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.
Published 2020-09-25 19:15:15
Updated 2021-08-17 13:21:51
Source GitHub, Inc.
View at NVD,   CVE.org
Vulnerability category: Input validationDenial of service

Exploit prediction scoring system (EPSS) score for CVE-2020-15197

0.18%
Probability of exploitation activity in the next 30 days EPSS Score History
~ 55 %
Percentile, the proportion of vulnerabilities that are scored at or less

CVSS scores for CVE-2020-15197

Base Score Base Severity CVSS Vector Exploitability Score Impact Score Score Source First Seen
3.5
LOW AV:N/AC:M/Au:S/C:N/I:N/A:P
6.8
2.9
NIST
6.3
MEDIUM CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H
1.8
4.0
NIST
6.3
MEDIUM CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H
1.8
4.0
GitHub, Inc.

CWE ids for CVE-2020-15197

  • The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.
    Assigned by: security-advisories@github.com (Primary)
  • The product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary.
    Assigned by: security-advisories@github.com (Primary)

References for CVE-2020-15197

Products affected by CVE-2020-15197

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