CWE-88Weakness · CWE-88

CVE-2026-31230

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 command-line argument injection vulnerability in its Kubeflow component (robustness_evaluation_fgsm_pytorch.py). The script uses the unsafe eval() function to parse string values provided via the --clip_values and --input_shape command-line arguments. This allows an attacker to inject arbitrary Python code into these arguments, which will be executed when eval() is called. The vulnerability can be exploited remotely if an attacker can control these arguments (e.g., through pipeline configuration or automated scripts), leading to arbitrary code execution on 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) Kubeflow component uses the unsafe eval() function to parse command-line arguments --clip_values and --input_shape. An attacker who can control these arguments (e.g., through pipeline configuration or automated scripts) can inject arbitrary Python code that will be executed on the target system with the privileges of the running process.

MitigationReplace the unsafe eval() calls with safe parsing methods such as ast.literal_eval() for tuple/numeric values or implement strict input validation with allowlist-based type checking to prevent code injection.

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. Locate the affected file
    Search for robustness_evaluation_fgsm_pytorch.py in your environment, typically found within ART (Adversarial Robustness Toolbox) installations used in Kubeflow pipelines
    Affected if The file exists in your Kubeflow or ML workflow environment
  2. Verify eval() usage for argument parsing
    Open the file and search for eval() calls that parse command-line arguments, specifically looking for lines handling --clip_values and --input_shape
    Affected if eval() is used to parse --clip_values or --input_shape arguments from the command line
  3. Identify if running as part of Kubeflow
    Check if your ART-based robustness evaluation script runs as a Kubeflow pipeline component or is invoked within a Kubeflow container/pod
    Affected if The vulnerable script runs in a Kubeflow context with user-accessible command-line arguments
  4. Confirm external argument exposure
    Review how the script is invoked - check if --clip_values and --input_shape arguments can be supplied by users, external pipelines, or untrusted sources
    Affected if Users or external systems can supply --clip_values or --input_shape to the script

You are affected if the file robustness_evaluation_fgsm_pytorch.py exists in your environment and uses eval() to parse --clip_values or --input_shape arguments that can be supplied by users or external sources.

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

Replace the unsafe eval() calls with safe parsing methods such as ast.literal_eval() for tuple/numeric values or implement strict input validation with allowlist-based type checking to prevent code injection.

Have this fixed Scoped from the published advisory
  • Consultation2.0 h
  • Implementation2.0 h
  • Testing3.0 h
  • Review / QA1.0 h
8.0 hours of engineering $1,390
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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-2026-31230 in production — separate from our analysis above.

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