Data-Centric AI Security: Securing Models, Learning Agents, and Autonomous Systems

Scope

This symposium explores security challenges in data-centric AI systems, with particular attention to learning agents and autonomous systems. Topics include secure data ingestion and training data integrity, dataset poisoning detection, privacy risks in model training, and secure memory management in agent-based systems. We also welcome research on adversarial threats such as prompt injection, RAG-based manipulation, model extraction, reward hacking, and attacks on autonomous decision-making processes. Contributions addressing secure model lifecycle engineering, Zero Trust architectures, authorization control for AI APIs and agents, and runtime monitoring of autonomous workflows are encouraged. In addition, discussions on governance and risk management—including alignment with NIST AI RMF, dual-use risks, and responsible disclosure for agentic and self-learning systems—are highly relevant.