A Comparative Survey of Security Risks in AI Systems: From LLMs to AI Agents and Embodied Agents
Сравнительный обзор рисков безопасности в системах искусственного интеллекта: от больших языковых моделей до ИИ-агентов и воплощённых агентов
2026-08-01
SCID: 54.1/8wmdcqkk
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Abstract (AI)
Rapid AI development across industries raises pressing security and privacy risks. This work presents a unified comparison of large language models, AI agents, and embodied agents, introducing a taxonomy of risks spanning data, models, systems, content, and applications, alongside a catalog of 24 specific threats. We contrast attack surfaces and methods across the three system types to reveal common patterns and distinctive vulnerabilities. We also survey mainstream AI security assessment frameworks and evaluate how relevant laws and regulations currently address these risks. Finally, we outline concrete directions for future research and practice aimed at building robust, secure agent ecosystems.
Key Findings
1
Comparing attack surfaces and methods reveals both shared patterns and vulnerabilities distinctive to each system category.
2
It introduces a five-layer risk taxonomy covering data, models, systems, content, and applications.
3
The survey provides a unified security comparison of large language models, AI agents, and embodied agents.
4
The survey reviews AI security assessment frameworks and evaluates how existing laws and regulations address identified risks, while proposing future research directions.
5
The work catalogs 24 specific security and privacy threats across the three AI system types.
Research Object
large language models, AI agents, and embodied agents
Research Subject
comparative security and privacy risks, attack surfaces, vulnerabilities, assessment frameworks, and regulatory coverage across the three AI system types
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2026-08-01
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