Assertion failure based denial of service in Tensorflow

Overview

Impact

The implementation of *Bincount operations allows malicious users to cause denial of service by passing in arguments which would trigger a CHECK-fail:

import tensorflow as tf

tf.raw_ops.DenseBincount(
  input=[[0], [1], [2]],
  size=[1],
  weights=[3,2,1],
  binary_output=False)

There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in CHECK failures later when the output tensors get allocated.

Patches

We have patched the issue in GitHub commit 7019ce4f68925fd01cdafde26f8d8c938f47e6f9.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Faysal Hossain Shezan from University of Virginia.

Affected packages

• tensorflow < 2.5.3
• tensorflow >= 2.6.0, < 2.6.3
• tensorflow >= 2.7.0, < 2.7.1
• tensorflow-cpu < 2.5.3
• tensorflow-cpu >= 2.6.0, < 2.6.3
• tensorflow-cpu >= 2.7.0, < 2.7.1
• tensorflow-gpu < 2.5.3
• tensorflow-gpu >= 2.6.0, < 2.6.3
• tensorflow-gpu >= 2.7.0, < 2.7.1

Remediation

Upgrade to a patched release:

• tensorflow 2.5.3
• tensorflow 2.6.3
• tensorflow 2.7.1
• tensorflow-cpu 2.5.3
• tensorflow-cpu 2.6.3
• tensorflow-cpu 2.7.1
• tensorflow-gpu 2.5.3
• tensorflow-gpu 2.6.3
• tensorflow-gpu 2.7.1