CVE-2021-29583: TensorFlow GPU - FusedBatchNorm Heap Buffer Overflow
Summary
CVE-2021-29583: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow-gpu across 5 version ranges.
>= 2.2.0, < 2.3.0rc0
< 2.2.0rc0
>= 2.3.0, < 2.3.4
>= 2.4.0, < 2.4.3
< 6972f9dfe325636b3db4e0bc517ee22a159365c0
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-2021-29583 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 developing and deploying machine learning models. TensorFlow GPU is a variant of TensorFlow that leverages GPU acceleration for faster computation. The tf.raw_ops.FusedBatchNorm function in TensorFlow GPU is vulnerable to a heap buffer overflow.
Vulnerability Detail
The vulnerability arises due to the lack of validation in the implementation of tf.raw_ops.FusedBatchNorm. When the tensors scale, offset, mean, and variance (the last two only when required) do not have the same number of elements as the number of channels of x, heap out-of-bounds reads can occur. This can lead to a heap buffer overflow, potentially resulting in arbitrary code execution. Additionally, if the tensors are empty, the lack of validation can trigger undefined behavior by dereferencing null pointers.
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.
-
< 2.2.0rc02.2.0rc0pip install --upgrade "tensorflow-gpu==2.2.0rc0" -
>= 2.2.0, < 2.3.0rc02.3.0rc0pip install --upgrade "tensorflow-gpu==2.3.0rc0" -
>= 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 in TensorFlow GPU's tf.raw_ops.FusedBatchNorm function can lead to a heap buffer overflow and potential arbitrary code execution. It is crucial to update to the latest version of TensorFlow GPU or apply the provided fix to mitigate the risk. For further support on vulnerability remediation, please reach out to DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow is an open source platform for machine learning that provides tools and resources for developing and deploying machine learning models.
TensorFlow GPU is a variant of TensorFlow that utilizes GPU acceleration for faster computation.
tf.raw_ops.FusedBatchNorm is a function in TensorFlow GPU that performs batch normalization on input tensors.
A heap buffer overflow occurs when a program writes data beyond the bounds of a dynamically allocated buffer in the heap, potentially overwriting adjacent memory and leading to various security issues.
You can use Vulert's playground (https://vulert.com/scanner) to check if your application is affected by this vulnerability. Vulert monitors and alerts in real-time for open-source vulnerabilities without requiring access to the code or installation.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-gpu.
