CVE-2022-20845

Nov. 21, 2024, 6:43 a.m.

6.0
Medium

Description

A vulnerability in the TL1 function of Cisco Network Convergence System (NCS) 4000 Series could allow an authenticated, local attacker to cause a memory leak in the TL1 process. This vulnerability is due to TL1 not freeing memory under some conditions. An attacker could exploit this vulnerability by connecting to the device and issuing TL1 commands after being authenticated. A successful exploit could allow the attacker to cause the TL1 process to consume large amounts of memory. When the memory reaches a threshold, the Resource Monitor (Resmon) process will begin to restart or shutdown the top five consumers of memory, resulting in a denial of service (DoS).Cisco has released software updates that address this vulnerability. There are no workarounds that address this vulnerability.This advisory is part of the September 2022 release of the Cisco IOS XR Software Security Advisory Bundled Publication. For a complete list of the advisories and links to them, see .

Product(s) Impacted

Product Versions
Cisco Network Convergence System (NCS) 4000 Series
  • []

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-789
Memory Allocation with Excessive Size Value
The product allocates memory based on an untrusted, large size value, but it does not ensure that the size is within expected limits, allowing arbitrary amounts of memory to be allocated.

CVSS Score

6.0 / 10

CVSS Data - 3.1

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

    View Vector String

Timeline

Published: Nov. 15, 2024, 4:15 p.m.
Last Modified: Nov. 21, 2024, 6:43 a.m.

Status : Awaiting Analysis

CVE has been recently published to the CVE List and has been received by the NVD.

More info

Source

ykramarz@cisco.com

*Disclaimer: Some vulnerabilities do not have an associated CPE. To enhance the data, we use AI to infer CPEs based on CVE details. This is an automated process and might not always be accurate.