CVE-2021-41205: TensorFlow - Out-of-Bounds Read in QuantizeAndDequantizeV* Operations

tensorflow Updated 02 Oct 2025

Summary

05/11/2021

Disclosed on

CVE-2021-41205

CVE

Yes i

Fix available

Moderate

Severity

7.1

CVSS Score

CVE-2021-41205: TensorFlow is an open source platform for machine learning. Affects tensorflow 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 < 7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d

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-2021-41205 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 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 affects certain versions of the package.

Vulnerability Detail

The vulnerability in TensorFlow is related to the shape inference functions used in the QuantizeAndDequantizeV* operations. These functions can trigger a read outside the bounds of a heap-allocated array, leading to potential memory corruption or information disclosure. An attacker could potentially exploit this vulnerability to execute arbitrary code, crash the application, or leak sensitive information. The issue has been assigned the identifier CVE-2021-41205 and has been classified as a high-severity vulnerability.

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.

< 2.4.4 2.4.4

pip install --upgrade "tensorflow==2.4.4"

>= 2.5.0, < 2.5.2 2.5.2

pip install --upgrade "tensorflow==2.5.2"

>= 2.6.0, < 2.6.1 2.6.1

pip install --upgrade "tensorflow==2.6.1"

>= 2.7.0rc0, < 2.7.0 2.7.0

pip install --upgrade "tensorflow==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 strongly advised to apply the provided patches or update to the latest version of TensorFlow as soon as possible.

Conclusion

In conclusion, the vulnerability in TensorFlow's shape inference functions for QuantizeAndDequantizeV* operations can lead to out-of-bounds reads and potential memory corruption or information disclosure. It is crucial to update to the latest patched version of TensorFlow or apply the provided patches to mitigate the vulnerability. For further support on vulnerability remediation, you can reach out to DevNack.com.

See every vulnerability recorded for tensorflow →

FAQ

What is TensorFlow?

TensorFlow is an open source platform for machine learning that provides tools and resources for developing and deploying machine learning models.

What is the impact of this vulnerability?

The vulnerability can potentially allow an attacker to execute arbitrary code, crash the application, or leak sensitive information.

Which versions of TensorFlow are affected?

The vulnerability affects TensorFlow versions 2.4.4, 2.5.2, 2.6.1, and 2.7.0rc0. Earlier versions may also be affected.

Are there any known workarounds?

No, there are no known workarounds for this vulnerability. It is recommended to update to the latest patched version of TensorFlow.

Where can I find more information about this vulnerability?

You can find more information about this vulnerability in the official TensorFlow advisory database: https://github.com/pypa/advisory-database/blob/main/vulns/tensorflow/PYSEC-2021-398.yaml

References

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