CVE-2021-29590: TensorFlow-GPU - Out-of-Bounds Read in Minimum and Maximum TFLite Operators
Summary
CVE-2021-29590: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow-gpu across 5 version ranges.
< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
< 953f28dca13c92839ba389c055587cfe6c723578
Fixed in 4 releases; the minimum fixed version depends on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow · PyPI
- tensorflow-cpu · PyPI
The same CVE-2021-29590 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 end-to-end open source platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and community resources that enable researchers and developers to build and deploy machine learning models. TensorFlow-GPU is a variant of TensorFlow that utilizes GPU acceleration for faster computation. It is widely used in various industries and research domains for training and inference of machine learning models.
Vulnerability Detail
The Minimum and Maximum TFLite operators in TensorFlow-GPU are susceptible to an out-of-bounds read vulnerability. When either of the two input tensor arguments is empty, the broadcasting implementation of these operators allows reading data outside the bounds of heap allocated objects. This occurs because the implementation indexes into both tensors with the same index but fails to validate whether the index is within bounds. As a result, an attacker could potentially exploit this vulnerability to read sensitive data or cause a denial-of-service condition.
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.
-
< 2.1.42.1.4pip install --upgrade "tensorflow-gpu==2.1.4" -
>= 2.2.0, < 2.2.32.2.3pip install --upgrade "tensorflow-gpu==2.2.3" -
>= 2.3.0, < 2.3.32.3.3pip install --upgrade "tensorflow-gpu==2.3.3" -
>= 2.4.0, < 2.4.22.4.2pip install --upgrade "tensorflow-gpu==2.4.2"
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Workaround
There is no known workaround for this vulnerability. It is recommended to apply the fix by updating to the patched versions of TensorFlow-GPU.
Conclusion
The Minimum and Maximum TFLite operators in TensorFlow-GPU are vulnerable to an out-of-bounds read issue when either of the input tensors is empty. This could potentially lead to unauthorized data access or denial-of-service attacks. It is crucial for users of TensorFlow-GPU to update their installations to the fixed versions mentioned above to mitigate this vulnerability. For further assistance or support in vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow-GPU is a variant of TensorFlow that utilizes GPU acceleration for faster computation. It is widely used in various industries and research domains for training and inference of machine learning models.
The Minimum and Maximum TFLite operators are operations in TensorFlow-GPU that compute the element-wise minimum and maximum of two input tensors, respectively.
The vulnerability occurs when either of the two input tensor arguments is empty. The broadcasting implementation of these operators fails to validate the index used for indexing into both tensors, allowing data to be read outside the bounds of heap allocated objects.
The vulnerability could potentially be exploited by an attacker to read sensitive data or cause a denial-of-service condition.
To fix this vulnerability, users should update their TensorFlow-GPU installation to one of the fixed versions: TensorFlow 2.5.0, TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3, or TensorFlow 2.1.4.
References
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