Memory leak in Tensorflow
Moderate severity
GitHub Reviewed
Published
Sep 24, 2020
in
tensorflow/tensorflow
•
Updated Oct 28, 2024
Description
Reviewed
Sep 25, 2020
Published to the GitHub Advisory Database
Sep 25, 2020
Published by the National Vulnerability Database
Sep 25, 2020
Last updated
Oct 28, 2024
Impact
If a user passes a list of strings to
dlpack.to_dlpack
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.
References