Unlike conventional IT systems—with bounded entry points, predictable patch cycles, and known vulnerabilities—large language models (LLMs) and next-generation AI agents create an attack surface so broad, dynamic, and interconnected that comprehensively mapping or policing it becomes nearly impossible. Every new integration, plugin, RAG pipeline, or deployment scenario multiplies exposure:

• AI systems undergo constant updates and retraining, creating novel, unknown behaviors.
• Users interact live with models, exposing them to innovative attacks such as prompt injection, systemic trust exploitation, and automated API abuse.
• Agents act across digital domains, combining social engineering, system exploitation, and data extraction at machine speed.

Recent studies reveal that over 80% of production models tested in 2025 still succumb to at least one form of adversarial exploitation.

Read my full article on Security Boulevard.