Heap buffer overflow in Tensorflow

Overview

Impact

The RaggedCountSparseOutput implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the splits tensor generate a valid partitioning of the values tensor. Hence, this code is prone to heap buffer overflow: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L248-L251

If split_values does not end with a value at least num_values then the while loop condition will trigger a read outside of the bounds of split_values once batch_idx grows too large.

Patches

We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.

We recommend users to upgrade to TensorFlow 2.3.1.

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 >= 2.3.0, < 2.3.1
• tensorflow-cpu >= 2.3.0, < 2.3.1
• tensorflow-gpu >= 2.3.0, < 2.3.1

Remediation

Upgrade to a patched release:

• tensorflow 2.3.1
• tensorflow-cpu 2.3.1
• tensorflow-gpu 2.3.1