Memory leak in Tensorflow

Overview

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.

Affected packages

• tensorflow >= 2.2.0, < 2.2.1
• tensorflow >= 2.3.0, < 2.3.1
• tensorflow-cpu >= 2.2.0, < 2.2.1
• tensorflow-cpu >= 2.3.0, < 2.3.1
• tensorflow-gpu >= 2.2.0, < 2.2.1
• tensorflow-gpu >= 2.3.0, < 2.3.1

Remediation

Upgrade to a patched release:

• tensorflow 2.2.1
• tensorflow 2.3.1
• tensorflow-cpu 2.2.1
• tensorflow-cpu 2.3.1
• tensorflow-gpu 2.2.1
• tensorflow-gpu 2.3.1