[Paper Review] Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends

Yuntao Wang, Yanghe Pan+7 more|arXiv (Cornell University)|Sep 22, 2024

Scientific Computing and Data ManagementDecision Sciences3 citations

TL;DR

This paper presents a comprehensive survey of large model (LM) agents, analyzing their architecture, cooperation paradigms, security, and privacy challenges. It proposes a unified framework for autonomous, embodied, and connected LM agents, emphasizing multi-agent collaboration, trust mechanisms, and blockchain-integrated value ecosystems, with key contributions in taxonomy, threat modeling, and future research directions for robust and secure agent ecosystems.

ABSTRACT

With the rapid advancement of large models (LMs), the development of general-purpose intelligent agents powered by LMs has become a reality. It is foreseeable that in the near future, LM-driven general AI agents will serve as essential tools in production tasks, capable of autonomous communication and collaboration without human intervention. This paper investigates scenarios involving the autonomous collaboration of future LM agents. We review the current state of LM agents, the key technologies enabling LM agent collaboration, and the security and privacy challenges they face during cooperative operations. To this end, we first explore the foundational principles of LM agents, including their general architecture, key components, enabling technologies, and modern applications. We then discuss practical collaboration paradigms from data, computation, and knowledge perspectives to achieve connected intelligence among LM agents. After that, we analyze the security vulnerabilities and privacy risks associated with LM agents, particularly in multi-agent settings, examining underlying mechanisms and reviewing current and potential countermeasures. Lastly, we propose future research directions for building robust and secure LM agent ecosystems.

Motivation & Objective

• To provide a systematic survey of the state-of-the-art in large model (LM) agents, focusing on their architectural foundations and enabling technologies.
• To analyze cooperation paradigms across data, computation, and knowledge dimensions to enable collective intelligence in multi-agent systems.
• To identify and categorize critical security and privacy threats in LM agent ecosystems, especially in multi-agent and cyber-physical settings.
• To explore the integration of blockchain and zero-trust architectures for secure, transparent, and autonomous value exchange among LM agents.
• To outline future research directions for building scalable, trustworthy, and robust LM agent ecosystems toward Artificial General Intelligence (AGI).

Proposed method

• Proposes a four-component architecture for LM agents: planning, action, memory, and interaction, with large foundation models serving as the cognitive core.
• Introduces a taxonomy of interaction patterns based on data, computation, and knowledge sharing to enable collective intelligence in multi-agent systems.
• Applies advanced reasoning techniques such as Chain-of-Thought (CoT), Tree-of-Thought (ToT), and reflection to enhance task decomposition and decision-making.
• Integrates Retrieval-Augmented Generation (RAG) to enable access to external knowledge, improving response accuracy and context awareness.
• Employs zero-trust security models with continuous authentication and device integrity verification to mitigate internal and external threats.
• Proposes blockchain-based value ecosystems using smart contracts and oracles to enable secure, transparent, and decentralized transaction management among agents.

Experimental results

Research questions

• RQ1 How can large model agents be architected to achieve autonomy, embodiment, and connectivity across physical, virtual, and mixed-reality environments?
• RQ2 What are the effective collaboration paradigms for LM agents in terms of data, computation, and knowledge sharing to enable collective intelligence?
• RQ3 What are the primary security and privacy vulnerabilities in multi-agent LM systems, especially in cyber-physical-social systems (CPSS)?
• RQ4 How can zero-trust and legal reasoning mechanisms be integrated into LM agents to ensure compliance and resilience against adversarial threats?
• RQ5 What are the key challenges and opportunities in building scalable, interoperable, and secure value networks for LM agents using blockchain and smart contracts?

Key findings

• Large model agents, powered by foundation models like GPT-4o and PaLM 2, demonstrate advanced capabilities in reasoning, planning, and adaptation, enabling complex task execution through techniques like Chain-of-Thought and Tree-of-Thought.
• Multi-agent collaboration via data, computation, and knowledge sharing significantly enhances system-level intelligence, with practical implementations such as AutoGPT demonstrating effective task decomposition and execution.
• Security threats in LM agent ecosystems include prompt injection, model stealing, and adversarial attacks, particularly in interconnected CPSS environments, with zero-trust architectures identified as critical for mitigation.
• Privacy risks such as data leakage and model inversion are heightened in multi-agent settings, and countermeasures like differential privacy and federated learning are emerging as essential protections.
• Blockchain-based value networks with smart contracts and oracles enable secure, autonomous value exchange among agents, though challenges in cross-chain interoperability and scalability remain significant.
• The global market for autonomous AI agents is projected to grow at a CAGR of 43%, reaching USD 28.5 billion by 2028, signaling strong industry momentum and research potential.