CVE-2020-15210: TensorFlow-GPU - Memory Corruption Vulnerability

tensorflow-gpu Updated 02 Oct 2025

Summary

25/09/2020

Disclosed on

CVE-2020-15210

CVE

Yes i

Fix available

Unrated

Severity

Not published

CVSS Score

CVE-2020-15210: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentati... Affects tensorflow-gpu across 6 version ranges.

< 1.15.4 >= 2.0.0, < 2.0.3 >= 2.1.0, < 2.1.2 >= 2.2.0, < 2.2.1 >= 2.3.0, < 2.3.1 < d58c96946b2880991d63d1dacacb32f0a4dfa453

Fixed in 5 releases; the minimum fixed version depends on the release line you run. How to fix ↓

Also tracked for

tensorflow · Bitnami
tensorflow · PyPI
tensorflow-cpu · PyPI
tensorflow · PyPI

The same CVE-2020-15210 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-GPU is an open-source machine learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and resources for building and deploying machine learning models. TensorFlow-GPU leverages the power of GPUs to accelerate computation, making it suitable for training and inference tasks that require high performance. However, a vulnerability has been discovered in versions prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 of TensorFlow-GPU.

Vulnerability Detail

The vulnerability (CVE-2020-15210) arises when a TFLite saved model uses the same tensor as both input and output of an operator. Depending on the specific operator, this can lead to a segmentation fault or memory corruption. The issue has been patched in the latest releases of TensorFlow-GPU. If left unaddressed, this vulnerability could potentially be exploited by an attacker to cause a denial-of-service (DoS) condition or execute arbitrary code.

How To Fix

Latest published version: 2.12.0 as of 22 Oct 2026

pip install --upgrade "tensorflow-gpu==2.12.0"

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.

< 1.15.4 1.15.4

pip install --upgrade "tensorflow-gpu==1.15.4"

>= 2.0.0, < 2.0.3 2.0.3

pip install --upgrade "tensorflow-gpu==2.0.3"

>= 2.1.0, < 2.1.2 2.1.2

pip install --upgrade "tensorflow-gpu==2.1.2"

>= 2.2.0, < 2.2.1 2.2.1

pip install --upgrade "tensorflow-gpu==2.2.1"

>= 2.3.0, < 2.3.1 2.3.1

pip install --upgrade "tensorflow-gpu==2.3.1"

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 mentioned for this vulnerability. The recommended approach is to upgrade to the patched versions of TensorFlow-GPU.

Conclusion

The memory corruption vulnerability in TensorFlow-GPU (CVE-2020-15210) can have serious implications if left unaddressed. It is crucial for users to upgrade to the latest patched versions of TensorFlow-GPU to mitigate the risk of exploitation. For further support and assistance in vulnerability remediation, please contact DevNack.com.

See every vulnerability recorded for tensorflow-gpu →

FAQ

What is TensorFlow-GPU?

TensorFlow-GPU is an open-source machine learning framework developed by Google. It provides tools and resources for building and deploying machine learning models using GPUs for accelerated computation.

What is the impact of this vulnerability?

If exploited, this vulnerability can lead to a segmentation fault or memory corruption, potentially resulting in a denial-of-service (DoS) condition or arbitrary code execution.

Which versions of TensorFlow-GPU are affected?

Versions prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 of TensorFlow-GPU are affected by this vulnerability.

How can I fix this vulnerability?

To fix this vulnerability, it is recommended to upgrade to TensorFlow-GPU versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. Follow the upgrade instructions provided by the TensorFlow-GPU documentation.

Is there a workaround available?

There is no specific workaround mentioned for this vulnerability. Upgrading to the patched versions is the recommended solution.

References

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