CVE-2021-29548: TensorFlow - Division by Zero Denial of Service

tensorflow Updated 02 Oct 2025

Summary

14/05/2021

Disclosed on

CVE-2021-29548

CVE

Yes i

Fix available

Low

Severity

2.5

CVSS Score

CVE-2021-29548: 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 < d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b

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-2021-29548 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 research, industry, and academia. It offers support for both CPU and GPU computations, making it suitable for a wide range of hardware configurations. However, a vulnerability has been discovered in TensorFlow that can lead to a division by zero error and denial of service.

Vulnerability Detail

The vulnerability (CVE-2021-29548) exists in the tf.raw_ops.QuantizedBatchNormWithGlobalNormalization function of TensorFlow. An attacker can exploit this vulnerability by causing a runtime division by zero error. The issue arises due to the lack of proper validation of constraints specified in the op's contract. This vulnerability can be triggered when processing quantized batch normalization operations with global normalization. The impact of this vulnerability includes denial of service, potentially disrupting the availability of TensorFlow-based machine learning systems.

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 known workaround available for this vulnerability. It is recommended to apply the fix by upgrading to the patched versions of TensorFlow.

Conclusion

The division by zero denial of service vulnerability in TensorFlow (CVE-2021-29548) can be exploited by an attacker to disrupt the availability of TensorFlow-based machine learning systems. It is crucial to upgrade to the fixed versions of TensorFlow to mitigate this vulnerability. For further support and assistance in vulnerability remediation, please contact DevNack.com.

See every vulnerability recorded for tensorflow →

FAQ

What is TensorFlow?

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

What is the impact of this vulnerability?

The vulnerability can lead to a division by zero error and denial of service, potentially disrupting the availability of TensorFlow-based machine learning systems.

How can I fix this vulnerability?

To fix the vulnerability, it is recommended to upgrade to one of the fixed versions: TensorFlow 2.5.0, TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3, or TensorFlow 2.1.4.

Is there a workaround available?

There is no known workaround for this vulnerability. It is recommended to apply the fix by upgrading to the patched versions of TensorFlow.

Where can I find more information about this vulnerability?

You can find more information about this vulnerability in the TensorFlow advisory database: https://github.com/pypa/advisory-database/blob/main/vulns/tensorflow/PYSEC-2021-185.yaml

References

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