CVE-2021-37651: TensorFlow GPU - Heap Buffer Overflow in FractionalAvgPoolGrad
Summary
CVE-2021-37651: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow-gpu across 3 version ranges.
< 0f931751fb20f565c4e94aa6df58d54a003cdb30
>= 2.3.0, < 2.3.4
>= 2.4.0, < 2.4.3
Fixed in 2.3.4 or 2.4.3, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow · PyPI
- tensorflow-cpu · PyPI
The same CVE-2021-37651 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 resources for building and deploying machine learning models. TensorFlow GPU is a variant of TensorFlow that leverages GPU acceleration for faster training and inference. It is widely used in various industries and research domains for deep learning tasks.
Vulnerability Detail
The vulnerability (CVE-2021-37651) affects the tf.raw_ops.FractionalAvgPoolGrad implementation in TensorFlow GPU. This implementation can be tricked into accessing data outside the bounds of heap allocated buffers. The issue arises due to the lack of validation for non-empty input tensors. An attacker can exploit this vulnerability to construct an empty EigenDoubleMatrixMap and access the buffer with indices outside the empty area. This can lead to heap buffer overflow, potentially resulting in arbitrary code execution or denial of service.
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.
-
>= 2.3.0, < 2.3.42.3.4pip install --upgrade "tensorflow-gpu==2.3.4" -
>= 2.4.0, < 2.4.32.4.3pip install --upgrade "tensorflow-gpu==2.4.3"
Tip — Not sure which release line you're on? Vulert can find the fix for the version you are using. Scan free →
Workaround
There is no specific workaround mentioned for this vulnerability.
Conclusion
The vulnerability (CVE-2021-37651) in TensorFlow GPU's tf.raw_ops.FractionalAvgPoolGrad implementation can result in a heap buffer overflow. It is crucial for users to update their TensorFlow GPU installations to the patched version or manually apply the fix to mitigate the risk of arbitrary code execution or denial of service. For further support on vulnerability remediation, contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow is an open source platform for machine learning that provides tools and libraries for building and deploying machine learning models.
TensorFlow GPU is a variant of TensorFlow that utilizes GPU acceleration for faster training and inference of machine learning models.
This vulnerability can lead to heap buffer overflow, potentially resulting in arbitrary code execution or denial of service.
To fix this vulnerability, update your TensorFlow GPU installation to the patched version once it becomes available. Alternatively, manually apply the fix by cherry-picking the relevant commit onto the affected TensorFlow versions.
Yes, TensorFlow versions 2.3.0 and above are affected. The vulnerability has been patched in TensorFlow 2.3.4, 2.4.3, and will be included in TensorFlow 2.6.0.
References
Related vulnerabilities
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