Null pointer dereference and heap OOB read in operations restoring tensors

Impact

When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer:

import tensorflow as tf

tf.raw_ops.Restore(
  file_pattern=['/tmp'],
  tensor_name=[], 
  default_value=21,
  dt=tf.int,
  preferred_shard=1)

The same undefined behavior can be triggered by tf.raw_ops.RestoreSlice:

import tensorflow as tf

tf.raw_ops.RestoreSlice(
  file_pattern=['/tmp'],
  tensor_name=[], 
  shape_and_slice='2',
  dt=inp.array([tf.int]),
  preferred_shard=1)

Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration:

import tensorflow as tf

tf.raw_ops.Restore(
  file_pattern=['/tmp'],
  tensor_name=['x'], 
  default_value=21,
  dt=tf.int,
  preferred_shard=42)

The implementation retrieves the tensor list corresponding to the tensor_name user controlled input and immediately retrieves the tensor at the restoration index (controlled via preferred_shard argument). This occurs without validating that the provided list has enough values.

If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read.

Patches

We have patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622.

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 members of the Aivul Team from Qihoo 360.