Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912406978953216 |
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| author | Wang, Yuntao Pan, Yanghe Su, Zhou Deng, Yi Zhao, Quan Du, Linkang Luan, Tom H. Kang, Jiawen Niyato, Dusit |
| author_facet | Wang, Yuntao Pan, Yanghe Su, Zhou Deng, Yi Zhao, Quan Du, Linkang Luan, Tom H. Kang, Jiawen Niyato, Dusit |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14457 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends Wang, Yuntao Pan, Yanghe Su, Zhou Deng, Yi Zhao, Quan Du, Linkang Luan, Tom H. Kang, Jiawen Niyato, Dusit Artificial Intelligence 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. |
| title | Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2409.14457 |