Future CBDC Security Risks

This analysis was conducted to: 1. Strengthen CBDC Security Posture By anticipating future threats enabled by AI-driven attackers, this report identifies emerging vulnerabilities that traditional cybersecurity models may overlook. 2. Prepare Technical and Policy Frameworks The scenarios and techniques outlined here are intended to assist developers, governments, and regulators in designing more robust, adaptive, and AI-aware defenses for CBDC platforms. 3. Raise Awareness Among Stakeholders It highlights how AI will fundamentally change the threat landscape — shifting attacks from code-breaking to behavioral exploitation, social manipulation, and logic corruption. 4. Encourage Proactive Defense Strategies The findings urge institutions to move from reactive to predictive defense models, including adversarial AI testing, biometric hardening, logic rule auditing, and public digital education. 5. Support Crisis Response Readiness In the event of AI-induced destabilization or mass-manipulation campaigns, this document serves as a reference point for incident response, damage containment, and user protection protocols. 6. Adapt Legacy Fraud Detection Models By analyzing how classic Bitcoin attacks can be reimagined under CBDC constraints, this report pushes for an evolution of detection systems toward logic-aware, AI-resilient infrastructures.