CVE-2021-29544: Denial of Service in TensorFlow

tensorflow-cpu Updated 02 Oct 2025

Summary

14/05/2021

Disclosed on

CVE-2021-29544

CVE

Yes i

Fix available

Unrated

Severity

Not published

CVSS Score

CVE-2021-29544: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow-cpu 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 < 20431e9044cf2ad3c0323c34888b192f3289af6b

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-2021-29544 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 domains, including research, industry, and academia. It offers support for both CPU and GPU computations, enabling efficient training and inference on a wide range of hardware configurations. However, a vulnerability has been discovered in TensorFlow that can lead to a denial of service attack.

Vulnerability Detail

The vulnerability (CVE-2021-29544) in TensorFlow is triggered by a CHECK-fail in the tf.raw_ops.QuantizeAndDequantizeV4Grad function. The implementation of this function does not properly validate the rank of the input tensors, allowing an attacker to pass tensors with an incorrect rank. This leads to a CHECK failure in the vec<T> method, resulting in a denial of service. The issue has been identified in the codebase and a fix will be included in TensorFlow 2.5.0. The affected versions include TensorFlow 2.2.0rc0, 2.2.0, 2.3.0rc0, 2.3.0, 2.3.4, 2.4.0, and 2.4.3.

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.

< 2.2.0rc0 2.2.0rc0

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

>= 2.2.0, < 2.3.0rc0 2.3.0rc0

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

>= 2.3.0, < 2.3.4 2.3.4

pip install --upgrade "tensorflow-cpu==2.3.4"

>= 2.4.0, < 2.4.3 2.4.3

pip install --upgrade "tensorflow-cpu==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 mentioned for this vulnerability. However, users can mitigate the risk by following the recommended fix and updating to the patched versions of TensorFlow.

Conclusion

The vulnerability in TensorFlow (CVE-2021-29544) can lead to a denial of service attack. It is crucial for users of affected versions to update to the patched versions or apply the provided patch to ensure the security and stability of their machine learning workflows. For further support on vulnerability remediation, please contact 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 libraries for developing and deploying machine learning models.

What is the impact of this vulnerability?

The vulnerability can be exploited by an attacker to trigger a denial of service attack, potentially disrupting machine learning workflows and impacting system availability.

Which versions of TensorFlow are affected?

The vulnerability affects TensorFlow versions 2.2.0rc0, 2.2.0, 2.3.0rc0, 2.3.0, 2.3.4, 2.4.0, and 2.4.3.

How can I fix the vulnerability?

To fix the vulnerability, it is recommended to update TensorFlow to version 2.5.0 once it is released. Alternatively, users can upgrade to TensorFlow 2.4.2 or apply the provided patch manually.

Is there a workaround available?

There is no specific workaround mentioned for this vulnerability. However, users can mitigate the risk by following the recommended fix and updating to the patched versions of TensorFlow.

References

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