates in default configurations, meaning that organizations deploying Feast without additional security hardening are immediately exposed to this risk.
The technical implementation of this flaw allows for multiple attack vectors depending on the attacker's authentication status. In the case of remote unauthenticated access, an attacker can simply upload a malicious UDF to the registry and trigger its execution when the feature server processes it, enabling arbitrary code execution on the server itself. For authenticated attackers who have access to the system, the vulnerability becomes even more dangerous as they can bypass existing authorization controls during the deserialization process, allowing them to execute commands directly on the registry server. This dual attack surface significantly increases the potential impact of exploitation and demonstrates a fundamental flaw in the system's security model.
The operational impact of this vulnerability extends far beyond simple code execution, creating serious implications for data security and system integrity within Feast deployments. Attackers who successfully exploit this vulnerability can achieve cross-tenant data access, meaning that malicious actors can potentially read or modify data belonging to other tenants within the same Feast instance. This capability enables lateral movement throughout the system, allowing attackers to explore and compromise additional components of the feature store infrastructure. The vulnerability essentially creates a backdoor that can be used to escalate privileges and expand access within the system, making it particularly dangerous in multi-tenant environments where data isolation is critical.
Security mitigations for this vulnerability should focus on implementing strict input validation and sandboxing measures during UDF deserialization processes. Organizations should consider disabling or restricting the ability to store arbitrary UDFs when default configurations are used, as well as implementing proper authorization controls that cannot be bypassed during the deserialization phase. The implementation of secure coding practices that follow established security standards such as those outlined in CWE 502 - Deserialization of Untrusted Data provides a framework for addressing this specific vulnerability class. Additionally, organizations should consider using principle of least privilege configurations and implementing network segmentation to limit the potential damage from successful exploitation attempts.
This vulnerability aligns with several ATT&CK techniques including T1059.001 - Command and Scripting Interpreter: PowerShell and T1021.004 - Remote Services: SSH, as attackers can leverage the arbitrary code execution capabilities to establish persistent access and move laterally through the system. The weakness also maps to CWE 1237 - Improper Restriction of XML External Entity Reference, though more specifically it represents a deserialization vulnerability that lacks proper security controls during object reconstruction. Organizations should implement regular security assessments and penetration testing to identify similar vulnerabilities in their machine learning infrastructure, particularly focusing on serialization mechanisms that handle user-provided content without proper security validation.