Deserialization of Untrusted DataWeakness · CWE-502

CVE-2026-24141

HIGH · 7.8 CVSS v3.1 Published 2026-03-24
Mitigation only
No fix yet — a mitigation exists. There is no fixed release. A documented workaround reduces exposure in the meantime.
See remediation →
81/100
Remediation priority · High
No privileges

Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.

NVD · unedited
NVIDIA Model Optimizer for Windows and Linux contains a vulnerability in the ONNX quantization feature, where a user could cause unsafe deserialization by providing a specially crafted input file. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, data tampering, and information disclosure.

Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.

dbcve analysis · high confidence

NVIDIA Model Optimizer contains an unsafe deserialization vulnerability in its ONNX quantization feature. By providing a specially crafted malicious ONNX input file, an attacker can trigger unsafe deserialization, potentially achieving code execution, privilege escalation, data tampering, or information disclosure.

MitigationAvoid processing untrusted or unverified ONNX files with the quantization feature until an official patch is available. Implement strict input validation and consider sandboxing quantization operations.

Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.

CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.

From the vector
Attack vector
Local
Complexity
Low
Privileges
None
User interaction
Required
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.

dbcve checks

Work through these to decide whether this CVE applies to you.

  1. Check if NVIDIA Model Optimizer is installed
    Locate the model optimizer installation by searching for 'model_optimizer', 'mo', or 'optimizer' in common Python package locations. Run: pip list | grep -i model_optimizer or pip show nvidia-model-optimizer
    Affected if NVIDIA Model Optimizer package is not found in the environment
  2. Determine the installed version of NVIDIA Model Optimizer
    Run: pip show nvidia-model-optimizer or python -c 'import nvidia.model_optimizer; print(nvidia.model_optimizer.__version__)'
    Affected if The installed version falls within any affected version range or cannot be determined (treat unknown versions as potentially vulnerable)
  3. Verify if ONNX quantization feature is in use
    Search project code, scripts, or configuration files for references to 'quantization', 'onnx quantization', or calls to quantization-related functions or commands. Inspect any pipeline configuration that loads or processes ONNX files.
    Affected if ONNX quantization is invoked or configured to process ONNX input files
  4. Inspect input handling for ONNX files
    Review how ONNX files are loaded in the quantization workflow. Check if there is validation on ONNX files before they are passed to the quantization feature.
    Affected if ONNX files from untrusted sources are processed without validation or verification steps

The environment is affected if NVIDIA Model Optimizer is installed, the ONNX quantization feature is used, and untrusted or unverified ONNX files can be processed by that feature.

Generated from the published advisory. Verify against your own configuration.

Check your environment

Paste your version and any relevant configuration and it will be compared against the affected criteria above. Do not include secrets or credentials.

AI-assisted, checked against the advisory. Informational, not a guarantee.

Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.

From vendor data
Mitigation available No clean upgrade yet — mitigate in the meantime
Mitigation

Avoid processing untrusted or unverified ONNX files with the quantization feature until an official patch is available. Implement strict input validation and consider sandboxing quantization operations.

Have this fixed Scoped from the published advisory
  • Consultation6.0 h
  • Implementation12.0 h
  • Testing8.0 h
  • Review / QA4.0 h
30.0 hours of engineering $5,280
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References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.

Primary sources

Practitioner notes

Contributed

Peer-ranked notes from engineers who’ve handled CVE-2026-24141 in production — separate from our analysis above.

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What this is

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What belongs here
  • Verified mitigations, workarounds, and config changes
  • Version or environment caveats, and links to real fixes
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