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Release of RHOAI 2.25.1 provides these changes:

The vulnerability CVE-2025-9905 is a path traversal flaw in the keras Python library when used with the extract=True option for tar archives. The issue arises because the tarfile.extractall function is used without proper filtering, allowing specially crafted tar archives with malicious symlinks to write files outside the intended extraction directory. This can enable an attacker to overwrite or create critical files, potentially leading to unauthorized code execution, data disclosure, or denial of service. Red Hat OpenShift AI versions >=2.25.0 and <2.25.1 are affected. Red Hat has released OpenShift AI 2.25.1 with fixes. The vulnerability is rated high severity by Red Hat, with a CVSS v3 base score of 8.3 according to Red Hat's assessment. No exploits are currently known in the wild.

Detailed information about Red Hat Security Advisory: RHOAI 2.25.1 - Red Hat OpenShift AI affecting Red Hat Product Security Red Hat. Get real-time updates, tec

Red Hat Security Advisory: RHOAI 2.25.1 - Red Hat OpenShift AI - Live Threat Intelligence - Threat Radar | OffSeq.com

https://access.redhat.com/errata/RHSA-2025:23531

https://access.redhat.com/security/cve/CVE-2025-12060

https://access.redhat.com/security/cve/CVE-2025-12638

https://access.redhat.com/security/cve/CVE-2025-47913

https://access.redhat.com/security/cve/CVE-2025-49655

https://access.redhat.com/security/cve/CVE-2025-53643

https://access.redhat.com/security/cve/CVE-2025-62164

https://access.redhat.com/security/cve/CVE-2025-62593

https://access.redhat.com/security/cve/CVE-2025-62727

https://access.redhat.com/security/cve/CVE-2025-64756

https://access.redhat.com/security/cve/CVE-2025-66416

https://access.redhat.com/security/cve/CVE-2025-9905

https://access.redhat.com/security/cve/CVE-2025-9906

https://access.redhat.com/security/updates/classification/

https://docs.redhat.com/en/documentation/red_hat_openshift_ai/

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Red Hat Security Advisory: RHOAI 2.25.1 - Red Hat OpenShift AI

Release of RHOAI 2.22.3 provides these changes:

The vulnerability CVE-2025-9905 in Red Hat OpenShift AI (RHOAI) versions >=2.22.0 <2.22.3 is a path traversal flaw originating from the keras Python library's use of tarfile.extractall without proper filtering. An attacker can craft malicious tar archives containing special symlinks that, when extracted with the extract=True option, allow writing arbitrary files outside the intended directory. This can lead to unauthorized code execution, file modification, or data disclosure. The issue is tracked under multiple CWEs including CWE-22 (Path Traversal) and CWE-913 (Improper Control of Dynamically-Managed Code Resources). Red Hat has released RHOAI 2.22.3 with updated images to mitigate this vulnerability. The vendor advisory notes no known exploits in the wild and provides upgrade instructions.

Detailed information about Red Hat Security Advisory: RHOAI 2.22.3 - Red Hat OpenShift AI affecting Red Hat Product Security Red Hat. Get real-time updates, tec

Red Hat Security Advisory: RHOAI 2.22.3 - Red Hat OpenShift AI - Live Threat Intelligence - Threat Radar | OffSeq.com

https://access.redhat.com/errata/RHSA-2025:22759

https://access.redhat.com/security/cve/CVE-2025-47907

https://access.redhat.com/security/cve/CVE-2025-58183

https://access.redhat.com/security/cve/CVE-2025-58754

https://access.redhat.com/security/cve/CVE-2025-62156

Red Hat Security Advisory: RHOAI 2.22.3 - Red Hat OpenShift AI

The Keras Model.load_model method can be exploited to achieve arbitrary code execution, even with safe_mode=True.

One can create a specially crafted .h5/.hdf5 model archive that, when loaded via Model.load_model, will trigger arbitrary code to be executed.

This is achieved by crafting a special .h5 archive file that uses the Lambda layer feature of keras which allows arbitrary Python code in the form of pickled code. The vulnerability comes from the fact that the safe_mode=True option is not honored when reading .h5 archives.

Note that the .h5/.hdf5 format is a legacy format supported by Keras 3 for backwards compatibility.

CVE-2025-9905 is a vulnerability classified under CWE-913 (Improper Control of Dynamically-Managed Code Resources) affecting the Keras deep learning framework, specifically version 3.0.0. The issue arises in the Model.load_model method when loading legacy .h5 or .hdf5 model files, which are archives that can contain Keras model architectures and weights. The vulnerability exploits the Lambda layer feature in Keras, which permits embedding arbitrary Python code serialized via pickling. An attacker can craft a malicious .h5 archive containing a Lambda layer with pickled code that executes arbitrary commands upon loading. Although Keras 3 introduced a safe_mode=True parameter intended to restrict such code execution, this option is not enforced when loading legacy .h5 files, allowing the malicious payload to run. Exploitation requires the victim to load the crafted model file, which implies local access or user interaction. The vulnerability can lead to arbitrary code execution with limited privileges, potentially compromising the host system's confidentiality, integrity, and availability. The CVSS 4.0 score of 7.3 reflects high severity, considering the attack vector is local, requires high attack complexity, partial privileges, and user interaction, but results in high impact across all security properties. No patches or known exploits are currently reported, but the vulnerability poses a significant risk to organizations using Keras 3.0.0 with legacy model files, especially in environments where models from untrusted sources might be loaded. This issue underscores the risks of deserializing untrusted data and the challenges of maintaining backward compatibility with legacy formats.

Detailed information about CVE-2025-9905: CWE-913 Improper Control of Dynamically-Managed Code Resources in Keras-team Keras affecting Keras-team Keras. Get rea

CVE-2025-9905: CWE-913 Improper Control of Dynamically-Managed Code Resources in Keras-team Keras - Live Threat Intelligence - Threat Radar | OffSeq.com

CVE-2025-9905: CWE-913 Improper Control of Dynamically-Managed Code Resources in Keras-team Keras


Red Hat Security Advisory: RHOAI 2.25.1 - Red Hat OpenShift AICVE-2025-9905 0 Release of RHOAI 2.25.1 provides these changes: HighVulnerability#gcve #csaf #cve-2025-9905 Join the discussion	GCVE Database	12/17/2025, 15:17:16 UTC Added: 05/27/2026, 21:15:07 UTC
Red Hat Security Advisory: RHOAI 2.22.3 - Red Hat OpenShift AICVE-2025-9905 0 Release of RHOAI 2.22.3 provides these changes: HighVulnerability#gcve #csaf #cve-2025-9905 Join the discussion	GCVE Database	12/04/2025, 13:06:08 UTC Added: 05/27/2026, 21:14:59 UTC
CVE-2025-9905: CWE-913 Improper Control of Dynamically-Managed Code Resources in Keras-team KerasCVE-2025-9905 0 The Keras Model.load_model method can be exploited to achieve arbitrary code execution, even with safe_mode=True. One can create a specially crafted .h5/.hdf5 model archive that, when loaded via Model.load_model, will trigger arbitrary code to be executed. This is achieved by crafting a special .h5 archive file that uses the Lambda layer feature of keras which allows arbitrary Python code in the form of pickled code. The vulnerability comes from the fact that the safe_mode=True option is not honored when reading .h5 archives. Note that the .h5/.hdf5 format is a legacy format supported by Keras 3 for backwards compatibility. HighVulnerabilityUnited StatesChinaGermany+9#cve #cve-2025-9905 #cwe-913 Join the discussion	CVE Database V5	09/19/2025, 08:16:44 UTC Added: 09/19/2025, 08:21:17 UTC

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