AI Safety vs. AI Security: Demystifying the Distinction and Boundaries

2025-06-21

AI Summary

This paper addresses the conceptual conflation of “safety” and “security” in AI research—causing disciplinary fragmentation, hindered interdisciplinary collaboration, and policy ambiguity. We propose the first systematic conceptual framework distinguishing these notions, grounded in rigorous conceptual analysis and cross-domain analogical modeling (drawing on information theory and structural engineering metaphors). The framework rigorously differentiates safety and security along three dimensions: types of risks (e.g., unintentional failure vs. adversarial exploitation), underlying failure mechanisms (e.g., design flaws vs. malicious manipulation), and mitigation objectives (e.g., robustness to uncertainty vs. resilience against attacks). Crucially, we identify and formalize their bidirectional interdependence: safety deficiencies can expand the attack surface, while security vulnerabilities may trigger non-malicious system failures. This framework clarifies research boundaries, enables precise problem scoping, facilitates cross-disciplinary modeling, and supports layered governance design—providing a foundational theoretical tool for trustworthy AI development, evaluation, and policymaking.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness
• Humans and AI: Human-AI Collaboration / Human-AI Teaming

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Artificial Intelligence (AI) is rapidly being integrated into critical systems across various domains, from healthcare to autonomous vehicles. While its integration brings immense benefits, it also introduces significant risks, including those arising from AI misuse. Within the discourse on managing these risks, the terms "AI Safety" and "AI Security" are often used, sometimes interchangeably, resulting in conceptual confusion. This paper aims to demystify the distinction and delineate the precise research boundaries between AI Safety and AI Security. We provide rigorous definitions, outline their respective research focuses, and explore their interdependency, including how security breaches can precipitate safety failures and vice versa. Using clear analogies from message transmission and building construction, we illustrate these distinctions. Clarifying these boundaries is crucial for guiding precise research directions, fostering effective cross-disciplinary collaboration, enhancing policy effectiveness, and ultimately, promoting the deployment of trustworthy AI systems.

Problem

• Clarify distinction between AI Safety and AI Security
• Define research boundaries for AI Safety and Security
• Explore interdependency of AI Safety and Security risks

Innovation

• Defines AI Safety and Security distinctions rigorously
• Uses analogies to clarify conceptual boundaries
• Explores interdependency between safety and security