CVE-2020-15196: Heap buffer overflow in Tensorflow

Published Sep 25, 2020
·
Updated

Impact The SparseCountSparseOutput and RaggedCountSparseOutput implementations don't validate that the weights tensor has the same shape as the data. The check exists for DenseCountSparseOutput, where both tensors are fully specified: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/countops.cc#L110-L117

In the sparse and ragged count weights are still accessed in parallel with the data: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/countops.cc#L199-L201

But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights.

Patches We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.

We recommend users to upgrade to TensorFlow 2.3.1.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability is a variant of GHSA-p5f8-gfw5-33w4

Other sources

In Tensorflow version 2.3.0, the SparseCountSparseOutput and RaggedCountSparseOutput implementations don't validate that the weights tensor has the same shape as the data. The check exists for DenseCountSparseOutput, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

Affected Software

4 affected componentsFixes available
pip/tensorflow-gpu=2.3.0
2.3.1
pip/tensorflow-cpu=2.3.0
2.3.1
pip/tensorflow=2.3.0
2.3.1
Google TensorFlow=2.3.0

Event History

Sep 25, 2020
Advisory Published
via GitHub·06:28 PM
CVE Published
via MITRE·06:40 PM
Data Sourced
via MITRE·06:40 PM
DescriptionSeverityWeakness
Data Sourced
via NVD·07:15 PM
RemedyDescriptionSeverityWeaknessAffected Software

Frequently Asked Questions

1

What is the severity of CVE-2020-15196?

CVE-2020-15196 is classified as a medium severity vulnerability due to improper validation of tensor shapes in TensorFlow.

2

How do I fix CVE-2020-15196?

To fix CVE-2020-15196, upgrade to TensorFlow version 2.3.1 or later.

3

What are the affected software versions for CVE-2020-15196?

CVE-2020-15196 affects TensorFlow version 2.3.0 across various distributions including tensorflow-gpu and tensorflow-cpu packages.

4

What components of TensorFlow are impacted by CVE-2020-15196?

CVE-2020-15196 impacts the SparseCountSparseOutput and RaggedCountSparseOutput implementations of TensorFlow.

5

Is CVE-2020-15196 a common vulnerability?

CVE-2020-15196 is a known vulnerability within TensorFlow, but its prevalence will depend on the use of affected versions in projects.

Contact

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