Vulnerability Monitor

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CVE-2025-9905


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.


Security Impact Summary

This vulnerability carries a HIGH severity rating with a CVSS v3.1 score of 7.3, requiring local system access to exploit with relatively low complexity though user interaction is required requiring only low-level privileges . The vulnerability impacts confidentiality (data exposure), integrity (unauthorized modifications), and availability (service disruption) for affected systems. Impacting 1 product from keras organizations running these solutions should prioritize assessment and patching.

Historical Context

Reported in 2025, this vulnerability emerged during an era marked by increased sophistication in supply chain attacks, cloud infrastructure vulnerabilities, and software-as-a-service (SaaS) security challenges. Security practices during this period emphasized zero-trust architectures, container security, and API protection.


Published

2025-09-19T09:15:36.033

Last Modified

2026-06-17T10:10:00.037

Status

Analyzed

Source

[email protected]

Severity

CVSSv3.1: 7.3 (HIGH)

Weaknesses
  • Type: Secondary
    CWE-913

Affected Vendors & Products
Type Vendor Product Version/Range Vulnerable?
Application keras keras < 3.11.3 Yes

References

How SecUtils Interprets This CVE

SecUtils normalizes and enriches National Vulnerability Database (NVD) records by standardizing vendor and product identifiers, aggregating vulnerability metadata from both NVD and MITRE sources, and providing structured context for security teams. For keras's affected products, we extract Common Platform Enumeration (CPE) data, Common Weakness Enumeration (CWE) classifications, CVSS severity metrics, and reference data to enable rapid vulnerability prioritization and asset correlation. This record contains no exploit code, proof-of-concept instructions, or attack methodologies—only defensive intelligence necessary for patch management, risk assessment, and security operations.