CVE-2021-29610: TensorFlow - Heap Underflow in QuantizeAndDequantizeV2

tensorflow-cpu Updated 02 Oct 2025

Summary

14/05/2021

Disclosed on

CVE-2021-29610

CVE

Yes i

Fix available

Unrated

Severity

Not published

CVSS Score

CVE-2021-29610: 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 < c5b0d5f8ac19888e46ca14b0e27562e7fbbee9a9

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-2021-29610 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 building and deploying machine learning models. TensorFlow is widely used in various domains, including research, industry, and academia. The vulnerability described here affects the tf.raw_ops.QuantizeAndDequantizeV2 function in TensorFlow.

Vulnerability Detail

The vulnerability in tf.raw_ops.QuantizeAndDequantizeV2 arises from a validation issue with the axis argument. The validation logic in the function allows for invalid values of axis, leading to a heap underflow. An attacker can exploit this vulnerability to read from or write to other data on the heap, potentially resulting in unauthorized access or data corruption. The issue has been identified and fixed in TensorFlow 2.5.0, and patches have been backported to earlier versions.

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, you can avoid using the tf.raw_ops.QuantizeAndDequantizeV2 function with invalid values for the axis argument. Review your codebase and ensure that proper input validation and sanitization are in place to prevent the use of invalid values. Additionally, consider implementing runtime checks or input validation mechanisms to detect and handle any potential misuse of this function.

Conclusion

The vulnerability in TensorFlow's tf.raw_ops.QuantizeAndDequantizeV2 function has been identified and fixed in TensorFlow 2.5.0. It is crucial to update your TensorFlow installation to the latest version or apply the provided patches to ensure the security and integrity of your machine learning applications. If you have any further questions or need assistance with vulnerability remediation, please 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 a comprehensive set of tools and resources for building and deploying machine learning models.

What is the impact of this vulnerability?

This vulnerability allows attackers to read from or write to other data on the heap, potentially leading to unauthorized access or data corruption.

Which versions of TensorFlow are affected?

The vulnerability affects TensorFlow versions 2.1.4, 2.2.3, 2.3.3, and 2.4.2. These versions are still within the supported range and have received patches to address the issue.

How can I check if my application is affected?

You can use the Vulert Playground (https://vulert.com/scanner) to check if your application is affected by this vulnerability. The playground allows you to analyze your Pipfile.lock and provides real-time alerts for open-source vulnerabilities.

Where can I find more information about this vulnerability?

For more information about this vulnerability, you can refer to the official TensorFlow advisory (https://github.com/pypa/advisory-database/blob/main/vulns/tensorflow-cpu/PYSEC-2021-538.yaml). Additionally, you can reach out to info@devnack.com for further support on vulnerability remediation.

References

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