CVE-2025-33233 in Merlin Transformers4Rec
Summary
by MITRE • 01/20/2026
NVIDIA Merlin Transformers4Rec for all platforms contains a vulnerability where an attacker could cause code injection. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering.
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Analysis
by VulDB Data Team • 01/27/2026
The vulnerability identified as CVE-2025-33233 affects NVIDIA Merlin Transformers4Rec across all supported platforms, representing a critical security flaw that enables remote code injection attacks. This issue stems from inadequate input validation and sanitization mechanisms within the software's processing pipeline, creating a pathway for malicious actors to execute arbitrary code on affected systems. The vulnerability exists in the data handling and model execution components of the Transformers4Rec framework, which is designed for large-scale recommendation systems and natural language processing applications. Attackers can leverage this weakness by crafting malicious inputs that bypass normal validation checks and inject harmful code sequences into the processing environment.
The technical exploitation of this vulnerability follows patterns consistent with CWE-94, which describes improper control of generation of code, and aligns with ATT&CK technique T1059.001 for command and scripting interpreter. The flaw allows adversaries to inject code through various entry points within the software architecture, potentially leading to complete system compromise. When successfully exploited, the vulnerability enables attackers to execute malicious code with the privileges of the affected application, which typically runs with elevated permissions due to the nature of machine learning workloads. The attack surface extends beyond simple code execution to include privilege escalation opportunities, as the injected code can leverage the application's elevated access to perform additional malicious activities.
The operational impact of CVE-2025-33233 is severe and multifaceted, affecting organizations that deploy NVIDIA Merlin Transformers4Rec for production recommendation systems, content filtering, or natural language processing tasks. Organizations using this framework may experience unauthorized access to sensitive data, system compromise, and potential data exfiltration from their machine learning infrastructure. The vulnerability's presence in all platforms indicates a widespread risk across different deployment environments, including cloud deployments, on-premises systems, and hybrid configurations. Data tampering capabilities provide attackers with the means to manipulate recommendation results, potentially affecting user experiences, business decisions, and competitive advantages. Information disclosure risks include exposure of training datasets, model parameters, and other proprietary information that could be leveraged for additional attacks or competitive advantage.
Mitigation strategies for this vulnerability should focus on immediate patching and configuration hardening measures. Organizations should prioritize applying the latest NVIDIA security updates and patches as soon as they become available, following the vendor's security advisory guidance. Network segmentation and access controls should be implemented to limit exposure of affected systems to untrusted networks. Input validation should be strengthened through comprehensive sanitization of all data inputs, particularly those originating from external sources or user interactions. Monitoring and logging mechanisms should be enhanced to detect anomalous code execution patterns and potential exploitation attempts. Security teams should also consider implementing application whitelisting policies and restricting the execution permissions of the Transformers4Rec components to minimize the impact of successful exploitation attempts. Additionally, regular security assessments and penetration testing should be conducted to identify and remediate similar vulnerabilities in the broader machine learning infrastructure ecosystem.