NVIDIA NVFlare Dashboard EUVD-2026-26075

No special conditions required - remote unauthenticated exploitation against default configurations of NVIDIA NVFlare Dashboard. The vulnerability exists in the core user management and authentication system, requiring only network accessibility to the Dashboard interface (typically HTTP/HTTPS ports). The CVSS vector confirms no authentication (PR:N), no user interaction (UI:N), and low attack complexity (AC:L), indicating exploitation against standard deployments without requiring non-default settings, specific feature enablement, or social engineering. The primary limiting factor is network reachability: organizations that have not exposed the Dashboard to untrusted networks or the public internet significantly reduce their attack surface, though internal threat actors or attackers with network access post-initial-compromise can still exploit this flaw.

This vulnerability presents critical real-world risk based on converging threat indicators. The CVSS vector (AV:N/AC:L/PR:N/UI:N) represents the worst-case exploitability scenario: network-accessible, low attack complexity, requiring no authentication or user interaction. The 9.8 CVSS score with maximum impact ratings (C:H/I:H/A:H) indicates complete system compromise potential. While no CISA KEV listing or public POC has been identified, the technical simplicity implied by AC:L combined with the authentication bypass nature means exploitation code could be developed rapidly once vulnerability details are disclosed. The SSVC framework would classify this as requiring urgent action given the technical impact (total system compromise), the automatable attack vector, and deployment in federated learning environments that likely handle sensitive training data. The primary risk mitigation factor is that NVIDIA Flare Dashboard deployment is likely limited to specialized ML/AI development environments rather than internet-facing production systems, potentially reducing the attack surface compared to widely deployed software. Organizations running federated learning infrastructure should treat this as a priority patch given the potential for unauthorized access to proprietary ML models, training data, and computational resources.

An external attacker identifies an internet-accessible NVIDIA NVFlare Dashboard instance through network reconnaissance. Without any credentials, the attacker crafts malicious requests containing manipulated authentication keys or tokens that exploit the user-controlled key vulnerability in the authentication system. The system incorrectly validates these attacker-controlled keys, granting administrative access to the federated learning environment. The attacker then executes arbitrary code on connected nodes, exfiltrates proprietary ML models and training datasets, manipulates federated learning experiments to poison models, or deploys ransomware across the distributed infrastructure.