Improper input validation in Google Tensorflow
CVE-2020-15197
In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix. However, malicious users can pass in tensors of different rank, resulting in a `CHECK` assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
Vulnerability class: Drupalgeddon 2 (CVE-2018-7600)
EPSS: 0.007 (50.4th percentile) — read the EPSS interpretation.
CVSS v3 metric
CVSS v3 base score 6.3 (Medium). Vector: CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H.
Affected products
- Google Tensorflow — versions 2.3.0
- Tensorflow — versions = 2.3.0
Weakness classification (CWE)
References
- security-advisories@github.com (Third Party Advisory, x_refsource_MISC)
- security-advisories@github.com (Patch, Third Party Advisory, x_refsource_MISC)
- security-advisories@github.com (x_refsource_CONFIRM, Exploit, Third Party Advisory)
Frequently asked questions
- What is CVE-2020-15197?
- CVE-2020-15197 is a medium-severity vulnerability in Google Tensorflow, classified under Improper Input Validation. CVSS score: 6.3/10. Published 2020-09-25.
- How severe is CVE-2020-15197?
- Medium severity. CVSS v3 base score is 6.3 out of 10.