CVE-2021-29565: TensorFlow - Null Pointer Dereference in SparseFillEmptyRows

tensorflow Updated 02 Oct 2025

Summary

14/05/2021

Disclosed on

CVE-2021-29565

CVE

Yes i

Fix available

Low

Severity

2.5

CVSS Score

CVE-2021-29565: 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 < faa76f39014ed3b5e2c158593b1335522e573c7f

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-2021-29565 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 and can be exploited by an attacker to trigger a null pointer dereference in the implementation of tf.raw_ops.SparseFillEmptyRows.

Vulnerability Detail

The vulnerability arises due to missing validation in the implementation of tf.raw_ops.SparseFillEmptyRows. Specifically, the dense_shape tensor is not properly validated when it is empty. As a result, if the dense_shape tensor is empty, the dense_shape_t.vec<>() operation can cause a null pointer dereference. This vulnerability can be exploited by an attacker to crash the application or potentially execute arbitrary code. The issue has been identified and fixed in TensorFlow 2.5.0. To mitigate the risk, it is recommended to update to the latest version of TensorFlow. The fix has also been backported to TensorFlow versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4, which are still within the supported range.

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 specific workaround provided for this vulnerability. The recommended approach is to update the TensorFlow package to the latest version or apply the corresponding patched version if using an older version.

Conclusion

In conclusion, the vulnerability in TensorFlow (CVE-2021-29565) allows for a null pointer dereference in the tf.raw_ops.SparseFillEmptyRows implementation. This can be exploited by an attacker to crash the application or potentially execute arbitrary code. It is crucial to update the TensorFlow package to the latest version or apply the corresponding patched version to mitigate the risk. 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 a comprehensive ecosystem of tools and resources.

What is the impact of this vulnerability?

This vulnerability can be exploited by an attacker to crash the application or potentially execute arbitrary code.

How can I fix this vulnerability?

To fix this vulnerability, update the TensorFlow package to the latest version (2.5.0) or apply the corresponding patched version if using an older version.

Are there any workarounds available?

There are no specific workarounds provided for this vulnerability. Updating the TensorFlow package is the recommended approach.

Which versions of TensorFlow are affected?

The vulnerability affects TensorFlow versions 2.1.4, 2.2.3, 2.3.3, 2.4.2, and versions prior to 2.5.0.

References

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