Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2026-24186 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Nvidia Nvflare. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 39th percentile by exploit likelihood (below the median); 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.
CVE-2026-24186 is a vulnerability in the NVIDIA FLARE SDK, specifically within its FOBS component, that enables deserialization of untrusted data. An attacker can trigger this by sending a malicious FOBS-encoded message, potentially leading to arbitrary code execution. The issue is classified under CWE-502 (Deserialization of Untrusted Data) and carries a CVSS v3.1 base score of 8.8 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H), indicating high severity due to its network accessibility, low complexity, and significant impacts on confidentiality, integrity, and availability.
Exploitation requires low privileges (PR:L) and can be performed over the network (AV:N) with low attack complexity and no user interaction. A successful attack allows the adversary to achieve code execution on the targeted system, compromising high levels of confidentiality, integrity, and availability without changing scope.
Mitigation details are available in published advisories, including the NVIDIA security bulletin at https://nvidia.custhelp.com/app/answers/detail/a_id/5819, as well as NVD entry https://nvd.nist.gov/vuln/detail/CVE-2026-24186 and CVE record https://www.cve.org/CVERecord?id=CVE-2026-24186. The vulnerability was published on 2026-04-28.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-26076
Vulnerability Data
NVIDIA FLARE SDK contains a vulnerability in FOBS, where an attacker may cause deserialization of untrusted data by sending a malicious FOBS- encoded message. A successful exploit of this vulnerability might lead to code execution.
- 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.