FPE in LSH in TFLite
Moderate severity
GitHub Reviewed
Published
Aug 11, 2021
in
tensorflow/tensorflow
•
Updated Nov 13, 2024
Description
Published by the National Vulnerability Database
Aug 12, 2021
Reviewed
Aug 24, 2021
Published to the GitHub Advisory Database
Aug 25, 2021
Last updated
Nov 13, 2024
Impact
An attacker can craft a TFLite model that would trigger a division by zero error in LSH implementation.
There is no check that the first dimension of the input is non zero.
Patches
We have patched the issue in GitHub commit 0575b640091680cfb70f4dd93e70658de43b94f9.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick thiscommit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
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 reported by Yakun Zhang of Baidu Security.
References