CVE-2020-15192: Memory leak in Tensorflow

Published Sep 25, 2020
·
Updated

Impact If a user passes a list of strings to dlpack.todlpack there is a memory leak following an expected validation failure: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L100-L104

The allocated memory is from https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L256

The issue occurs because the status argument during validation failures is not properly checked: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L265-L267

Since each of the above methods can return an error status, the status value must be checked before continuing.

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 a list of strings to dlpack.todlpack there is a memory leak following an expected validation failure. The issue occurs because the status argument during validation failures is not properly checked. Since each of the above methods can return an error status, the status value must be checked before continuing. 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·06:40 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-2020-15192?

CVE-2020-15192 is classified as having a medium severity due to the potential for memory leaks.

2

How do I fix CVE-2020-15192?

To mitigate CVE-2020-15192, upgrade TensorFlow to version 2.3.1 or later.

3

What versions of TensorFlow are affected by CVE-2020-15192?

CVE-2020-15192 affects TensorFlow versions 2.3.0 and 2.2.0.

4

Can CVE-2020-15192 be exploited remotely?

CVE-2020-15192 is not a remotely exploitable vulnerability as it requires the user to pass malicious input.

5

What software packages are impacted by CVE-2020-15192?

CVE-2020-15192 impacts the tensorflow, tensorflow-gpu, and tensorflow-cpu packages.

Contact

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