Advisory Package Curation

TensorFlow vulnerable to segfault in `QuantizeDownAndShrinkRange` ### Impact If `QuantizeDownAndShrinkRange` is given nonscalar inputs for `input_min` or `input_max`, 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 input = tf.constant([1], shape=[3], dtype=tf.qint32) input_min = tf.constant([], shape=[0], dtype=tf.float32) input_max = tf.constant(-256, shape=[1], dtype=tf.float32) tf.raw_ops.QuantizeDownAndShrinkRange(input=input, input_min=input_min, input_max=input_max, out_type=out_type) ``` ### Patches We have patched the issue in GitHub commit 73ad1815ebcfeb7c051f9c2f7ab5024380ca8613 https://github.com/tensorflow/tensorflow/commit/73ad1815ebcfeb7c051f9c2f7ab5024380ca8613. 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.

Improper Input Validation TensorFlow is an open source platform for machine learning. If `QuantizeDownAndShrinkRange` is given nonscalar inputs for `input_min` or `input_max`, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 73ad1815ebcfeb7c051f9c2f7ab5024380ca8613. 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. There are no known workarounds for this issue.

TensorFlow vulnerable to segfault in `QuantizeDownAndShrinkRange` ### Impact If `QuantizeDownAndShrinkRange` is given nonscalar inputs for `input_min` or `input_max`, 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 input = tf.constant([1], shape=[3], dtype=tf.qint32) input_min = tf.constant([], shape=[0], dtype=tf.float32) input_max = tf.constant(-256, shape=[1], dtype=tf.float32) tf.raw_ops.QuantizeDownAndShrinkRange(input=input, input_min=input_min, input_max=input_max, out_type=out_type) ``` ### Patches We have patched the issue in GitHub commit 73ad1815ebcfeb7c051f9c2f7ab5024380ca8613 https://github.com/tensorflow/tensorflow/commit/73ad1815ebcfeb7c051f9c2f7ab5024380ca8613. 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.

TensorFlow vulnerable to segfault in `QuantizeDownAndShrinkRange` ### Impact If `QuantizeDownAndShrinkRange` is given nonscalar inputs for `input_min` or `input_max`, 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 input = tf.constant([1], shape=[3], dtype=tf.qint32) input_min = tf.constant([], shape=[0], dtype=tf.float32) input_max = tf.constant(-256, shape=[1], dtype=tf.float32) tf.raw_ops.QuantizeDownAndShrinkRange(input=input, input_min=input_min, input_max=input_max, out_type=out_type) ``` ### Patches We have patched the issue in GitHub commit 73ad1815ebcfeb7c051f9c2f7ab5024380ca8613 https://github.com/tensorflow/tensorflow/commit/73ad1815ebcfeb7c051f9c2f7ab5024380ca8613. 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.