Memory corruption in `DrawBoundingBoxesV2`
Moderate severity
GitHub Reviewed
Published
May 13, 2021
in
tensorflow/tensorflow
•
Updated Feb 1, 2023
Package
Affected versions
< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
Patched versions
2.1.4
2.2.3
2.3.3
2.4.2
< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
2.1.4
2.2.3
2.3.3
2.4.2
< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
2.1.4
2.2.3
2.3.3
2.4.2
Description
Published by the National Vulnerability Database
May 14, 2021
Reviewed
May 18, 2021
Published to the GitHub Advisory Database
May 21, 2021
Last updated
Feb 1, 2023
Impact
The implementation of
tf.raw_ops.MaxPoolGradWithArgmax
can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs:The implementation assumes that the last element of
boxes
input is 4, as required by the op. Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption:If the last dimension in
boxes
is less than 4, accesses similar totboxes(b, bb, 3)
will access data outside of bounds. Further during code execution there are also writes to these indices.Patches
We have patched the issue in GitHub commit 79865b542f9ffdc9caeb255631f7c56f1d4b6517.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 and Ying Wang of Baidu X-Team.
References