CVE-2020-15197

What it is

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.

At a glance

Exploitability

Can be exploited over the network with a low-privilege account. High risk of service disruption.

Exploit odds

0.7% chance of exploitation in the next 30 days (EPSS).

Fix

A patch, advisory or fixed version is referenced. Upgrade or apply it.

Timeline

1. 2020-09-25 19:15 UTC PublishedDisclosed publicly on CVE.org and NVD
2. 2026-06-17 02:56 UTC Last updatedMost recent change to the record

Why it ranks here

• Exploited in the wild: no, CISA does not list it.
• Exploit odds: 0.7% in the next 30 days (EPSS).
• Severity: 6.3 MEDIUM (CVSS).
• Fix: a patch or advisory is linked.

Details

Published

2020-09-25 19:15 UTC

Last modified

2026-06-17 02:56 UTC

Exploit probability

0.7% · EPSS, next 30 days

Weakness

CWE-20 · Improper input validation

Record status

Modified

Affected

• Google Tensorflow 2.3.0

Attack profile CVSS 3.1 · 6.3

Attack vector

Network

Complexity

High

Privileges needed

Low

User interaction

None

Scope

Changed

Confidentiality

None

Integrity

None

Availability

High

Is it in your code?

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