QID 981516

Security update has been released for tensorflow-gpu,tensorflow,tensorflow-cpu to fix the vulnerability.

Note: The preceding description block is extracted directly from the security advisory. Using automation, we have attempted to clean and format it as much as possible without introducing additional issues.

When a user does not supply arguments that determine a valid sparse tensor, `tf.raw_ops.SparseTensorSliceDataset` implementation can be made to dereference a null pointer:

```python
import tensorflow as tf

tf.raw_ops.SparseTensorSliceDataset(
indices=[[],[],[]],
values=[1,2,3],
dense_shape=[3,3])
```

The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L240-L251) has some argument validation but fails to consider the case when either `indices` or `values` are provided for an empty sparse tensor when the other is not.

If `indices` is empty (as in the example above), then [code that performs validation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L260-L261) (i.e., checking that the indices are monotonically increasing) results in a null pointer dereference:

```cc
for (int64_t i = 0; i < indices->dim_size(0); ++i) {
int64_t next_batch_index = indices->matrix<int64>()(i, 0);
...
}
```

If `indices` as provided by the user is empty, then `indices` in the C++ code above is backed by an empty `std::vector`, hence calling `indices->dim_size(0)` results in null pointer dereferencing (same as calling `std::vector::at()` on an empty vector).