CVE-2021-29517: TensorFlow Division by Zero in Conv3D Implementation

tensorflow-cpu Updated 02 Oct 2025

Summary

14/05/2021

Disclosed on

CVE-2021-29517

CVE

Yes i

Fix available

Unrated

Severity

Not published

CVSS Score

CVE-2021-29517: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow-cpu 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 < 799f835a3dfa00a4d852defa29b15841eea9d64f

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-gpu · PyPI

The same CVE-2021-29517 is tracked separately for each ecosystem and vendor. Its fix information is the same; the package and affected versions differ.

Free scan

Check if this vulnerability affects your application

Upload a manifest or SBOM and we'll show whether you're affected, plus every other risky package in the same scan.

Check my app free

No signup required · Results in about 60 seconds

Upload your dependency manifest or SBOM

Drag & drop your file here or Browse

Java Javascript PHP Python Go Rust Ruby Dart C/C++ Elixir CycloneDX SPDX

Upload a supported dependency manifest or SBOM. No installation or source-code access required.

See supported files and SBOM formats

SBOM sbom.json · bom.json · bom.xml · *.spdx.json · *.cdx.json · *.cdx.xml

Java pom.xml · gradle.lockfile · buildscript-gradle.lockfile

Javascript package-lock.json · yarn.lock · pnpm-lock.yaml

PHP composer.lock

Python poetry.lock · requirements.txt · Pipfile.lock

Go go.mod

Rust Cargo.lock

Ruby Gemfile.lock

Dart pubspec.lock

C/C++ conan.lock

Elixir mix.lock

Enter your work email to receive your report link.

Verify your email to view the full report and start 30 days of continuous monitoring. No credit card required.

Background

TensorFlow is an end-to-end open source platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and resources for building and deploying machine learning models. TensorFlow includes various operations and functions for performing convolutional operations, such as the Conv3D implementation. This implementation is used for 3D convolutional operations in deep learning models. However, a vulnerability exists in the Conv3D implementation that can be exploited by a malicious user.

Vulnerability Detail

The vulnerability in the Conv3D implementation arises from a division by zero error. The implementation performs a modulo operation based on user-controlled input. If the fifth element of the filter tensor is set to 0, it results in a division by zero. This can lead to unexpected behavior or crashes in the program. Additionally, if the shape of the two tensors involved in the convolution operation is not valid, it can trigger an Eigen assertion and cause a program crash. The vulnerability can be exploited by an attacker to disrupt the execution of TensorFlow models or potentially execute arbitrary code.

How To Fix

Latest published version: 2.21.0 as of 22 Oct 2026

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

>= 2.2.0, < 2.2.3 2.2.3

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

>= 2.3.0, < 2.3.3 2.3.3

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

>= 2.4.0, < 2.4.2 2.4.2

pip install --upgrade "tensorflow-cpu==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 provided for this vulnerability. The recommended approach is to upgrade to the patched versions of TensorFlow.

Conclusion

The division by zero vulnerability in the Conv3D implementation of TensorFlow can be exploited by a malicious user to disrupt the execution of TensorFlow models or potentially execute arbitrary code. It is recommended to upgrade to the patched versions of TensorFlow to mitigate this vulnerability. For further support on vulnerability remediation, you can reach out to DevNack.com.

See every vulnerability recorded for tensorflow-cpu →

FAQ

What is TensorFlow?

TensorFlow is an open source platform for machine learning that provides tools and resources for building and deploying machine learning models.

What is the `Conv3D` implementation in TensorFlow?

Conv3D is an operation in TensorFlow that performs 3D convolutional operations, commonly used in deep learning models for processing volumetric data.

How can the division by zero vulnerability be exploited?

A malicious user can provide a filter tensor with a 0 as the fifth element, triggering a division by zero in the Conv3D implementation. This can lead to unexpected behavior or crashes in the program.

What is an Eigen assertion?

Eigen is a C++ template library for linear algebra. An Eigen assertion is a mechanism used in Eigen to check for conditions that should always hold true. In this vulnerability, an invalid shape of the tensors can trigger an Eigen assertion and cause a program crash.

Which versions of TensorFlow are affected by this vulnerability?

The vulnerability affects TensorFlow versions 2.1.4 and above, including versions 2.2.3, 2.3.3, 2.4.2, and any versions in the 2.5.0 release candidate range.

References

Related vulnerabilities

Recent vulnerabilities recorded for tensorflow-cpu.

View all tensorflow-cpu vulnerabilities →