What is the CVSS score of CVE-2026-24186?

CVE-2026-24186 has a CVSS 3.1 base score of 8.8 (High). CVSS vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H. EPSS exploitation probability: 0.1%.

Which versions of NVIDIA FLARE SDK are affected by CVE-2026-24186?

NVIDIA FLARE SDK versions prior to the fix release contain a remote code execution vulnerability (CVSS 8.8) affecting federated learning infrastructure.

How to fix or mitigate CVE-2026-24186?

Within 24 hours: Inventory all NVIDIA FLARE SDK deployments and document versions in use; verify whether systems are internet-facing or have external user access. Within 7 days: Implement network segmentation to restrict FLARE message traffic to trusted internal networks only; enforce strong authentication and audit logging on all FLARE components; review access controls to ensure only essential personnel have FLARE user accounts. Within 30 days: Monitor NVIDIA security advisories for patch availability; prepare and test a patching plan for immediate deployment once vendor releases a fix; consider temporary migration of non-critical federated learning workloads to alternative platforms if risk tolerance is low.

NVIDIA FLARE SDK CVE-2026-24186

EUVD-2026-26076 HIGH

Deserialization of Untrusted Data (CWE-502)

2026-04-28 nvidia

Nvidia Deserialization RCE

8.8

CVSS 3.1 · NVD

Severity by source

NVD PRIMARY

8.8 HIGH

AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Primary rating from NVD · only source for this CVE.

CVSS VectorNVD

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Attack Vector

Network

Attack Complexity

Low

Privileges Required

Low

User Interaction

None

Scope

Unchanged

Confidentiality

High

Integrity

High

Availability

High

Lifecycle Timeline

Re-analysis Queued

Apr 28, 2026 - 20:23 vuln.today

cvss_changed

Analysis Generated

Apr 28, 2026 - 20:01 vuln.today

EUVD ID Assigned

Apr 28, 2026 - 19:30 euvd

EUVD-2026-26076

Analysis Generated

Apr 28, 2026 - 19:30 vuln.today

CVE Published

Apr 28, 2026 - 17:45 nvd

HIGH 8.8

DescriptionCVE.org

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.

AnalysisAI

Remote code execution in NVIDIA FLARE SDK allows authenticated attackers to execute arbitrary code by sending maliciously crafted FOBS-encoded messages that exploit unsafe deserialization in the FOBS component. The vulnerability affects federated learning deployments where NVIDIA FLARE SDK processes messages from low-privileged authenticated users, enabling complete system compromise with high impact to confidentiality, integrity, and availability. No active exploitation confirmed (not in CISA KEV) and public exploit status unknown at time of analysis.

Technical ContextAI

NVIDIA FLARE (Federated Learning Application Runtime Environment) SDK is a framework for privacy-preserving collaborative machine learning across distributed participants. FOBS (FLARE OBject Serialization) is NVIDIA's custom serialization mechanism for efficiently encoding and transmitting objects between federated learning nodes. This vulnerability stems from CWE-502 (Deserialization of Untrusted Data), where the FOBS deserializer fails to properly validate incoming serialized data before reconstructing objects. When malicious FOBS-encoded messages are processed, attackers can instantiate arbitrary Python objects and invoke methods during deserialization, leading to remote code execution. This class of vulnerability is particularly dangerous in distributed computing frameworks where nodes regularly exchange serialized data structures.

RemediationAI

Apply the security update detailed in NVIDIA Security Bulletin 5819 available at https://nvidia.custhelp.com/app/answers/detail/a_id/5819. Upgrade NVIDIA FLARE SDK to the patched version specified in the bulletin immediately. As interim mitigation until patching is feasible, restrict network access to FLARE SDK endpoints using firewall rules to allow only explicitly trusted federated learning participant IP addresses, reducing the pool of potential authenticated attackers. Implement additional authentication layers such as mutual TLS with certificate pinning for FLARE node-to-node communication to raise the authentication barrier beyond low-privilege credentials. Monitor FOBS message traffic for anomalous patterns such as unusually large serialized payloads or unexpected object types, though sophisticated attacks may evade signature-based detection. Note that access restrictions trade off the flexibility of federated learning participant onboarding against security, requiring manual IP allowlisting for each new participant.