CVE-2021-41197: TensorFlow CPU - Integer Overflow Vulnerability

tensorflow-cpu Updated 02 Oct 2025

Summary

05/11/2021

Disclosed on

CVE-2021-41197

CVE

Yes i

Fix available

Unrated

Severity

Not published

CVSS Score

CVE-2021-41197: TensorFlow is an open source platform for machine learning. Affects tensorflow-cpu across 7 version ranges.

< 2.4.4 >= 2.5.0, < 2.5.2 >= 2.6.0, < 2.6.1 >= 2.7.0rc0, < 2.7.0 < 7c1692bd417eb4f9b33ead749a41166d6080af85 < a871989d7b6c18cdebf2fb4f0e5c5b62fbc19edf < d81b1351da3e8c884ff836b64458d94e4a157c15

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-2021-41197 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 building and deploying machine learning models. TensorFlow allows tensors to have a large number of dimensions, with each dimension capable of being as large as desired. However, there is a vulnerability in TensorFlow versions prior to 2.7.0 that allows an integer overflow to occur when the total number of elements in a tensor exceeds the maximum limit of an int64_t. This vulnerability is similar to CVE-2021-29584. The issue has been identified and fixed in TensorFlow 2.7.0, and the fix has also been backported to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4.

Vulnerability Detail

The vulnerability in TensorFlow arises when a tensor has a large number of dimensions and the total number of elements in the tensor exceeds the maximum limit of an int64_t. When an overflow occurs, the function MultiplyWithoutOverflow returns a negative result. In most cases, this leads to a CHECK-failure, causing the binary to crash. However, newer constructs have been introduced that return a Status instead of crashing the binary. This vulnerability can potentially be exploited to cause denial of service (DoS) attacks or other unexpected behavior in TensorFlow applications.

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 specific workaround mentioned for this vulnerability. The recommended approach is to update TensorFlow to the fixed versions mentioned above.

Conclusion

The integer overflow vulnerability in TensorFlow can lead to unexpected crashes or DoS attacks in TensorFlow applications. It is crucial to update to the fixed versions (TensorFlow 2.7.0, TensorFlow 2.6.1, TensorFlow 2.5.2, or TensorFlow 2.4.4) to ensure the security and stability of your TensorFlow deployments. 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 libraries for building and deploying machine learning models.

What is an integer overflow vulnerability?

An integer overflow vulnerability occurs when an arithmetic operation results in a value that exceeds the maximum limit of the data type used to store the result. In the case of TensorFlow, the total number of elements in a tensor exceeds the maximum limit of an int64_t, leading to an overflow.

What are the potential consequences of this vulnerability?

The integer overflow vulnerability in TensorFlow can potentially be exploited to cause denial of service (DoS) attacks or other unexpected behavior in TensorFlow applications. It is important to update to the fixed versions to mitigate these risks.

Are all versions of TensorFlow affected by this vulnerability?

No, only versions prior to TensorFlow 2.7.0 are affected by this vulnerability. The fix has been backported to TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4.

How can I update TensorFlow to the fixed version?

You can update TensorFlow to the fixed version by using the package manager or following the installation instructions provided by the TensorFlow project. Make sure to update to TensorFlow 2.7.0, TensorFlow 2.6.1, TensorFlow 2.5.2, or TensorFlow 2.4.4.

References

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