Heap buffer overflow in `FractionalAvgPoolGrad`

Impact

The implementation for tf.raw_ops.FractionalAvgPoolGrad can be tricked into accessing data outside of bounds of heap allocated buffers:

import tensorflow as tf

tf.raw_ops.FractionalAvgPoolGrad(
  orig_input_tensor_shape=[0,1,2,3],
  out_backprop = np.array([[[[541],[541]],[[541],[541]]]]),
  row_pooling_sequence=[0, 0, 0, 0, 0],
  col_pooling_sequence=[-2, 0, 0, 2, 0],
  overlapping=True)

The implementation does not validate that the input tensor is non-empty. Thus, code constructs an empty EigenDoubleMatrixMap and then accesses this buffer with indices that are outside of the empty area.

Patches

We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30.

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.