Remote code execution via unsafe eval
Published May 12, 2026 · Updated May 12, 2026
Code injection in Adversarial Robustness Toolbox 1.20.1 and earlier allows remote attackers to execute Python code through Kubeflow evaluations. The PyTorch robustness evaluation passes user-supplied LossFn and Optimizer strings directly to eval(), which interprets them as Python expressions without validation. Published assessments disagree on the gate: CISA rates it unauthenticated, while Red Hat requires low privileges; successful input runs with evaluator privileges.
Summary
What happened
Code injection in Adversarial Robustness Toolbox 1.20.1 and earlier allows remote attackers to execute Python code through Kubeflow evaluations. The PyTorch robustness evaluation passes user-supplied LossFn and Optimizer strings directly to eval(), which interprets them as Python expressions without validation. Published assessments disagree on the gate: CISA rates it unauthenticated, while Red Hat requires low privileges; successful input runs with evaluator privileges.
The record
- CVE
- CVE-2026-31228
- Published
- May 12, 2026
- Updated
- May 12, 2026
- Vendor
- Unknown vendor
- Product
- Unknown product
- Classifications
- CWE-94, T1059.006
- Attack vector
- network
- Privileges
- Unavailable
Timeline
How it unfolded
- May 12, 2026CVE publishedPublication date reported by the CVE source.
- May 12, 2026Record updatedLatest update available in the CVE record.
Exploitability
Present is not the same as exploitable
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Is a vulnerable build present?
What conditions does exploitation require?
What is affected?
Affected products and versions are unavailable in this record.
Published CVSS scores
CVSS describes severity. EPSS estimates exploitation probability.
Attacks
What attackers are doing with it
Daily unique IPs observed by Shadowserver honeypots for known exploited vulnerabilities (KEVs). Missing observations do not establish an absence of attacks.
Weakness, pattern, technique
Public exploit references
No public exploit references are available in this record.
Labels summarize the accepted research assessment. They do not indicate a test against your environment.
Technologies
Your stack
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Your stack
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