CVE-2020-15197 Denial of Service in Tensorflow

2020-09-2518:40:31

CWE-20

CWE-617

GitHub_M

www.cve.org

denial of service

tensorflow

sparse tensor

vulnerability

CVSS3

6.3

Attack Vector

NETWORK

Attack Complexity

HIGH

Privileges Required

LOW

User Interaction

NONE

Scope

CHANGED

Confidentiality Impact

NONE

Integrity Impact

NONE

Availability Impact

HIGH

CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H

EPSS

0.002

Percentile

55.6%

JSON

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.

CNA Affected

[
  {
    "product": "tensorflow",
    "vendor": "tensorflow",
    "versions": [
      {
        "status": "affected",
        "version": "= 2.3.0"
      }
    ]
  }
]