CVE-2021-41210: TensorFlow Heap Read Out-of-Bounds in SparseCountSparseOutput Shape Inference

tensorflow-cpu Updated 02 Oct 2025

Summary

05/11/2021

Disclosed on

CVE-2021-41210

CVE

Yes i

Fix available

Unrated

Severity

Not published

CVSS Score

CVE-2021-41210: TensorFlow is an open source platform for machine learning. Affects tensorflow-cpu 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 < 701cfaca222a82afbeeb17496bd718baa65a67d2

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-2021-41210 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 includes various functions and APIs for performing operations on tensors, which are multi-dimensional arrays. One of the functionalities in TensorFlow is shape inference, which determines the shape of tensors based on the operations performed on them. This vulnerability specifically affects the shape inference functions for SparseCountSparseOutput in TensorFlow.

Vulnerability Detail

The vulnerability in TensorFlow's shape inference functions for SparseCountSparseOutput can trigger a read outside of the bounds of a heap-allocated array. This can lead to memory corruption and potentially allow an attacker to execute arbitrary code or cause a denial of service. The issue arises due to improper bounds checking in the affected functions. The vulnerability has been assigned CVE-2021-41210 and has been reported as PYSEC-2021-619. It was discovered by the TensorFlow security team.

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.

< 2.4.4 2.4.4

pip install --upgrade "tensorflow-cpu==2.4.4"

>= 2.5.0, < 2.5.2 2.5.2

pip install --upgrade "tensorflow-cpu==2.5.2"

>= 2.6.0, < 2.6.1 2.6.1

pip install --upgrade "tensorflow-cpu==2.6.1"

>= 2.7.0rc0, < 2.7.0 2.7.0

pip install --upgrade "tensorflow-cpu==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 known workaround for this vulnerability. It is recommended to apply the official fix provided by the TensorFlow team as soon as it is available.

Conclusion

In conclusion, the vulnerability in TensorFlow's shape inference functions for SparseCountSparseOutput can lead to memory corruption and potential code execution or denial of service. Users are advised to update their TensorFlow installations to the fixed versions as soon as they are available. 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 a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models.

What is shape inference in TensorFlow?

Shape inference in TensorFlow is the process of determining the shape of tensors based on the operations performed on them. It helps in understanding the dimensions and sizes of tensors at different stages of a machine learning model.

What is `SparseCountSparseOutput` in TensorFlow?

SparseCountSparseOutput is a function in TensorFlow that performs a specific operation on sparse tensors. It is used in various machine learning tasks, such as natural language processing and recommendation systems.

What are the potential consequences of this vulnerability?

This vulnerability can lead to memory corruption and potentially allow an attacker to execute arbitrary code or cause a denial of service. It is important to update TensorFlow installations to the fixed versions as soon as they are available to mitigate these risks.

How can I check if my TensorFlow installation is affected by this vulnerability?

You can use the Vulert Playground (https://vulert.com/scanner) to check if your application is affected by this vulnerability. Simply provide your Pipfile.lock and the playground will analyze it for any open-source vulnerabilities, including this one.

References

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