CVE-2021-29552: TensorFlow Denial of Service Vulnerability

tensorflow Updated 02 Oct 2025

Summary

14/05/2021

Disclosed on

CVE-2021-29552

CVE

Yes i

Fix available

Low

Severity

2.5

CVSS Score

CVE-2021-29552: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow across 5 version ranges.

>= 2.2.0, < 2.3.0rc0 < 2.2.0rc0 >= 2.3.0, < 2.3.4 >= 2.4.0, < 2.4.3 < 704866eabe03a9aeda044ec91a8d0c83fc1ebdbe

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-2021-29552 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 end-to-end 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 industries and research domains for tasks such as image recognition, natural language processing, and recommendation systems. However, a vulnerability has been discovered in TensorFlow that can lead to a denial of service (DoS) attack.

Vulnerability Detail

The vulnerability (CVE-2021-29552) in TensorFlow arises from a flaw in the UnsortedSegmentJoin function. An attacker can exploit this vulnerability by controlling the values of the num_segments tensor argument. The implementation assumes that the num_segments tensor is a valid scalar, but if the tensor is empty, the validation check fails and the process terminates, resulting in a DoS condition. This vulnerability can be triggered remotely by an attacker with the ability to manipulate the num_segments tensor. The impact of this vulnerability is the disruption of TensorFlow-based applications and services, leading to service unavailability and potential financial losses.

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.

< 2.2.0rc0 2.2.0rc0

pip install --upgrade "tensorflow==2.2.0rc0"

>= 2.2.0, < 2.3.0rc0 2.3.0rc0

pip install --upgrade "tensorflow==2.3.0rc0"

>= 2.3.0, < 2.3.4 2.3.4

pip install --upgrade "tensorflow==2.3.4"

>= 2.4.0, < 2.4.3 2.4.3

pip install --upgrade "tensorflow==2.4.3"

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 course of action is to apply the available patches or upgrade to the latest version of TensorFlow.

Conclusion

The vulnerability (CVE-2021-29552) in TensorFlow can lead to a denial of service attack, causing disruption to TensorFlow-based applications and services. It is crucial to upgrade to the latest version of TensorFlow or apply the available patches to mitigate the risk. Regularly checking for security updates and promptly applying them is recommended. 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 a comprehensive set of tools and libraries for developing and deploying machine learning models.

What is the impact of this vulnerability?

The vulnerability can be exploited by an attacker to cause a denial of service, leading to the disruption of TensorFlow-based applications and services.

How can I fix this vulnerability?

To fix this vulnerability, it is recommended to upgrade to the latest version of TensorFlow or apply the available patches for the affected versions.

Are there any workarounds for this vulnerability?

No specific workarounds have been provided. It is recommended to apply the patches or upgrade to the latest version of TensorFlow.

Can this vulnerability be exploited remotely?

Yes, this vulnerability can be triggered remotely by an attacker with the ability to manipulate the num_segments tensor.

References

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