Heap OOB in `RaggedGather`

Overview

Impact

If the arguments to tf.raw_ops.RaggedGather don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers.

import tensorflow as tf

tf.raw_ops.RaggedGather(
  params_nested_splits = [0,0,0],
  params_dense_values = [1,1],
  indices = [0,0,9,0,0],
  OUTPUT_RAGGED_RANK=0)

In debug mode, the same code triggers a CHECK failure.

The implementation directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by params_nested_splits is not an empty list of tensors.

Patches

We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373.

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