CVE-2020-15199: TensorFlow - Invalid Ragged Tensor Validation

tensorflow Updated 02 Oct 2025

Summary

06/03/2024

Disclosed on

CVE-2020-15199

CVE

Yes i

Fix available

Medium

Severity

Not published

CVSS Score

CVE-2020-15199: Denial of Service in Tensorflow. Affects tensorflow versions >= 2.3.0, < 2.3.1. Fixed in 2.3.1. How to fix ↓

Also tracked for

tensorflow · PyPI
tensorflow-cpu · PyPI
tensorflow-gpu · PyPI
tensorflow · PyPI

The same CVE-2020-15199 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 developed by Google. It provides a comprehensive ecosystem of tools, libraries, and resources for building and deploying machine learning models. TensorFlow allows users to create and manipulate tensors, which are multi-dimensional arrays used as the fundamental data structure in machine learning algorithms. The RaggedCountSparseOutput is a feature in TensorFlow that counts the number of non-zero elements in a ragged tensor, which is a tensor with variable-length dimensions. This vulnerability affects TensorFlow versions prior to 2.3.1.

Vulnerability Detail

The vulnerability in TensorFlow arises from the lack of validation in the RaggedCountSparseOutput function. Specifically, it fails to check if the input arguments form a valid ragged tensor, particularly the splits tensor. The splits tensor must have a minimum required number of elements, but this is not validated. Consequently, when initializing the BatchedMap data structure, which is equivalent to a vector, a SIGABRT signal is raised by the operating system if the splits tensor is empty or has only one element. This vulnerability can potentially lead to a denial-of-service (DoS) scenario. The issue has been patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

How To Fix

Minimum fixed version

2.3.1

This is the first release that contains the fix.

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Workaround

There is no known workaround for this vulnerability. It is recommended to apply the fix mentioned above.

Conclusion

The vulnerability in TensorFlow versions prior to 2.3.1 allows for a denial-of-service (DoS) scenario due to the lack of validation in the RaggedCountSparseOutput function. Users should upgrade to TensorFlow 2.3.1 or later to mitigate this vulnerability. For further support on vulnerability remediation, please contact DevNack.com.

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