5.8

CVE-2023-20071

Multiple Cisco products are affected by a vulnerability in the Snort detection engine that could allow an unauthenticated, remote attacker to bypass the configured policies on an affected system. This vulnerability is due to a flaw in the FTP module of the Snort detection engine. An attacker could exploit this vulnerability by sending crafted FTP traffic through an affected device. A successful exploit could allow the attacker to bypass FTP inspection and deliver a malicious payload.

Daten sind bereitgestellt durch National Vulnerability Database (NVD)
Cisco ≫ Firepower Threat Defense Version < 6.4.0.17
   Snort ≫ Snort Version2.0 Update-
Cisco ≫ Firepower Threat Defense Version >= 6.5.0 < 7.0.6
   Snort ≫ Snort Version2.0 Update-
Cisco ≫ Firepower Threat Defense Version >= 7.1.0 < 7.2.4
   Snort ≫ Snort Version2.0 Update-
Cisco ≫ Firepower Threat Defense Version >= 7.3.0 < 7.3.1.2
   Snort ≫ Snort Version2.0 Update-
Cisco ≫ Firepower Threat Defense Version >= 6.7.0 < 7.0.5
   Snort ≫ Snort Version < 3.1.32.0
Cisco ≫ Firepower Threat Defense Version >= 7.1.0 < 7.1.0.3
   Snort ≫ Snort Version < 3.1.32.0
Cisco ≫ Firepower Threat Defense Version >= 7.2.0 < 7.2.1
   Snort ≫ Snort Version < 3.1.32.0
Cisco ≫ Cyber Vision Version < 4.1.3
Cisco ≫ Unified Threat Defense Version >= 17.3 < 17.3.8
Cisco ≫ Unified Threat Defense Version >= 17.6 < 17.6.6
Cisco ≫ Unified Threat Defense Version >= 17.9 < 17.9.4
Cisco ≫ Unified Threat Defense Version >= 17.11 < 17.11.1a
Cisco ≫ Unified Threat Defense Version >= 17.12 < 17.12.1a
Zu dieser CVE wurde keine CISA KEV oder CERT.AT-Warnung gefunden.
EPSS Metriken
Typ Quelle Score Percentile
EPSS FIRST.org 0.02% 0.037
CVSS Metriken
Quelle Base Score Exploit Score Impact Score Vector String
nvd@nist.gov 5.8 3.9 1.4
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:N/I:L/A:N
psirt@cisco.com 5.8 3.9 1.4
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:N/I:L/A:N
CWE-1039 Automated Recognition Mechanism with Inadequate Detection or Handling of Adversarial Input Perturbations

The product uses an automated mechanism such as machine learning to recognize complex data inputs (e.g. image or audio) as a particular concept or category, but it does not properly detect or handle inputs that have been modified or constructed in a way that causes the mechanism to detect a different, incorrect concept.