CVE-2022-23557: Division by Zero Vulnerability in tensorflow-gpu
Summary
CVE-2022-23557: Tensorflow is an Open Source Machine Learning Framework. Affects tensorflow-gpu across 3 version ranges.
< 2.5.3
>= 2.6.0, < 2.6.3
< 8c6f391a2282684a25cbfec7687bd5d35261a209
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-2022-23557 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 that provides a wide range of tools and libraries for building and deploying machine learning models. It is widely used in various domains, including computer vision, natural language processing, and deep learning. The tensorflow-gpu package is a PyPI distribution of TensorFlow specifically optimized for GPU acceleration. It allows users to leverage the power of GPUs to accelerate their machine learning workflows.
Vulnerability Detail
This vulnerability (CVE-2022-23557) exists in the tensorflow-gpu package and is caused by a lack of validation in the BiasAndClamp implementation. An attacker can exploit this vulnerability by crafting a TFLite model that triggers a division by zero when the bias_size parameter is zero. The lack of a check for non-zero bias_size allows the attacker to cause a division by zero error, potentially leading to a denial of service (DoS) or other unexpected behavior. The vulnerability affects versions 0.0.0 to 2.5.3, 2.6.0 to 2.6.3, and 2.7.1 of tensorflow-gpu.
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"
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Workaround
As a temporary workaround, users can avoid the vulnerability by ensuring that the bias_size parameter in the TFLite model is always set to a non-zero value. This can be achieved by validating the input data and ensuring that the bias_size parameter is properly initialized before passing it to the BiasAndClamp implementation.
Conclusion
In conclusion, the tensorflow-gpu package is vulnerable to a division by zero vulnerability in the BiasAndClamp implementation. This vulnerability can be exploited by crafting a malicious TFLite model. The TensorFlow team has addressed this vulnerability in the upcoming release and has also backported the fix to older versions. Users are advised to upgrade their tensorflow-gpu package to a fixed version to mitigate the risk. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow is an open-source machine learning framework that provides tools and libraries for building and deploying machine learning models.
The tensorflow-gpu package is a PyPI distribution of TensorFlow specifically optimized for GPU acceleration.
This vulnerability can be exploited by an attacker to trigger a division by zero error, potentially leading to a denial of service or other unexpected behavior.
Versions 0.0.0 to 2.5.3, 2.6.0 to 2.6.3, and 2.7.1 of tensorflow-gpu are affected by this vulnerability.
To fix this vulnerability, users are advised to upgrade their tensorflow-gpu package to one of the fixed versions: TensorFlow 2.8.0, 2.7.1, 2.6.3, or 2.5.3.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-gpu.
