CVE-2021-29610: TensorFlow GPU - Heap Underflow in QuantizeAndDequantizeV2

tensorflow-gpu Updated 02 Oct 2025

Summary

14/05/2021

Disclosed on

CVE-2021-29610

CVE

Yes i

Fix available

Unrated

Severity

Not published

CVSS Score

CVE-2021-29610: 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 < c5b0d5f8ac19888e46ca14b0e27562e7fbbee9a9

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-2021-29610 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. It is widely used in various industries and research domains for deep learning tasks.

Vulnerability Detail

The vulnerability (CVE-2021-29610) exists in the tf.raw_ops.QuantizeAndDequantizeV2 function of TensorFlow GPU. The validation of the axis argument in this function allows for the presence of invalid values. The validation logic mistakenly combines two different conditions using the logical OR operator (||). As a result, if the value of axis is less than -1, the condition in OP_REQUIRES will still evaluate to true, leading to a heap underflow. This heap underflow can be exploited by attackers to read from or write to other data on the heap, potentially leading to unauthorized access or data corruption.

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.

< 2.2.0rc0 2.2.0rc0

pip install --upgrade "tensorflow-gpu==2.2.0rc0"

>= 2.2.0, < 2.3.0rc0 2.3.0rc0

pip install --upgrade "tensorflow-gpu==2.3.0rc0"

>= 2.3.0, < 2.3.4 2.3.4

pip install --upgrade "tensorflow-gpu==2.3.4"

>= 2.4.0, < 2.4.3 2.4.3

pip 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 provided for this vulnerability. It is recommended to apply the fix by upgrading to the patched version of TensorFlow or TensorFlow GPU.

Conclusion

The vulnerability (CVE-2021-29610) in TensorFlow GPU's tf.raw_ops.QuantizeAndDequantizeV2 function allows for a heap underflow, which can be exploited by attackers to read from or write to other data on the heap. To mitigate this issue, it is crucial to upgrade to TensorFlow 2.5.0 or a later version, or apply the fix by cherrypicking the relevant commit for older versions. For further support on vulnerability remediation, please contact DevNack.com.

See every vulnerability recorded for tensorflow-gpu →

FAQ

What is TensorFlow?

TensorFlow is an open source platform for machine learning that provides a comprehensive set of tools and libraries for developing and deploying machine learning models.

What is TensorFlow GPU?

TensorFlow GPU is a variant of TensorFlow that leverages GPU acceleration for faster computation. It is commonly used in deep learning tasks that require intensive computational power.

What is the impact of this vulnerability?

This vulnerability allows attackers to read from or write to other data on the heap, potentially leading to unauthorized access or data corruption. It is important to apply the fix to prevent such exploitation.

How can I fix this vulnerability?

To fix this vulnerability, upgrade to TensorFlow 2.5.0 or a later version. If you are using TensorFlow GPU, make sure to update to the corresponding patched version.

Are older versions of TensorFlow affected?

Yes, older versions of TensorFlow GPU (2.4.2, 2.3.3, 2.2.3, and 2.1.4) are also affected by this vulnerability. It is recommended to upgrade to the patched versions of these releases.

References

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