Heap out of bounds access in sparse reduction operations
### Impact
The implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data:
```python
import tensorflow as tf
x = tf.SparseTensor(
indices=[[773, 773, 773], [773, 773, 773]],
values=[1, 1],
dense_shape=[337, 337, 337])
tf.sparse.reduce_sum(x, 1)
```
The implementation
https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_reduce_op.cc#L217-L228 fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor.
### Patches
We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750
https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750.
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.