CVE-2020-15205: Data leak in Tensorflow
Impact The datasplits argument of tf.rawops.StringNGrams lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory python >> tf.rawops.StringNGrams(data=["aa", "bb", "cc", "dd", "ee", "ff"], datasplits=[0,8], separator=" ", ngramwidths=[3], leftpad="", rightpad="", padwidth=0, preserveshortsequences=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.
Other sources
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the datasplits argument of tf.rawops.StringNGrams lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, 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. The issue is patched in commit 0462de5b544ed4731aa2fb23946ac22c01856b80, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Affected Software
Remediation
Event History
Frequently Asked Questions
What is the severity of CVE-2020-15205?
CVE-2020-15205 is a high severity vulnerability that can lead to heap overflow errors.
How do I fix CVE-2020-15205?
The vulnerability can be fixed by upgrading to TensorFlow versions 2.3.1 or later.
What software is affected by CVE-2020-15205?
CVE-2020-15205 affects several versions of TensorFlow, TensorFlow-GPU, and TensorFlow-CPU.
What impact does CVE-2020-15205 have on systems?
The impact includes possible memory leaks and exposure of sensitive data due to unvalidated inputs.
Which versions of TensorFlow are vulnerable to CVE-2020-15205?
Vulnerable versions include TensorFlow 1.15.4 and below and specific 2.x versions prior to 2.3.1.