GHSA-v7vw-577f-vp8x TensorFlow vulnerable to segfault in `QuantizedRelu` and `QuantizedRelu6`

• PyPI tensorflow: < 2.7.2 → fixed in 2.7.2 • PyPI tensorflow: >= 2.8.0, < 2.8.1 → fixed in 2.8.1 • PyPI tensorflow: >= 2.9.0, < 2.9.1 → fixed in 2.9.1 • PyPI tensorflow-cpu: < 2.7.2 → fixed in 2.7.2 • PyPI tensorflow-cpu: >= 2.8.0, < 2.8.1 → fixed in 2.8.1 • PyPI tensorflow-cpu: >= 2.9.0, < 2.9.1 → fixed in 2.9.1 • PyPI tensorflow-gpu: < 2.7.2 → fixed in 2.7.2 • PyPI tensorflow-gpu: >= 2.8.0, < 2.8.1 → fixed in 2.8.1 • PyPI tensorflow-gpu: >= 2.9.0, < 2.9.1 → fixed in 2.9.1

Impact If `QuantizedRelu` or `QuantizedRelu6` are given nonscalar inputs for `min_features` or `max_features`, it results in a segfault that can be used to trigger a denial of service attack. ```python import tensorflow as tf out_type = tf.quint8 features = tf.constant(28, shape=[4,2], dtype=tf.quint8) min_features = tf.constant([], shape=[0], dtype=tf.float32) max_features = tf.constant(-128, shape=[1], dtype=tf.float32) tf.raw_ops.QuantizedRelu(features=features, min_features=min_features, max_features=max_features, out_type=out_type) tf.raw_ops.QuantizedRelu6(features=features, min_features=min_features, max_features=max_features, out_type=out_type) ``` Patches We have patched the issue in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89. 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 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.