Raw vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HSummary
CVE-2024-13163 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Ivanti Endpoint Manager. Its CVSS base score is 7.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 5% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
The vulnerability is a deserialization of untrusted data flaw, tracked as CWE-502, that affects Ivanti EPM prior to the 2024 January-2025 Security Update and the 2022 SU6 January-2025 Security Update. It carries a CVSS 3.1 base score of 7.8 with the vector AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H.
A remote unauthenticated attacker can leverage the issue to obtain remote code execution on affected systems, although successful exploitation requires local user interaction.
The referenced Ivanti security advisory describes the January 2025 updates that address the flaw in both the 2024 and 2022 SU6 release streams. The associated EPSS score has remained flat at 0.3228 with no material increase observed after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-51389
Vulnerability Data
Deserialization of untrusted data in Ivanti EPM before the 2024 January-2025 Security Update and 2022 SU6 January-2025 Security Update allows a remote unauthenticated attacker to achieve remote code execution. Local user interaction is required.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Mandatory malware scanning of data received over networks or storage media intercepts malicious serialized payloads before they are deserialized by the target application.