CVE-2021-29529: TensorFlow Heap Buffer Overflow in tf.raw_ops.QuantizedResizeBilinear

tensorflow Updated 02 Oct 2025

Summary

14/05/2021

Disclosed on

CVE-2021-29529

CVE

Yes i

Fix available

Low

Severity

2.5

CVSS Score

CVE-2021-29529: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow 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 < f851613f8f0fb0c838d160ced13c134f778e3ce7

Fixed in 4 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-2021-29529 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 is widely used in various domains, including computer vision, natural language processing, and reinforcement learning. This vulnerability affects the TensorFlow package, specifically the tf.raw_ops.QuantizedResizeBilinear function.

Vulnerability Detail

The vulnerability in tf.raw_ops.QuantizedResizeBilinear arises due to a heap buffer overflow caused by an off-by-one error in accessing image elements. The implementation of the function computes upper and lower bounds for interpolation by rounding floating point values. However, for certain input values, the upper bound can be smaller than the lower bound, resulting in an out-of-bounds access. This can lead to a heap buffer overflow when performing interpolation. An attacker can exploit this vulnerability by manipulating input values to trigger the off-by-one error. The impact of this vulnerability can range from crashing the application to potential 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.

< 2.1.4 2.1.4

pip install --upgrade "tensorflow==2.1.4"

>= 2.2.0, < 2.2.3 2.2.3

pip install --upgrade "tensorflow==2.2.3"

>= 2.3.0, < 2.3.3 2.3.3

pip install --upgrade "tensorflow==2.3.3"

>= 2.4.0, < 2.4.2 2.4.2

pip install --upgrade "tensorflow==2.4.2"

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 updating to the patched versions of TensorFlow.

Conclusion

The vulnerability in TensorFlow's tf.raw_ops.QuantizedResizeBilinear function can be exploited to trigger a heap buffer overflow. It is crucial to update to the patched versions of TensorFlow or apply the fix to mitigate this vulnerability. For further support on vulnerability remediation, you can reach out to DevNack.com.

See every vulnerability recorded for tensorflow →

FAQ

What is TensorFlow?

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

What is the impact of this vulnerability?

The impact of this vulnerability can range from crashing the application to potential remote code execution.

How can I fix this vulnerability?

To fix this vulnerability, update to TensorFlow 2.5.0 or later. Alternatively, you can upgrade to the patched versions 2.4.2, 2.3.3, 2.2.3, or 2.1.4.

Is there a workaround available?

No specific workaround is provided. It is recommended to apply the fix by updating to the patched versions.

Where can I find more information about this vulnerability?

You can find more information about this vulnerability in the official TensorFlow advisory and the associated GitHub commit.

References

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