CVE-2021-29595: Division by zero in TFLite's implementation of DepthToSpace

tensorflow Updated 02 Oct 2025

Summary

21/05/2021

Disclosed on

CVE-2021-29595

CVE

Yes i

Fix available

Low

Severity

2.5

CVSS Score

CVE-2021-29595: Division by zero in TFLite's implementation of DepthToSpace. 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-2021-29595 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 developed by Google. It provides a comprehensive set of tools and libraries for building and deploying machine learning models. TensorFlow Lite (TFLite) is a lightweight version of TensorFlow specifically designed for mobile and embedded devices. It allows developers to run machine learning models on resource-constrained platforms. TFLite includes an operator called DepthToSpace, which rearranges data in the depth dimension of a tensor. This operator is vulnerable to a division by zero error, which can be exploited by an attacker.

Vulnerability Detail

The vulnerability in TFLite's DepthToSpace operator arises from a division by zero error in its implementation. The vulnerable code can be found in the depth_to_space.cc file of the TensorFlow Lite library. The operator calculates the output channel size by dividing the input channel size by the block size squared. However, if the block size is set to zero, a division by zero occurs, leading to a runtime error. An attacker can craft a malicious model that triggers this vulnerability, potentially causing the application to crash or behave unexpectedly. The impact of this vulnerability is considered low, as it requires the attacker to have control over the model inputs.

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.

Conclusion

The division by zero vulnerability in TFLite's DepthToSpace operator has been addressed by the TensorFlow team. Users are advised to update their TensorFlow installations to the patched versions once they are available. For further support and assistance with vulnerability remediation, please contact DevNack.com.

See every vulnerability recorded for tensorflow →

FAQ

What is TensorFlow?

TensorFlow is an open-source machine learning framework developed by Google. It provides tools and libraries for building and deploying machine learning models.

What is TensorFlow Lite?

TensorFlow Lite (TFLite) is a lightweight version of TensorFlow designed for mobile and embedded devices. It allows running machine learning models on resource-constrained platforms.

What is the DepthToSpace operator in TFLite?

The DepthToSpace operator in TFLite rearranges data in the depth dimension of a tensor. It is used for tasks like upsampling and image super-resolution.

How can an attacker exploit this vulnerability?

An attacker can craft a malicious model that triggers the division by zero error in the DepthToSpace operator. This can potentially cause the application to crash or behave unexpectedly.

What is the impact of this vulnerability?

The impact of this vulnerability is considered low, as it requires the attacker to have control over the model inputs. However, it can lead to application crashes or unexpected behavior.

References

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