CVE-2021-41218: TensorFlow AllToAll Division by Zero Vulnerability

tensorflow Updated 02 Oct 2025

Summary

05/11/2021

Disclosed on

CVE-2021-41218

CVE

Yes i

Fix available

Moderate

Severity

5.5

CVSS Score

CVE-2021-41218: TensorFlow is an open source platform for machine learning. Affects tensorflow across 5 version ranges.

< 2.4.4 >= 2.5.0, < 2.5.2 >= 2.6.0, < 2.6.1 >= 2.7.0rc0, < 2.7.0 < a8ad3e5e79c75f36edb81e0ba3f3c0c5442aeddc

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-2021-41218 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 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 distributed computing, allowing users to train and deploy models on multiple devices or machines. However, a vulnerability has been discovered in TensorFlow that affects the shape inference code for the AllToAll operation.

Vulnerability Detail

The vulnerability in TensorFlow arises when the split_count argument in the AllToAll operation is set to 0. This triggers a division by zero error in the shape inference code. An attacker can exploit this vulnerability by crafting malicious input data that triggers the AllToAll operation with a split_count of 0. The division by zero error can lead to a crash or unexpected behavior in the TensorFlow application. The impact of this vulnerability depends on the specific usage of TensorFlow in the application. It could potentially result in denial of service, data corruption, or even remote code execution if the application is exposed to untrusted input.

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.

< 2.4.4 2.4.4

pip install --upgrade "tensorflow==2.4.4"

>= 2.5.0, < 2.5.2 2.5.2

pip install --upgrade "tensorflow==2.5.2"

>= 2.6.0, < 2.6.1 2.6.1

pip install --upgrade "tensorflow==2.6.1"

>= 2.7.0rc0, < 2.7.0 2.7.0

pip install --upgrade "tensorflow==2.7.0"

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. The recommended approach is to update TensorFlow to the fixed versions mentioned above.

Conclusion

In conclusion, the TensorFlow AllToAll Division by Zero vulnerability can be exploited by setting the split_count argument to 0 in the AllToAll operation. This can lead to a division by zero error and potentially result in a crash or unexpected behavior in the TensorFlow application. It is crucial to update TensorFlow to the fixed versions to mitigate this vulnerability. For further support on 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 tools and resources for developing and deploying machine learning models.

What is the impact of this vulnerability?

The impact of this vulnerability depends on the specific usage of TensorFlow in the application. It could potentially result in denial of service, data corruption, or even remote code execution if the application is exposed to untrusted input.

How can I fix this vulnerability?

To fix this vulnerability, it is recommended to update TensorFlow to version 2.7.0 or the backported versions mentioned earlier.

Are there any workarounds available?

There are no specific workarounds provided for this vulnerability. Updating to the fixed versions of TensorFlow is the recommended approach.

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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