CVE-2020-15197 - CVE House
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Status published Medium CVE-2020-15197

Denial of Service in Tensorflow

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.

Impact Analysis

Refer to official advisory for detailed impact metrics.

Remediation

Ensure systems are updated to the latest vendor-supplied patch levels.

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Credits & Attribution

No credits recorded in the NVD database.

References

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Affected Vendor

Affected Software

tensorflow
Vulnerable Versions:
= 2.3.0

Timeline

Official Publish: September 25th, 2020
Last Modified: August 4th, 2024
Added to House: July 21st, 2026

CVSS Vectors

V3: CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H

Weaknesses (CWE)

CWE-617 CWE-20

MITRE ATT&CK TTPs

T1499 Endpoint Denial of Service
Impact
T1190 Exploit Public-Facing Application
Initial Access
T1059 Command and Scripting Interpreter
Execution
T1562 Impair Defenses
Defense Evasion
T1485 Data Destruction
Impact
T1068 Exploitation for Privilege Escalation
Privilege Escalation
T1213 Data from Information Repositories
Collection
T1565.002 Stored Data Manipulation
Impact