CVE-2026-24141: NVIDIA Model Optimizer RCE Vulnerability

CVE-2026-24141 Overview

NVIDIA Model Optimizer for Windows and Linux contains an unsafe deserialization vulnerability in the ONNX quantization feature. An attacker could exploit this flaw by providing a specially crafted input file, leading to arbitrary code execution, privilege escalation, data tampering, and information disclosure. This vulnerability affects AI/ML workflows that rely on NVIDIA's Model Optimizer for neural network quantization and optimization tasks.

Critical Impact

Successful exploitation enables arbitrary code execution with the privileges of the user running Model Optimizer, potentially compromising machine learning pipelines and sensitive model data.

Affected Products

• NVIDIA Model Optimizer for Windows
• NVIDIA Model Optimizer for Linux
• Systems utilizing ONNX quantization features in Model Optimizer

Discovery Timeline

• 2026-03-24 - CVE-2026-24141 published to NVD
• 2026-03-25 - Last updated in NVD database

Technical Details for CVE-2026-24141

Vulnerability Analysis

This vulnerability stems from CWE-502 (Deserialization of Untrusted Data), a class of security flaws where applications deserialize data from untrusted sources without adequate validation. In the context of NVIDIA Model Optimizer, the ONNX quantization feature processes model files that may contain serialized objects. When a malicious actor crafts an ONNX file with embedded malicious payloads, the deserialization process can instantiate arbitrary objects, execute malicious code, or manipulate application state.

The local attack vector requires user interaction—specifically, the victim must open or process the malicious file. However, once this condition is met, the attacker gains significant control without requiring authentication or elevated privileges to initiate the attack.

Root Cause

The root cause lies in insufficient validation during the deserialization of ONNX model files within the quantization workflow. When Model Optimizer parses input files to perform quantization operations, it deserializes embedded data structures without properly sanitizing or restricting the types of objects that can be instantiated. This allows attackers to embed arbitrary serialized objects that execute malicious code during the deserialization process.

Attack Vector

The attack requires local access and user interaction. An attacker would craft a malicious ONNX file containing a specially constructed serialized payload. The attack scenario typically involves:

1. Attacker creates a malicious ONNX file with embedded deserialization payload
2. The file is delivered to the victim through social engineering (email attachment, compromised model repository, supply chain attack)
3. Victim processes the file using NVIDIA Model Optimizer's quantization feature
4. The deserialization process triggers execution of the embedded malicious code
5. Attacker achieves code execution with the victim's privileges

The vulnerability is particularly concerning in ML/AI development environments where model files are frequently shared and processed from various sources, including public model repositories.

Detection Methods for CVE-2026-24141

Indicators of Compromise

• Unexpected process spawning from NVIDIA Model Optimizer processes
• Anomalous network connections originating from ML optimization workflows
• Unusual file system modifications during model quantization operations
• Suspicious ONNX files with abnormal structures or embedded executable content

Detection Strategies

• Monitor for unexpected child processes spawned by Model Optimizer binaries
• Implement file integrity monitoring on systems running NVIDIA Model Optimizer
• Deploy behavioral analysis to detect anomalous activity during model processing tasks
• Scan incoming ONNX files for known malicious serialization patterns before processing

Monitoring Recommendations

• Enable detailed logging for all Model Optimizer operations and quantization tasks
• Monitor for privilege escalation attempts following model file processing
• Implement network segmentation for ML development environments to limit lateral movement
• Track file access patterns to identify processing of files from untrusted sources

How to Mitigate CVE-2026-24141

Immediate Actions Required

• Review and apply patches from NVIDIA as soon as available via the NVIDIA Support Advisory
• Restrict Model Optimizer usage to trusted, verified ONNX model files only
• Implement application whitelisting to control which processes can execute on ML development systems
• Run Model Optimizer in isolated environments (containers, VMs) with limited privileges

Patch Information

Organizations should consult the official NVIDIA Support Advisory for specific patch information and updated versions of Model Optimizer that address this vulnerability. Apply vendor-provided updates following your organization's change management procedures. Additional technical details are available at the NVD CVE-2026-24141 Detail page.

Workarounds

• Process ONNX files only from trusted and verified sources with established provenance
• Implement strict access controls limiting who can submit model files for processing
• Use sandboxed or containerized environments when processing model files from less-trusted sources
• Consider implementing input validation layers that scan ONNX files before processing

# Example: Run Model Optimizer in a restricted container environment
# This limits potential impact of exploitation

docker run --rm \
  --user $(id -u):$(id -g) \
  --read-only \
  --network none \
  --cap-drop ALL \
  -v /path/to/trusted/models:/models:ro \
  -v /path/to/output:/output:rw \
  nvidia-model-optimizer:latest \
  --input /models/trusted_model.onnx \
  --output /output/quantized_model.onnx