CVE-2021-29529: TensorFlow GPU - Heap Buffer Overflow in QuantizedResizeBilinear

tensorflow-gpu Updated 02 Oct 2025

Summary

14/05/2021

Disclosed on

CVE-2021-29529

CVE

Yes i

Fix available

Unrated

Severity

Not published

CVSS Score

CVE-2021-29529: 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 < 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 · PyPI
tensorflow · PyPI
tensorflow-cpu · 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 community resources that allow researchers and developers to build and deploy machine learning models. TensorFlow GPU is a variant of TensorFlow that leverages GPU acceleration for faster computation. It is widely used in various domains, including computer vision, natural language processing, and deep learning.

Vulnerability Detail

A heap buffer overflow vulnerability exists in the tf.raw_ops.QuantizedResizeBilinear function of TensorFlow GPU. The vulnerability arises due to an off-by-one error in accessing image elements caused by float rounding during the computation of interpolation bounds. This can be exploited by an attacker by manipulating input values, leading to a heap buffer overflow. The overflow can result in arbitrary code execution or a denial of service condition. The vulnerability affects versions 2.1.4 and above of TensorFlow GPU.

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.

< 2.1.4 2.1.4

pip install --upgrade "tensorflow-gpu==2.1.4"

>= 2.2.0, < 2.2.3 2.2.3

pip install --upgrade "tensorflow-gpu==2.2.3"

>= 2.3.0, < 2.3.3 2.3.3

pip install --upgrade "tensorflow-gpu==2.3.3"

>= 2.4.0, < 2.4.2 2.4.2

pip install --upgrade "tensorflow-gpu==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 known workaround for this vulnerability. It is recommended to apply the fix by updating to the latest fixed version of TensorFlow GPU.

Conclusion

The heap buffer overflow vulnerability in TensorFlow GPU's tf.raw_ops.QuantizedResizeBilinear function poses a significant risk to systems utilizing this package. It is crucial to update to the fixed versions of TensorFlow GPU to mitigate the vulnerability. For further support and assistance in vulnerability remediation, please contact DevNack.com.

See every vulnerability recorded for tensorflow-gpu →

FAQ

What is TensorFlow GPU?

TensorFlow GPU is a variant of TensorFlow that utilizes GPU acceleration for faster computation. It is commonly used in machine learning applications that require high-performance computing.

What is a heap buffer overflow?

A heap buffer overflow occurs when a program writes data beyond the allocated buffer in the heap memory. This can lead to memory corruption, crashes, or even remote code execution.

How can an attacker exploit this vulnerability?

An attacker can exploit this vulnerability by manipulating input values to trigger the off-by-one error in accessing image elements. This can lead to a heap buffer overflow, allowing the attacker to execute arbitrary code or cause a denial of service.

Are there any known instances of this vulnerability being exploited in the wild?

There are no known instances of this vulnerability being exploited in the wild at the time of this report. However, it is important to apply the fix as soon as possible to mitigate the risk.

Where can I get more information about the vulnerability?

For more information about the vulnerability, you can refer to the official TensorFlow repository and the associated GitHub Security Advisory (GHSA-jfp7-4j67-8r3q).

References

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