The Autonomy Paradox: Securing Agentic AI Systems Against MachineSpeed and Semantic Threats
Year: 2026
Type: article
Abstract: As enterprises transition from static Generative AI to autonomous Agentic AI, they unlock unprecedented productivity while dramatically expanding their attack surface. This shift introduces the Autonomy Paradox: to defend against machine-speed, AI-driven attacks, organizations must grant defensive AI systems the very autonomy that makes them vulnerable. This paper explores the emerging threat landscape of agentic systems, detailing critical vectors such as Indirect Prompt Injection (IPI), privilege escalation, and Model Context Protocol (MCP) supply chain compromises. Through empirical case studies—including the 2026 Obsidian plugin abuse and Axios npm compromise—we analyze how threat actors exploit software trust to deliver AI-driven backdoors. Finally, we propose a multi-layered, secure-by-design architectural framework combining Double-Agent Semantic Firewalls, System Vectors (SysVec), and Cisco SnortML. This framework shifts enterprise defense from perimeter-based scanning to real-time, runtime reasoning governance.
Author: Liudmyla Cherniakova
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204446244