CVE-2021-29560: TensorFlow Heap Buffer Overflow in tf.raw_ops.RaggedTensorToTensor

tensorflow-cpu Updated 02 Oct 2025

Summary

14/05/2021

Disclosed on

CVE-2021-29560

CVE

Yes i

Fix available

Unrated

Severity

Not published

CVSS Score

CVE-2021-29560: 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 < a84358aa12f0b1518e606095ab9cfddbf597c121

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-2021-29560 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 allows users to build and train neural networks, perform data preprocessing, and evaluate model performance. It is widely used in various industries and research domains.

Vulnerability Detail

The vulnerability (CVE-2021-29560) in TensorFlow affects the tf.raw_ops.RaggedTensorToTensor function. This function is responsible for converting a ragged tensor to a dense tensor. The vulnerability arises due to a heap buffer overflow caused by accessing two arrays in parallel using the same index. An attacker can exploit this vulnerability by controlling the shape of the input arguments and triggering a heap out-of-bounds access. This can lead to arbitrary code execution or a denial of service attack. The vulnerability has been assigned a CVSS score of X.X, indicating its severity.

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

As a temporary workaround, users can avoid using the tf.raw_ops.RaggedTensorToTensor function or ensure that the input arguments have compatible shapes to prevent triggering the heap buffer overflow. However, this workaround may not provide complete protection, and it is strongly recommended to apply the official fix by updating TensorFlow to a fixed version.

Conclusion

The vulnerability in TensorFlow (CVE-2021-29560) can lead to a heap buffer overflow in the tf.raw_ops.RaggedTensorToTensor function. It is crucial for users to update their TensorFlow installations to the fixed versions provided by the TensorFlow team. Regularly updating dependencies and staying informed about security advisories is essential to ensure the security of machine learning projects. For further support and assistance in 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 building and training neural networks.

What is the impact of this vulnerability?

This vulnerability can allow an attacker to execute arbitrary code or cause a denial of service attack by triggering a heap buffer overflow in TensorFlow.

How can I fix this vulnerability?

To fix this vulnerability, update your TensorFlow installation to one of the fixed versions: TensorFlow 2.5.0, TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3, or TensorFlow 2.1.4.

Are there any workarounds available?

As a temporary workaround, you can avoid using the vulnerable tf.raw_ops.RaggedTensorToTensor function or ensure that the input arguments have compatible shapes.

Where can I find more information about this vulnerability?

For more information about this vulnerability, refer to the official security advisory and the GitHub repository of TensorFlow.

References

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