Missing validation in shape inference for `Dequantize`
### Impact
The shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments:
```python
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()
tf.raw_ops.Dequantize(
input_tensor = tf.constant(-10.0, dtype=tf.float32),
input_tensor = tf.cast(input_tensor, dtype=tf.quint8),
min_range = tf.constant([], shape=[0], dtype=tf.float32),
max_range = tf.constant([], shape=[0], dtype=tf.float32),
mode = 'MIN_COMBINED',
narrow_range=False,
axis=-10,
dtype=tf.dtypes.float32)
```
The shape inference implementation
https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014 uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values.
### Patches
We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764
https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764.
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 Yakun Zhang of Baidu Security.