CVE-2020-15191: Undefined behavior in Tensorflow

Published Sep 25, 2020
·
Updated

Impact If a user passes an invalid argument to dlpack.todlpack the expected validations will cause variables to bind to nullptr while setting a status variable to the error condition.

However, this status argument is not properly checked: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L265-L267

Hence, code following these methods will bind references to null pointers: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L279-L285

This is undefined behavior and reported as an error if compiling with -fsanitize=null.

Patches We have patched the issue in 22e07fb204386768e5bcbea563641ea11f96ceb8 and will release a patch release for all affected versions.

We recommend users to upgrade to TensorFlow 2.2.1 or 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 has been discovered during variant analysis of GHSA-rjjg-hgv6-h69v.

Other sources

In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to dlpack.todlpack the expected validations will cause variables to bind to nullptr while setting a status variable to the error condition. However, this status argument is not properly checked. Hence, code following these methods will bind references to null pointers. This is undefined behavior and reported as an error if compiling with -fsanitize=null. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1.

Affected Software

9 affected componentsFixes available
pip/tensorflow-gpu=2.3.0
2.3.1
pip/tensorflow-gpu=2.2.0
2.2.1
pip/tensorflow-cpu=2.3.0
2.3.1
pip/tensorflow-cpu=2.2.0
2.2.1
pip/tensorflow=2.3.0
2.3.1
pip/tensorflow=2.2.0
2.2.1
Google TensorFlow=2.2.0
Google TensorFlow=2.3.0
openSUSE Leap=15.2

Event History

Sep 25, 2020
Advisory Published
via GitHub·06:28 PM
CVE Published
via MITRE·06:41 PM
Data Sourced
via MITRE·06:41 PM
DescriptionSeverityWeakness
Data Sourced
via NVD·07:15 PM
RemedyDescriptionSeverityWeaknessAffected Software

Frequently Asked Questions

1

What is the severity of CVE-2020-15191?

CVE-2020-15191 is classified as a medium severity vulnerability.

2

How do I fix CVE-2020-15191?

To fix CVE-2020-15191, upgrade to TensorFlow versions 2.3.1 or 2.2.1, depending on your package.

3

What are the affected versions in CVE-2020-15191?

The affected versions in CVE-2020-15191 include TensorFlow 2.2.0 and 2.3.0.

4

What software is affected by CVE-2020-15191?

CVE-2020-15191 affects TensorFlow, specifically the tensorflow-gpu and tensorflow-cpu packages.

5

What happens if I don't address CVE-2020-15191?

If CVE-2020-15191 is not addressed, it may lead to potential application instability due to improper error handling.

Contact

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