CVE-2021-29586: Division by zero in optimized pooling implementations in TFLite

tensorflow Updated 02 Oct 2025

Summary

21/05/2021

Disclosed on

CVE-2021-29586

CVE

Yes i

Fix available

Low

Severity

2.5

CVSS Score

CVE-2021-29586: Division by zero in optimized pooling implementations in TFLite. Affects tensorflow across 4 version ranges.

< 2.1.4 >= 2.2.0, < 2.2.3 >= 2.3.0, < 2.3.3 >= 2.4.0, < 2.4.2

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-2021-29586 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

The TensorFlow package is an open-source machine learning framework that provides a wide range of tools and libraries for building and deploying machine learning models. It is widely used in various applications, including image recognition, natural language processing, and recommendation systems. The vulnerability identified as CVE-2021-29586 affects the TensorFlow Lite (TFLite) component of the TensorFlow package. TFLite is a lightweight version of TensorFlow designed for mobile and embedded devices. It provides optimized implementations of common operations, such as pooling, to improve performance on resource-constrained devices.

Vulnerability Detail

The vulnerability in TFLite arises from a failure to check the stride arguments before calling the ComputePaddingHeightWidth function. This allows users to create models with zero stride values, leading to a division by zero error. An attacker can exploit this vulnerability by crafting a malicious model that triggers the division by zero error, potentially causing a denial of service or other unexpected behavior. The vulnerability has a CVSS score of 2.5, indicating a low severity level.

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 mentioned for this vulnerability. Users are advised to update to the patched versions of TensorFlow as mentioned in the 'how_to_fix' section.

Conclusion

The vulnerability in the optimized pooling implementations in TFLite has been identified and patched in the TensorFlow package. Users are advised to update to the latest version or the patched versions based on their current version to mitigate the risk. For further support and assistance in vulnerability remediation, please contact DevNack.com.

See every vulnerability recorded for tensorflow →

FAQ

What is TensorFlow Lite (TFLite)?

TensorFlow Lite (TFLite) is a lightweight version of TensorFlow designed for mobile and embedded devices. It provides optimized implementations of common operations to improve performance on resource-constrained devices.

What is the impact of this vulnerability?

The vulnerability allows for a division by zero error in optimized pooling implementations in TFLite. This can be exploited by crafting malicious models, potentially leading to a denial of service or other unexpected behavior.

How can I fix this vulnerability?

To fix this vulnerability, update to the latest version of TensorFlow (2.5.0) or the patched versions for earlier versions (2.1.4, 2.2.3, 2.3.3, and 2.4.2).

Is there a workaround available?

There is no specific workaround mentioned for this vulnerability. Updating to the patched versions is the recommended solution.

What is the severity level of this vulnerability?

The vulnerability has a CVSS score of 2.5, indicating a low severity level.

References

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