Null pointer dereference in `SparseTensorSliceDataset`

Impact

When a user does not supply arguments that determine a valid sparse tensor, tf.raw_ops.SparseTensorSliceDataset implementation can be made to dereference a null pointer:

import tensorflow as tf

tf.raw_ops.SparseTensorSliceDataset(
  indices=[[],[],[]],
  values=[1,2,3],
  dense_shape=[3,3])

The implementation has some argument validation but fails to consider the case when either indices or values are provided for an empty sparse tensor when the other is not.

If indices is empty (as in the example above), then code that performs validation (i.e., checking that the indices are monotonically increasing) results in a null pointer dereference:

    for (int64_t i = 0; i < indices->dim_size(0); ++i) {
      int64_t next_batch_index = indices->matrix<int64>()(i, 0);
      ...
    }

If indices as provided by the user is empty, then indices in the C++ code above is backed by an empty std::vector, hence calling indices->dim_size(0) results in null pointer dereferencing (same as calling std::vector::at() on an empty vector).

Patches

We have patched the issue in GitHub commit 02cc160e29d20631de3859c6653184e3f876b9d7.

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.