CVE-2022-21739: TensorFlow GPU - QuantizedMaxPool Null Pointer Reference Binding
Summary
CVE-2022-21739: Tensorflow is an Open Source Machine Learning Framework. Affects tensorflow-gpu across 3 version ranges.
< 2.5.3
>= 2.6.0, < 2.6.3
< 53b0dd6dc5957652f35964af16b892ec9af4a559
Fixed in 2.5.3 or 2.6.3, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow · PyPI
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow-cpu · PyPI
The same CVE-2022-21739 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 machine learning framework widely used for developing and training machine learning models. It provides a comprehensive set of tools and libraries for building and deploying machine learning applications. TensorFlow GPU is a variant of TensorFlow that leverages GPU acceleration for faster computation.
Vulnerability Detail
The vulnerability (CVE-2022-21739) exists in the implementation of QuantizedMaxPool in TensorFlow GPU. It allows user-controlled inputs to trigger a reference binding to a null pointer, leading to undefined behavior. This vulnerability can be exploited by an attacker to cause a denial-of-service (DoS) condition or potentially execute arbitrary code.
How To Fix
Latest published version: 2.12.0 as of 22 Oct 2026
pip install --upgrade "tensorflow-gpu==2.12.0"Upgrading to it may include breaking changes; the versions below are the minimum fixed release for this vulnerability.
If you can't move to the latest
The minimum fixed version depends on the release line you run.
-
< 2.5.32.5.3pip install --upgrade "tensorflow-gpu==2.5.3" -
>= 2.6.0, < 2.6.32.6.3pip install --upgrade "tensorflow-gpu==2.6.3"
Tip — Not sure which release line you're on? Vulert can find the fix for the version you are using. Scan free →
Workaround
Currently, there is no known workaround for this vulnerability. It is recommended to apply the official fix or update to the patched versions of TensorFlow GPU.
Conclusion
The vulnerability (CVE-2022-21739) in TensorFlow GPU's QuantizedMaxPool implementation poses a risk of DoS or arbitrary code execution. Users should update their installations to the fixed versions or apply the official patches. For further assistance or support in vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow is an open-source machine learning framework used for developing and training machine learning models. It provides a wide range of tools and libraries for building and deploying machine learning applications.
TensorFlow GPU is a variant of TensorFlow that utilizes GPU acceleration for faster computation. It is specifically designed to leverage the power of GPUs in machine learning tasks.
This vulnerability in TensorFlow GPU can lead to a denial-of-service (DoS) condition or potentially allow an attacker to execute arbitrary code. It is important to update to the patched versions to mitigate the risk.
No, only TensorFlow GPU versions 2.5.3, 2.6.3, 2.7.1, and earlier are affected. The vulnerability has been fixed in TensorFlow 2.8.0 and will be backported to the affected versions.
Currently, there is no known workaround for this vulnerability. It is recommended to update to the patched versions of TensorFlow GPU as soon as they are available.
References
Related vulnerabilities
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