Agentic AI Security & Autonomous Red-Teaming

Recent progress in foundation models and multi-agent orchestration systems has increased their capabilities and also their attack surface. Cyber-physical systems and edge devices serve both as a target of deployment and as an enabler of operation. The security issues surrounding these enabling mechanisms are already becoming a reality, but the implications of these issues on AI-driven ecosystems are under-researched. In contrast to traditional security areas, threats in agentic AI environments are difficult to anticipate due to their dynamic execution contexts, lack of standardized operational baselines, and the unpredictable behaviors arising from autonomous and emergent agent strategies. This paper examines these challenges and proposes a forward-looking security approach centered on continuous model verification, alignment assurance, and transparency tooling tailored to agentic systems. The framework emphasizes early, automated, and lifecycle-integrated security validation, augmented by autonomous red-teaming to proactively surface weaknesses. The findings suggest that embedding self-assessing security mechanisms into agentic AI pipelines enables more resilient, adaptive, and accountable intelligent systems.