CVE-2023-25673: Floating Point Exception in TensorListSplit with XLA in TensorFlow

tensorflow Updated 02 Oct 2025

Summary

06/03/2024

Disclosed on

CVE-2023-25673

CVE

Yes i

Fix available

High

Severity

Not published

CVSS Score

CVE-2023-25673: TensorFlow has Floating Point Exception in TensorListSplit with XLA. Affects tensorflow versions < 2.12.0. Fixed in 2.12.0. How to fix ↓

Also tracked for

tensorflow · PyPI
tensorflow-cpu · PyPI
tensorflow-gpu · PyPI
tensorflow · PyPI

The same CVE-2023-25673 is tracked separately for each ecosystem and vendor. Its fix information is the same; the package and affected versions differ.

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Background

TensorFlow is an open source platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models. TensorFlow is widely used in various domains, including research, industry, and academia. It enables developers to build and train machine learning models efficiently and effectively. However, versions prior to 2.12.0 and 2.11.1 of TensorFlow are vulnerable to a Floating Point Exception in TensorListSplit with XLA.

Vulnerability Detail

The vulnerability in TensorFlow arises from a Floating Point Exception in the TensorListSplit function when used with XLA (Accelerated Linear Algebra). This vulnerability can be exploited by an attacker to cause a denial of service (DoS) condition or potentially execute arbitrary code. The issue has a high severity rating (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H) and can impact systems running vulnerable versions of TensorFlow.

How To Fix

Minimum fixed version

2.12.0

This is the first release that contains the fix.

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Workaround

There is no known workaround for this vulnerability. It is strongly advised to upgrade to the fixed versions of TensorFlow as mentioned in the 'how_to_fix' section.

Conclusion

The vulnerability in TensorFlow can be mitigated by upgrading to the fixed versions. It is crucial to promptly update TensorFlow installations to ensure the security and stability of machine learning systems. For further support and assistance in vulnerability remediation, please contact DevNack.com.

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