dbcve.org
Code InjectionWeakness · CWE-94

CVE-2026-31228

CRITICAL · 9.8 CVSS v3.1 Published 2026-05-12
Mitigation only
No fix yet — a mitigation exists. There is no fixed release. A documented workaround reduces exposure in the meantime.
See remediation →
100/100
Remediation priority · Urgent
Remotely reachable No privileges Zero-click

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
The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains a remote code execution vulnerability in its Kubeflow component. The robustness evaluation function for PyTorch models uses the unsafe eval() function to dynamically evaluate user-supplied strings for the LossFn and Optimizer parameters without any sanitization or security restrictions. An attacker can exploit this by providing a specially crafted string that contains arbitrary Python code, which will be executed when eval() is called, leading to complete compromise of the system running the ART evaluation.

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

The Adversarial Robustness Toolbox (ART) through 1.20.1 has a critical RCE vulnerability in its Kubeflow component where the PyTorch model robustness evaluation function uses unsafe eval() to process user-supplied LossFn and Optimizer parameters without any sanitization. Attackers can inject arbitrary Python code via these parameters for immediate execution, achieving full system compromise.

MitigationReplace the unsafe eval() calls with a secure expression evaluator (such as Python's ast.literal_eval for literal values, a restricted expression parser, or allowlisting pre-defined loss/optimizer classes) and implement strict input validation before evaluation.

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
Network
Complexity
Low
Privileges
None
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/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. Identify if ART is installed
    Run 'pip show adversarial-robustness-toolbox' or check your requirements/dependencies for the adversarial-robustness-toolbox package
    Affected if The package is not installed or not present in dependencies
  2. Check ART version
    Run 'pip show adversarial-robustness-toolbox' and note the Version field, or import art and print art.__version__
    Affected if The installed version is 1.20.1 or earlier (any version up to and including 1.20.1)
  3. Verify Kubeflow component is in use
    Search your codebase for imports from art.toolbox.kubeflow, or check if your project uses ART's Kubeflow-related functions for PyTorch model evaluation
    Affected if Your environment imports or uses any Kubeflow-related module from ART for model evaluation
  4. Identify if LossFn or Optimizer parameters accept user input
    Review your code that invokes ART's PyTorch model evaluation functions (such as for robustness assessment) and inspect how LossFn and Optimizer arguments are populated
    Affected if LossFn or Optimizer parameters are populated from user-provided data, external APIs, or untrusted input sources without sanitization

You are affected if you use ART version 1.20.1 or earlier, utilize the Kubeflow component for PyTorch model evaluation, and pass user-supplied or untrusted input to the LossFn or Optimizer parameters.

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.

dbcve · scoped
Mitigation available No clean upgrade yet — mitigate in the meantime
Mitigation

Replace the unsafe eval() calls with a secure expression evaluator (such as Python's ast.literal_eval for literal values, a restricted expression parser, or allowlisting pre-defined loss/optimizer classes) and implement strict input validation before evaluation.

Recommended fix Moderate confidence

ART version > 1.20.1 (latest stable release)

  1. Identify the current version of the Adversarial Robustness Toolbox (ART) installed in your environment using pip show adversarial-robustness-toolbox or your package manager
  2. Upgrade to the latest version of ART available on PyPI or the official GitHub repository: pip install --upgrade adversarial-robustness-toolbox
  3. If a fixed version is specifically mentioned in ART's release notes or security advisory, upgrade to that version instead (version > 1.20.1)
  4. After upgrading, verify the upgrade was successful by checking the new version: pip show adversarial-robustness-toolbox
  5. Test your pipeline to ensure the evaluation functionality still works correctly with the updated library
  6. If using the Kubeflow component, ensure any serialized artifacts or pipelines that passed unsafe strings to LossFn or Optimizer parameters are reviewed and updated to use safe alternatives
Caveat Minor or patch version upgrades should be low risk; verify compatibility with your PyTorch and Kubeflow versions before upgrading in production environments

Generated from the published advisory — verify against the referenced sources before acting.

Have this fixed Scoped from the published advisory
  • Consultation4.0 h
  • Implementation8.0 h
  • Testing6.0 h
  • Review / QA4.0 h
22.0 hours of engineering $3,860
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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-2026-31228 in production — separate from our analysis above.

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What this is

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What belongs here
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  • Version or environment caveats, and links to real fixes
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