CVE-2021-29603: TensorFlow Out-of-Bounds Write Vulnerability

tensorflow Updated 02 Oct 2025

Summary

14/05/2021

Disclosed on

CVE-2021-29603

CVE

Yes i

Fix available

Low

Severity

2.5

CVSS Score

CVE-2021-29603: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow across 5 version ranges.

< 2.1.4 >= 2.2.0, < 2.2.3 >= 2.3.0, < 2.3.3 >= 2.4.0, < 2.4.2 < c59c37e7b2d563967da813fa50fe20b21f4da683

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-2021-29603 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. This vulnerability affects the TensorFlow package, specifically the TFLite implementation of ArgMin/ArgMax.

Vulnerability Detail

A specially crafted TFLite model can trigger an out-of-bounds write on the heap in the TFLite implementation of ArgMin/ArgMax. The issue arises when the axis_value parameter is not within the valid range of 0 to NumDimensions(input). In such cases, the condition in the code is never true, causing the code to write beyond the last valid element of output_dims->data. This vulnerability can potentially lead to memory corruption and may be exploited by an attacker to execute arbitrary code or cause a denial-of-service condition.

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.

< 2.1.4 2.1.4

pip install --upgrade "tensorflow==2.1.4"

>= 2.2.0, < 2.2.3 2.2.3

pip install --upgrade "tensorflow==2.2.3"

>= 2.3.0, < 2.3.3 2.3.3

pip install --upgrade "tensorflow==2.3.3"

>= 2.4.0, < 2.4.2 2.4.2

pip install --upgrade "tensorflow==2.4.2"

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 recommended to apply the provided fix or upgrade to a fixed version of TensorFlow.

Conclusion

The TensorFlow package is vulnerable to an out-of-bounds write issue in the TFLite implementation of ArgMin/ArgMax. This vulnerability can potentially lead to memory corruption and may be exploited by an attacker. It is crucial to update your TensorFlow installation to a fixed version or apply the provided patch to mitigate the risk. For further support on vulnerability remediation, you can 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 TFLite?

TFLite is a lightweight version of TensorFlow designed for mobile and embedded devices. It allows developers to run TensorFlow models on resource-constrained platforms.

What is an out-of-bounds write vulnerability?

An out-of-bounds write vulnerability occurs when a program writes data beyond the boundaries of a buffer or allocated memory. This can lead to memory corruption and potentially allow an attacker to execute arbitrary code or cause a denial-of-service condition.

How can I check if my TensorFlow installation is affected?

You can use the Vulert Playground at Vulert Scanner to check if your application is affected by this vulnerability. Simply provide your Pipfile.lock, and Vulert will analyze it for open-source vulnerabilities in the software.

Where can I find more information about this vulnerability?

You can find more information about this vulnerability in the TensorFlow advisory database. Visit the following link for the specific vulnerability details: https://github.com/pypa/advisory-database/blob/main/vulns/tensorflow/PYSEC-2021-240.yaml

References

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