CVE-2025-33233: NVIDIA Merlin Transformers4Rec RCE Flaw

CVE-2025-33233 Overview

NVIDIA Merlin Transformers4Rec for all platforms contains a code injection vulnerability (CWE-94) that could allow an attacker with local access to inject and execute arbitrary code. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, and data tampering.

Critical Impact

This vulnerability enables code injection attacks that could result in arbitrary code execution, privilege escalation, unauthorized data access, and data integrity compromise on systems running NVIDIA Merlin Transformers4Rec.

Affected Products

• NVIDIA Merlin Transformers4Rec (all platforms)

Discovery Timeline

• 2026-01-20 - CVE-2025-33233 published to NVD
• 2026-01-20 - Last updated in NVD database

Technical Details for CVE-2025-33233

Vulnerability Analysis

This vulnerability is classified as a Code Injection flaw (CWE-94: Improper Control of Generation of Code). Code injection vulnerabilities occur when an application dynamically generates code using untrusted input without proper validation or sanitization. In the context of NVIDIA Merlin Transformers4Rec, a machine learning library for building transformer-based recommender systems, this vulnerability allows attackers to inject malicious code that gets executed within the application context.

The local attack vector indicates that exploitation requires the attacker to have some form of local access to the system, whether through an authenticated session, physical access, or through another compromised application running on the same host. The vulnerability requires low privileges to exploit and does not require user interaction, making it relatively straightforward to leverage once an attacker has initial access.

Successful exploitation can lead to multiple severe impacts: arbitrary code execution allows attackers to run malicious payloads; privilege escalation enables attackers to gain elevated system permissions; information disclosure permits unauthorized access to sensitive data processed by the ML pipeline; and data tampering allows modification of training data or model outputs.

Root Cause

The vulnerability stems from improper control of code generation (CWE-94). This typically occurs when user-controllable input is incorporated into dynamically generated code without adequate input validation, sanitization, or output encoding. In machine learning frameworks, this can manifest through unsafe deserialization of model files, improper handling of configuration files, or insufficient validation of user-provided code snippets used in data processing pipelines.

Attack Vector

The attack requires local access to the target system. An attacker with low-level privileges on a system running NVIDIA Merlin Transformers4Rec can craft malicious input designed to be interpreted as executable code. When processed by the vulnerable component, this injected code executes with the privileges of the application, potentially allowing the attacker to:

1. Execute arbitrary system commands
2. Escalate privileges to gain higher-level access
3. Access or exfiltrate sensitive training data and model information
4. Modify machine learning models or their outputs

The vulnerability's characteristics indicate that while network-based remote exploitation is not directly possible, any scenario where an attacker can influence input to the Transformers4Rec library could be leveraged for exploitation.

Detection Methods for CVE-2025-33233

Indicators of Compromise

• Unexpected child processes spawned by Python or Transformers4Rec-related processes
• Unusual file system access patterns from ML pipeline processes
• Anomalous network connections initiated by the Transformers4Rec application
• Modifications to model files or configuration files outside normal update windows

Detection Strategies

• Monitor process execution chains for suspicious activity originating from Transformers4Rec processes
• Implement application whitelisting to detect unauthorized code execution
• Deploy endpoint detection and response (EDR) solutions to identify code injection attempts
• Enable detailed logging for the Transformers4Rec application and analyze for anomalous behavior

Monitoring Recommendations

• Establish baseline behavior for Transformers4Rec processes and alert on deviations
• Monitor for privilege escalation attempts following Transformers4Rec process execution
• Implement file integrity monitoring on model files and configuration directories
• Review access logs for unusual authentication patterns on systems running affected software

How to Mitigate CVE-2025-33233

Immediate Actions Required

• Review the NVIDIA Support Article for official remediation guidance
• Inventory all systems running NVIDIA Merlin Transformers4Rec
• Restrict local access to systems running the vulnerable software to authorized personnel only
• Apply the principle of least privilege to accounts that interact with Transformers4Rec

Patch Information

NVIDIA has published security guidance for this vulnerability. Organizations should consult the NVIDIA Support Article for the latest patch information and update instructions. Apply vendor-provided patches as soon as they become available following your organization's change management procedures.

Additional technical details are available at the NVD CVE-2025-33233 Detail page.

Workarounds

• Isolate systems running Transformers4Rec in network segments with restricted access
• Implement strict input validation for any user-controllable data processed by the library
• Run Transformers4Rec processes with minimal required privileges using containerization or sandboxing
• Monitor and audit all local access to systems running the affected software

# Example: Run Transformers4Rec in a restricted container environment
# Limit capabilities and enforce read-only file systems where possible
docker run --rm \
  --read-only \
  --cap-drop=ALL \
  --security-opt=no-new-privileges:true \
  --user 1000:1000 \
  nvidia/transformers4rec:latest