ML Security PapersHuman Society-Inspired Approaches to Agentic AI Security: The 4C Framework
Alsharif Abuadbba 1, Nazatul Sultan 1, Surya Nepal 1, Sanjay Jha 2
Published on arXiv
2602.01942
Excessive Agency
OWASP LLM Top 10 — LLM08
Prompt Injection
OWASP LLM Top 10 — LLM01
Insecure Plugin Design
OWASP LLM Top 10 — LLM07
Key Finding
The 4C Framework identifies that system-centric defenses (prompt injection, data poisoning, tool misuse) fail to capture emergent risks from autonomy and interaction, requiring a behavioral-integrity-centered approach to agentic AI security.
4C Framework
Novel technique introduced
AI is moving from domain-specific autonomy in closed, predictable settings to large-language-model-driven agents that plan and act in open, cross-organizational environments. As a result, the cybersecurity risk landscape is changing in fundamental ways. Agentic AI systems can plan, act, collaborate, and persist over time, functioning as participants in complex socio-technical ecosystems rather than as isolated software components. Although recent work has strengthened defenses against model and pipeline level vulnerabilities such as prompt injection, data poisoning, and tool misuse, these system centric approaches may fail to capture risks that arise from autonomy, interaction, and emergent behavior. This article introduces the 4C Framework for multi-agent AI security, inspired by societal governance. It organizes agentic risks across four interdependent dimensions: Core (system, infrastructure, and environmental integrity), Connection (communication, coordination, and trust), Cognition (belief, goal, and reasoning integrity), and Compliance (ethical, legal, and institutional governance). By shifting AI security from a narrow focus on system-centric protection to the broader preservation of behavioral integrity and intent, the framework complements existing AI security strategies and offers a principled foundation for building agentic AI systems that are trustworthy, governable, and aligned with human values.
Key Contributions
- Introduces the 4C Framework organizing agentic AI security risks across Core (infrastructure integrity), Connection (inter-agent trust), Cognition (goal/reasoning integrity), and Compliance (governance) dimensions
- Shifts AI security framing from narrow system-centric protection to broader behavioral integrity and intent preservation in multi-agent settings
- Provides a principled, society-inspired foundation for designing trustworthy and governable agentic AI systems that complements existing pipeline-level defenses