Overflow/crash in `tf.range`

Overview

Impact

While calculating the size of the output within the tf.range kernel, there is a conditional statement of type int64 = condition ? int64 : double. Due to C++ implicit conversion rules, both branches of the condition will be cast to double and the result would be truncated before the assignment. This result in overflows:

import tensorflow as tf

tf.sparse.eye(num_rows=9223372036854775807, num_columns=None)

Similarly, tf.range would result in crashes due to overflows if the start or end point are too large.

import tensorflow as tf

tf.range(start=-1e+38, limit=1)

Patches

We have patched the issue in GitHub commits 6d94002a09711d297dbba90390d5482b76113899 (merging #51359) and 1b0e0ec27e7895b9985076eab32445026ae5ca94 (merging #51711).

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 externally via GitHub issue, GitHub issue and GitHub issue.

Affected packages

• tensorflow >= 2.6.0, < 2.6.1
• tensorflow >= 2.5.0, < 2.5.2
• tensorflow < 2.4.4
• tensorflow-cpu >= 2.6.0, < 2.6.1
• tensorflow-cpu >= 2.5.0, < 2.5.2
• tensorflow-cpu < 2.4.4
• tensorflow-gpu >= 2.6.0, < 2.6.1
• tensorflow-gpu >= 2.5.0, < 2.5.2
• tensorflow-gpu < 2.4.4

Remediation

Upgrade to a patched release:

• tensorflow 2.6.1
• tensorflow 2.5.2
• tensorflow 2.4.4
• tensorflow-cpu 2.6.1
• tensorflow-cpu 2.5.2
• tensorflow-cpu 2.4.4
• tensorflow-gpu 2.6.1
• tensorflow-gpu 2.5.2
• tensorflow-gpu 2.4.4