CVE-2020-15194: TensorFlow Incomplete Validation of Arguments in SparseFillEmptyRowsGrad

tensorflow Updated 02 Oct 2025

Summary

25/09/2020

Disclosed on

CVE-2020-15194

CVE

Yes i

Fix available

Moderate

Severity

5.3

CVSS Score

CVE-2020-15194: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the SparseFillEmptyRowsGrad implementation has incomplete validation of the shapes of its arguments. Affects tensorflow 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 < 390611e0d45c5793c7066110af37c8514e6a6c54

Fixed in 5 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-2020-15194 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 open-source machine learning framework that provides a comprehensive ecosystem of tools, libraries, and community resources. It is widely used for developing and deploying machine learning models. The vulnerability described here affects TensorFlow versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1. TensorFlow's SparseFillEmptyRowsGrad implementation lacks proper validation of the shapes of its arguments, specifically reverse_index_map_t and grad_values_t. This vulnerability can be exploited by malicious users to trigger an assertion failure in vec, leading to denial of service in serving installations.

Vulnerability Detail

The incomplete validation of arguments in TensorFlow's SparseFillEmptyRowsGrad implementation allows for the passing of a bad grad_values_t argument, triggering an assertion failure in vec. This can result in denial of service in serving installations. While reverse_index_map_t is properly validated, grad_values_t is not, enabling attackers to exploit this vulnerability. The issue has been patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54 and is addressed in 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==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==1.15.4"

>= 2.0.0, < 2.0.3 2.0.3

pip install --upgrade "tensorflow==2.0.3"

>= 2.1.0, < 2.1.2 2.1.2

pip install --upgrade "tensorflow==2.1.2"

>= 2.2.0, < 2.2.1 2.2.1

pip install --upgrade "tensorflow==2.2.1"

>= 2.3.0, < 2.3.1 2.3.1

pip install --upgrade "tensorflow==2.3.1"

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Workaround

There is no known workaround for this vulnerability. It is recommended to upgrade to the patched versions of TensorFlow as soon as possible.

Conclusion

The vulnerability in TensorFlow's SparseFillEmptyRowsGrad implementation can be exploited by passing a bad argument, leading to denial of service. Users should upgrade to the patched versions of TensorFlow to mitigate this vulnerability. For further support on vulnerability remediation, please contact DevNack.com.

See every vulnerability recorded for tensorflow →

FAQ

What is TensorFlow?

TensorFlow is an open-source machine learning framework used for developing and deploying machine learning models.

What versions of TensorFlow are affected by this vulnerability?

TensorFlow versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 are affected.

How can this vulnerability be exploited?

Malicious users can pass a bad grad_values_t argument to trigger an assertion failure in vec, causing denial of service in serving installations.

Has this vulnerability been patched?

Yes, the vulnerability has been patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54 and is addressed in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1.

Is there a workaround for this vulnerability?

No, there is no known workaround. It is recommended to upgrade to the patched versions of TensorFlow.

References

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