CVE-2021-29534: Denial of Service in TensorFlow

tensorflow Updated 02 Oct 2025

Summary

14/05/2021

Disclosed on

CVE-2021-29534

CVE

Yes i

Fix available

Low

Severity

2.5

CVSS Score

CVE-2021-29534: 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 < 69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c

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-2021-29534 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 classification, natural language processing, and reinforcement learning.

Vulnerability Detail

The vulnerability identified as CVE-2021-29534 affects TensorFlow versions 2.1.4 to 2.4.3. It can be exploited by an attacker to trigger a denial of service (DoS) attack. The vulnerability resides in the tf.raw_ops.SparseConcat function, specifically in the implementation of the output shape calculation. The code uses a legacy constructor, TensorShape, which relies on a CHECK operation that can fail when encountering overflows. This failure can be triggered by providing specific values in shapes[0], leading to a DoS condition. The issue has been addressed in TensorFlow 2.5.0 and will also be patched in earlier supported versions.

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

As a temporary workaround, users can avoid the vulnerability by refraining from using the tf.raw_ops.SparseConcat function or ensuring that the input values provided to shapes[0] do not trigger the CHECK failure. This can be achieved by carefully validating and sanitizing the input data before passing it to the function.

Conclusion

The vulnerability CVE-2021-29534 in TensorFlow can lead to a denial of service attack. It is crucial to update TensorFlow to version 2.5.0 or apply the provided patches to mitigate the risk. Users can also use Vulert (https://vulert.com) to monitor and receive real-time alerts for open-source vulnerabilities in their software.

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 CVE-2021-29534?

The vulnerability can be exploited by an attacker to trigger a denial of service attack, potentially disrupting the availability of TensorFlow-based applications or services.

Which versions of TensorFlow are affected?

The vulnerability affects TensorFlow versions 2.1.4 to 2.4.3. It has been addressed in TensorFlow 2.5.0 and will also be patched in earlier supported versions.

How can I fix the vulnerability?

To fix the vulnerability, it is recommended to update TensorFlow to version 2.5.0 or apply the relevant patches provided for earlier supported versions.

Is there a workaround available?

Yes, as a temporary workaround, users can avoid the vulnerability by refraining from using the tf.raw_ops.SparseConcat function or ensuring that the input values provided to shapes[0] do not trigger the CHECK failure.

References

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