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MEDIUM · 6.3

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 `...

Vulnerability Description

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.

CVSS Score

6.3

MEDIUM

CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H
Attack Vector
NETWORK
Attack Complexity
HIGH
Privileges Required
LOW
User Interaction
NONE
Scope
CHANGED
Confidentiality
NONE
Integrity
NONE
Availability
HIGH

Affected Products

VendorProductVersions
GoogleTensorflow2.3.0

Related Weaknesses (CWE)

CWE-20CWE-617

References

FAQ

What is CVE-2020-15197?

CVE-2020-15197 is a vulnerability with a CVSS score of 6.3 (MEDIUM). 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 `...

How severe is CVE-2020-15197?

CVE-2020-15197 has been rated MEDIUM with a CVSS base score of 6.3/10. Review the CVSS metrics above for detailed severity breakdown.

Is there a patch for CVE-2020-15197?

Check the references section above for vendor advisories and patch information. Affected products include: Google Tensorflow.