CVE-2026-1462: Safe Mode Bypass in keras-team/keras

Published Apr 13, 2026
·
Updated

A vulnerability in the TFSMLayer class of the keras package, version 3.13.0, allows attacker-controlled TensorFlow SavedModels to be loaded during deserialization of .keras models, even when safemode=True. This bypasses the security guarantees of safemode and enables arbitrary attacker-controlled code execution during model inference under the victim's privileges. The issue arises due to the unconditional loading of external SavedModels, serialization of attacker-controlled file paths, and the lack of validation in the fromconfig() method.

Affected Software

3 affected components
pypi/keras=3.13.0
Keras Keras=3.13.0
redhat OpenShift AI>=2.25<2.25.7

Remediation

Recommended actions to resolve this vulnerability, in priority order.

  1. Upgrade

    Upgrade keras to a version that resolves this vulnerability.

    Fixed in 3.13.0

Event History

Apr 13, 2026
CVE Published
via MITRE·02:55 PM
Data Sourced
via MITRE·02:55 PM
DescriptionSeverityWeakness
Data Sourced
via Red Hat·03:02 PM
DescriptionSeverityAffected Software
Data Sourced
via NVD·03:17 PM
RemedyDescriptionSeverityWeaknessAffected Software
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Frequently Asked Questions

1

What is the severity of CVE-2026-1462?

CVE-2026-1462 has a high severity rating of 7.8 according to the CVSS 3.1 scoring system.

2

How do I fix CVE-2026-1462?

To mitigate CVE-2026-1462, it is recommended to upgrade to a patched version of the keras package where the vulnerability is resolved.

3

What impacts does CVE-2026-1462 have on users?

CVE-2026-1462 allows attackers to bypass the safe mode settings and potentially execute malicious code through attacker-controlled TensorFlow SavedModels.

4

Which version of keras is affected by CVE-2026-1462?

The vulnerability exists in version 3.13.0 of the keras package.

5

Does CVE-2026-1462 affect other platforms besides keras?

While CVE-2026-1462 specifically affects the keras package, it may also impact applications that rely on the keras package for their machine learning models.

Contact

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