CVE-2024-12832

Jan. 3, 2025, 5:50 p.m.

6.3
Medium

Description

Arista NG Firewall ReportEntry SQL Injection Arbitrary File Read and Write Vulnerability. This vulnerability allows remote attackers to create arbitrary files and disclose sensitive information on affected installations of Arista NG Firewall. Authentication is required to exploit this vulnerability. The specific flaw exists within the ReportEntry class. The issue results from the lack of proper validation of a user-supplied string before using it to construct SQL queries. An attacker can leverage this in conjunction with other vulnerabilities to execute arbitrary code in the context of the www-data user. Was ZDI-CAN-24325.

Product(s) Impacted

Vendor Product Versions
Arista
  • Ng Firewall
  • 17.1.1

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-89
Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')
The product constructs all or part of an SQL command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended SQL command when it is sent to a downstream component.

*CPE(s)

Affected systems and software identified for this CVE.

Type Vendor Product Version Update Edition Language Software Edition Target Software Target Hardware Other Information
a arista ng_firewall 17.1.1 / / / / / / /

CVSS Score

6.3 / 10

CVSS Data - 3.1

  • Attack Vector: NETWORK
  • Attack Complexity: LOW
  • Privileges Required: LOW
  • Scope: UNCHANGED
  • Confidentiality Impact: LOW
  • Integrity Impact: LOW
  • Availability Impact: LOW
  • CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L

    View Vector String

Timeline

Published: Dec. 20, 2024, 1:15 a.m.
Last Modified: Jan. 3, 2025, 5:50 p.m.

Status : Analyzed

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

More info

Source

zdi-disclosures@trendmicro.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.