CVE-2024-20253
Published: 26 January 2024
Summary
CVE-2024-20253 is a critical-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Cisco Unified Communications Manager. Its CVSS base score is 9.9 (Critical).
Operationally, ranked in the top 13.1% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-17968
Vulnerability details
A vulnerability in multiple Cisco Unified Communications and Contact Center Solutions products could allow an unauthenticated, remote attacker to execute arbitrary code on an affected device. This vulnerability is due to the improper processing of user-provided data that is being…
more
read into memory. An attacker could exploit this vulnerability by sending a crafted message to a listening port of an affected device. A successful exploit could allow the attacker to execute arbitrary commands on the underlying operating system with the privileges of the web services user. With access to the underlying operating system, the attacker could also establish root access 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.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Evaluation of untrusted data handling (deserialization testing) reveals unsafe processing, which the required remediation process addresses.
Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.
Validates or rejects untrusted serialized data before deserialization occurs.
Identifies and blocks malicious code introduced through deserialization of untrusted data at system boundaries.
Integrity verification of serialized information can detect tampering before deserialization occurs.
Provenance of associated data allows detection of untrusted sources before deserialization or processing occurs.