CVE-2020-15210: TensorFlow Lite Memory Corruption Vulnerability

tensorflow-cpu 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-cpu 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-gpu · 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 Lite is an open-source deep learning framework designed for mobile and embedded devices. It provides a lightweight solution for running machine learning models on resource-constrained platforms. TensorFlow Lite is widely used in various applications, including image recognition, natural language processing, and robotics. However, versions prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 are vulnerable to a memory corruption issue.

Vulnerability Detail

The vulnerability 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. An attacker could potentially exploit this vulnerability to execute arbitrary code, cause a denial of service, or gain unauthorized access to sensitive information. The issue has been addressed in the latest patch releases for TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1.

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.

< 1.15.4 1.15.4

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

>= 2.0.0, < 2.0.3 2.0.3

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

>= 2.1.0, < 2.1.2 2.1.2

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

>= 2.2.0, < 2.2.1 2.2.1

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

>= 2.3.0, < 2.3.1 2.3.1

pip install --upgrade "tensorflow-cpu==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 known workaround for this vulnerability. It is crucial to apply the recommended patch or upgrade to the fixed versions of TensorFlow to mitigate the risk.

Conclusion

The TensorFlow Lite memory corruption vulnerability (CVE-2020-15210) poses a significant risk to applications utilizing affected versions of TensorFlow. To ensure the security and stability of your system, it is highly recommended to upgrade to the patched versions or apply the provided fix. For further assistance or support in vulnerability remediation, please contact DevNack.com.

See every vulnerability recorded for tensorflow-cpu →

FAQ

What is TensorFlow Lite?

What versions of TensorFlow Lite are affected?

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

What are the potential consequences of this vulnerability?

The vulnerability can lead to memory corruption, segmentation faults, arbitrary code execution, denial of service, or unauthorized access to sensitive information.

How can I fix this vulnerability?

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

Is there a workaround available?

There is no known workaround for this vulnerability. It is crucial to apply the recommended patch or upgrade to the fixed versions of TensorFlow to mitigate the risk.

References

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