CVE-2026-1669 Google CVE debrief

Who should care

Organizations using Keras for deep learning tasks should be aware of this vulnerability and take steps to mitigate it. This includes upgrading to a version of Keras that is not affected by the vulnerability and implementing additional security measures to prevent exploitation. Developers and security teams should prioritize patching and monitoring for potential exploitation.

Technical summary

The vulnerability is caused by a flaw in the model loading mechanism in Keras, specifically in the HDF5 integration. This allows remote attackers to read local files by providing a crafted .keras model file that utilizes HDF5 external dataset references. The vulnerability has a CVSS score of 7.1 and is considered high severity. The affected versions of Keras are 3.0.0 through 3.13.1. The vulnerability can be exploited via a network attack, and the attack complexity is low.

Defensive priority

High priority should be given to patching and mitigating this vulnerability, as it allows for remote code execution and disclosure of sensitive information. Organizations should prioritize upgrading to a non-affected version of Keras and implementing additional security measures to prevent exploitation.

Recommended defensive actions

• Upgrade to a version of Keras that is not affected by the vulnerability.
• Implement additional security measures to prevent exploitation, such as validating and sanitizing input files.
• Monitor for potential exploitation and implement incident response plans.
• Conduct a thorough review of the Keras library and its dependencies to ensure that they are up-to-date and secure.
• Consider implementing compensating controls, such as file access controls and monitoring.

Evidence notes

The vulnerability was published on February 11, 2026, and last modified on June 30, 2026. The CVSS score for this vulnerability is 7.1, indicating a high severity. The affected versions of Keras are 3.0.0 through 3.13.1. The vulnerability can be exploited via a network attack, and the attack complexity is low. There is no evidence of public exploitation or ransomware campaign use.

Sources and references

Methodology and review provenance

AI-assisted synthesis based on stored public vulnerability evidence. System validation, approval state, and publication status do not by themselves establish human review of this revision. PatchSiren helps prioritize defensive review and does not prove exposure or remediation on any system.