{"ID":2835211,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.00520","arxiv_id":"2512.00520","title":"Toward a Safe Internet of Agents","abstract":"Autonomous Artificial Intelligence (AI) agents, powered by Large Language Models (LLMs), advance rapidly toward interconnected systems -- an Internet of Agents (IoA). This vision enables complex problem-solving while introducing systemic safety and security risks. Beyond existing threat taxonomies, we provide a principled guide addressing architectural vulnerability sources. We offer a framework for engineering safe agentic systems through bottom-up deconstruction, analyzing each component as a dual-use interface where capability expansion creates attack surface growth. We examine three tiers: (1) Single Agents -- analyzing inherent risks in models, memory, design patterns, tools, and guardrails; (2) Multi-Agent Systems (MAS) -- examining collective behavior components including architectural patterns, communication mechanisms, verification, and system guardrails; and (3) Interoperable Multi-Agent Systems (IMAS) -- exploring four secure ecosystem pillars: standardized protocols, agent registration/discovery, resource vetting, and governance. Our analysis reveals a central principle: agentic safety must be co-designed with capability as a fundamental architectural property. We identify specific vulnerabilities at each level and derive core mitigation principles. The result is a foundational guide enabling developers and researchers to build not merely capable but safe, reliable agentic AI, contributing to secure IoA development.","short_abstract":"Autonomous Artificial Intelligence (AI) agents, powered by Large Language Models (LLMs), advance rapidly toward interconnected systems -- an Internet of Agents (IoA). This vision enables complex problem-solving while introducing systemic safety and security risks. Beyond existing threat taxonomies, we provide a princip...","url_abs":"https://arxiv.org/abs/2512.00520","url_pdf":"https://arxiv.org/pdf/2512.00520v2","authors":"[\"Juan A. Wibowo\",\"George C. Polyzos\"]","published":"2025-11-29T15:31:16Z","proceeding":"cs.MA","tasks":"[\"cs.MA\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}