CVE-2022-20797
Published: 27 May 2022
Summary
CVE-2022-20797 is a medium-severity Improper Input Validation (CWE-20) vulnerability in Cisco Secure Network Analytics. Its CVSS base score is 5.5 (Medium).
Operationally, ranked in the top 24.3% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-26047
Vulnerability details
A vulnerability in the web-based management interface of Cisco Secure Network Analytics, formerly Cisco Stealthwatch Enterprise, could allow an authenticated, remote attacker to execute arbitrary commands as an administrator on the underlying operating system. This vulnerability is due to insufficient…
more
user input validation by the web-based management interface of the affected software. An attacker could exploit this vulnerability by injecting arbitrary commands in the web-based management interface. A successful exploit could allow the attacker to make configuration changes on the affected device or cause certain services to restart unexpectedly.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Directly implements checks on information inputs to reject invalid data before processing.
Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.
Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.
Platform-independent apps typically execute inside a managed runtime or sandbox that restricts direct OS command execution, reducing the ability to exploit OS command injection.
Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.