Heap OOB in `RaggedGather`
### Impact
If the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers.
```python
import tensorflow as tf
tf.raw_ops.RaggedGather(
params_nested_splits = [0,0,0],
params_dense_values = [1,1],
indices = [0,0,9,0,0],
OUTPUT_RAGGED_RANK=0)
```
In debug mode, the same code triggers a `CHECK` failure.
The implementation
https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70 directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors.
### Patches
We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373
https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373.
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.