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

tensorflow-gpu Updated 02 Oct 2025

Summary

25/09/2020

Disclosed on

CVE-2020-15194

CVE

Yes i

Fix available

Unrated

Severity

Not published

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-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 < 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 · PyPI
tensorflow-cpu · PyPI
tensorflow · 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.

Free scan

Check if this vulnerability affects your application

Upload a manifest or SBOM and we'll show whether you're affected, plus every other risky package in the same scan.

Check my app free

No signup required · Results in about 60 seconds

Upload your dependency manifest or SBOM

Drag & drop your file here or Browse

Java Javascript PHP Python Go Rust Ruby Dart C/C++ Elixir CycloneDX SPDX

Upload a supported dependency manifest or SBOM. No installation or source-code access required.

See supported files and SBOM formats

SBOM sbom.json · bom.json · bom.xml · *.spdx.json · *.cdx.json · *.cdx.xml

Java pom.xml · gradle.lockfile · buildscript-gradle.lockfile

Javascript package-lock.json · yarn.lock · pnpm-lock.yaml

PHP composer.lock

Python poetry.lock · requirements.txt · Pipfile.lock

Go go.mod

Rust Cargo.lock

Ruby Gemfile.lock

Dart pubspec.lock

C/C++ conan.lock

Elixir mix.lock

Enter your work email to receive your report link.

Verify your email to view the full report and start 30 days of continuous monitoring. No credit card required.

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 utilizes the power of GPUs to accelerate the training and inference processes, making it suitable for large-scale deep learning applications. However, a vulnerability has been discovered in versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 of TensorFlow-GPU.

Vulnerability Detail

The vulnerability, identified as CVE-2020-15194, resides in the SparseFillEmptyRowsGrad implementation of TensorFlow-GPU. This implementation lacks complete validation of the shapes of its arguments. While reverse_index_map_t and grad_values_t are accessed in a similar pattern, only reverse_index_map_t is properly validated for its shape. This oversight allows malicious users to pass a malformed grad_values_t argument, triggering an assertion failure in vec. As a result, serving installations of TensorFlow-GPU can be disrupted, leading to a denial of service (DoS) condition. 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.

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

No workaround has been provided for this vulnerability. It is strongly advised to apply the patch or update to the latest patched version of TensorFlow-GPU as soon as possible.

Conclusion

In conclusion, the vulnerability (CVE-2020-15194) in TensorFlow-GPU's SparseFillEmptyRowsGrad implementation poses a risk of denial of service (DoS) attacks on serving installations. It is crucial to update TensorFlow-GPU to one of the patched versions (1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1) to mitigate this vulnerability. For further support on vulnerability remediation, you can reach out to 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 leverages the power of GPUs to accelerate the training and inference processes for machine learning models.

What is the impact of this vulnerability?

The vulnerability in TensorFlow-GPU can be exploited by malicious users to trigger a denial of service (DoS) condition in serving installations. This can disrupt the availability of TensorFlow-GPU and impact the functionality of machine learning applications relying on it.

How can I check if my TensorFlow-GPU installation is affected?

You can use the Vulert Playground at Vulert Scanner to check if your TensorFlow-GPU installation is affected by this vulnerability. Simply provide your Pipfile.lock file, and Vulert will analyze it for any open-source vulnerabilities, including this one.

Is there a workaround available for this vulnerability?

No workaround has been provided for this vulnerability. It is recommended to apply the patch or update to the latest patched version of TensorFlow-GPU to mitigate the risk.

Where can I find more information about this vulnerability?

For more information about this vulnerability, you can refer to the official GitHub advisory database for TensorFlow-GPU at https://github.com/pypa/advisory-database/blob/main/vulns/tensorflow-gpu/PYSEC-2020-309.yaml.

References

Related vulnerabilities

Recent vulnerabilities recorded for tensorflow-gpu.

View all tensorflow-gpu vulnerabilities →