CVE-2024-20405
Published: 05 June 2024
Summary
CVE-2024-20405 is a medium-severity Improper Input Validation (CWE-20) vulnerability in Cisco Finesse. Its CVSS base score is 4.8 (Medium).
Operationally, ranked in the top 24.5% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-18120
Vulnerability details
A vulnerability in the web-based management interface of Cisco Finesse could allow an unauthenticated, remote attacker to conduct a stored XSS attack by exploiting an RFI vulnerability. This vulnerability is due to insufficient validation of user-supplied input for specific HTTP…
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requests that are sent to an affected device. An attacker could exploit this vulnerability by persuading a user to click a crafted link. A successful exploit could allow the attacker to execute arbitrary script code in the context of the affected interface or access sensitive information on the affected device.
- 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.
Penetration testing submits XSS payloads to web applications, detecting cross-site scripting flaws for subsequent remediation.
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.
Output validation against expected content can reject or sanitize script content in generated web pages, reducing XSS exploitability.
Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.