CVE-2021-29525: Division by 0 in `Conv2DBackpropInput`

Impact An attacker can trigger a division by 0 in tf.rawops.Conv2DBackpropInput:

python import tensorflow as tf

inputtensor = tf.constant([52, 1, 1, 5], shape=[4], dtype=tf.int32) filtertensor = tf.constant([], shape=[0, 1, 5, 0], dtype=tf.float32) outbackprop = tf.constant([], shape=[52, 1, 1, 0], dtype=tf.float32)

tf.rawops.Conv2DBackpropInput(inputsizes=inputtensor, filter=filtertensor, outbackprop=outbackprop, strides=[1, 1, 1, 1], usecudnnongpu=True, padding='SAME', explicitpaddings=[], dataformat='NHWC', dilations=[1, 1, 1, 1]) This is because the implementation does a division by a quantity that is controlled by the caller:

cc const sizet sizeA = outputimagesize dims.outdepth; const sizet sizeB = filtertotalsize dims.outdepth; const sizet sizeC = outputimagesize filtertotalsize; const sizet workunitsize = sizeA + sizeB + sizeC; ... const sizet shardsize = useparallelcontraction ? 1 : (targetworkingsetsize + workunitsize - 1) / workunitsize;

Patches We have patched the issue in GitHub commit 2be2cdf3a123e231b16f766aa0e27d56b4606535.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Yakun Zhang and Ying Wang of Baidu X-Team.