CVE-2020-26266: Uninitialized memory access in TensorFlow
Summary
CVE-2020-26266: Uninitialized memory access in TensorFlow. Affects tensorflow across 5 version ranges.
< 1.15.5
>= 2.0.0, < 2.0.4
>= 2.1.0, < 2.1.3
>= 2.2.0, < 2.2.2
>= 2.3.0, < 2.3.2
Fixed in 5 releases; the minimum fixed version depends on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
The same CVE-2020-26266 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 developed by Google. It provides a comprehensive ecosystem of tools, libraries, and community resources that enable researchers and developers to build and deploy machine learning models. TensorFlow is widely used in various domains, including computer vision, natural language processing, and deep learning. However, a vulnerability has been discovered in TensorFlow that can lead to uninitialized memory access during code execution.
Vulnerability Detail
The vulnerability in TensorFlow arises from the use of uninitialized values in certain cases when executing code. This occurs when tensor buffers are filled with the default value of the type but the quantized floating point types in Eigen are not properly default initialized. As a result, uninitialized memory can be accessed, leading to potential security risks. The impact of this vulnerability can vary depending on the specific use case, but it has the potential to cause data corruption, crashes, or even remote code execution.
How To Fix
Latest published version: 2.21.0 as of 22 Oct 2026
pip install --upgrade "tensorflow==2.21.0"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.
-
< 1.15.51.15.5pip install --upgrade "tensorflow==1.15.5" -
>= 2.0.0, < 2.0.42.0.4pip install --upgrade "tensorflow==2.0.4" -
>= 2.1.0, < 2.1.32.1.3pip install --upgrade "tensorflow==2.1.3" -
>= 2.2.0, < 2.2.22.2.2pip install --upgrade "tensorflow==2.2.2" -
>= 2.3.0, < 2.3.22.3.2pip install --upgrade "tensorflow==2.3.2"
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Workaround
There is no specific workaround provided for this vulnerability. Users are recommended to update to the patched version of TensorFlow as soon as possible.
Conclusion
The vulnerability in TensorFlow that allows uninitialized memory access has been addressed by the TensorFlow team. Users are advised to update their installations to the latest patched version to mitigate the risk. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open-source machine learning framework developed by Google that provides tools and libraries for building and deploying machine learning models.
The impact of this vulnerability can vary depending on the specific use case, but it has the potential to cause data corruption, crashes, or even remote code execution.
To fix this vulnerability, users should update their TensorFlow installations to the latest patched version.
No specific workaround is provided for this vulnerability. Updating to the patched version is the recommended solution.
Versions of TensorFlow before 2.4 are affected, including releases between 1.15 and 2.3.
References
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