Heap buffer overflow in `FractionalAvgPoolGrad`
### Impact
The implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers:
```python
import tensorflow as tf
tf.raw_ops.FractionalAvgPoolGrad(
orig_input_tensor_shape=[0,1,2,3],
out_backprop = np.array([[[[541],[541]],[[541],[541]]]]),
row_pooling_sequence=[0, 0, 0, 0, 0],
col_pooling_sequence=[-2, 0, 0, 2, 0],
overlapping=True)
```
The implementation
https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205 does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area.
### Patches
We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30
https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 members of the Aivul Team from Qihoo 360.