CVE-2023-25670: TensorFlow Null Pointer Error in QuantizedMatMulWithBiasAndDequantize

tensorflow Updated 02 Oct 2025

Summary

06/03/2024
Yes i
High
Not published

CVE-2023-25670: TensorFlow has Null Pointer Error in QuantizedMatMulWithBiasAndDequantize. Affects tensorflow versions < 2.12.0. Fixed in 2.12.0. How to fix ↓

Also tracked for

The same CVE-2023-25670 is tracked separately for each ecosystem and vendor. Its fix information is the same; the package and affected versions differ.

Free scan

Check if this vulnerability affects your application

Upload a manifest or SBOM and we'll show whether you're affected, plus every other risky package in the same scan.

Check my app free

No signup required · Results in about 60 seconds

Sponsored Lume, the Habilume companion Habilume Habit tracker with a companion that grows with you Complete habits, earn Lume Points, and watch your Lume grow as you stay consistent. App Store Google Play

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 enables developers to build and train neural networks, perform data preprocessing, and deploy models in various environments. This vulnerability affects versions prior to 2.12.0 and 2.11.1 of TensorFlow.

Vulnerability Detail

The vulnerability in TensorFlow is a null pointer error in the QuantizedMatMulWithBiasAndDequantize function when using MKL (Math Kernel Library) enabled. This error can be exploited by an attacker to cause a denial of service (DoS) condition or potentially execute arbitrary code. The vulnerability is present in versions prior to 2.12.0 and 2.11.1. A fix has been included in TensorFlow version 2.12.0 and version 2.11.1.

How To Fix

Minimum fixed version

2.12.0

This is the first release that contains the fix.

Tip — Not sure which version your app runs? Vulert can match this fix to it. Scan free →

Workaround

There is no known workaround for this vulnerability. It is recommended to update to the fixed versions of TensorFlow as mentioned above.

Conclusion

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

See every vulnerability recorded for tensorflow →

Share this vulnerability

FAQ

TensorFlow is an open source platform for machine learning that provides tools and resources for developing and deploying machine learning models.

This vulnerability can lead to a denial of service (DoS) condition or potential execution of arbitrary code.

Versions prior to 2.12.0 and 2.11.1 of TensorFlow are affected by this vulnerability.

To fix this vulnerability, update TensorFlow to version 2.12.0 or version 2.11.1.

There is no known workaround for this vulnerability. Updating to the fixed versions is recommended.

Related vulnerabilities

Recent vulnerabilities recorded for tensorflow.

Sponsored Lume, the Habilume companion Habilume Habit tracker with a companion that grows with you Complete habits, earn Lume Points, and watch your Lume grow as you stay consistent. App Store Google Play

How To Fix

Minimum fixed version

2.12.0

This is the first release that contains the fix.

Tip — Not sure which version your app runs? Vulert can match this fix to it. Scan free →

Subscribe to Vulert on Telegram
Daily digest of new CVEs, critical and high severity, affected packages and fixes — plus a weekly roundup.
Join the channel →
Help improve this record
Report an issue or send feedback