mlsanityData to 7 Oct 2026

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

arXiv 2409.14457 ↗First posted 22 Sep 2024cs.AIcited by 26

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Cited by 20 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 160 of its 214 references that are in the map. First posted on arXiv 22 Sep 2024.

Builds on

160 of 214 references.

References · 160

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38 more
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Citing papers per year: 2024: 1; 2025: 15; 2026: 10.

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  1. 2025A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deploymentcited by 110
  2. 2025A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasurescited by 54
  3. 2025Internet of Agents: Fundamentals, Applications, and Challengescited by 31
  4. 2024Defining and Detecting the Defects of the Large Language Model-based Autonomous Agentscited by 14
  5. 2026From Spark to Fire: Modeling and Mitigating Error Cascades in LLM-Based Multi-Agent Collaborationcited by 13
  6. 2025LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-assisted Sensor Networkscited by 12
  7. 2025The Trust Paradox in LLM-Based Multi-Agent Systems: When Collaboration Becomes a Security Vulnerabilitycited by 11
  8. 2025LLM Applications: Current Paradigms and the Next Frontiercited by 8
  9. 2026ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networkscited by 8
  10. 2025Position: Towards a Responsible LLM-empowered Multi-Agent Systemscited by 7
  11. 2026When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agentscited by 6
  12. 2025FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoringcited by 4
14 more
  1. 2025ScamAgents: How AI Agents Can Simulate Human-Level Scam Callscited by 4
  2. 2025Exploring Agentic Artificial Intelligence Systems: Towards a Typological Frameworkcited by 2
  3. 2025Measuring Physical-World Privacy Awareness of Large Language Models: An Evaluation Benchmarkcited by 2
  4. 2025Graph Representation-based Model Poisoning on the Heterogeneous Internet of Agentscited by 2
  5. 2025Interpretable Anomaly-Based DDoS Detection in AI-RAN with XAI and LLMscited by 1
  6. 2025Enabling Regulatory Multi-Agent Collaboration: Architecture, Challenges, and Solutionscited by 1
  7. 2025AIC-VDS: Attention-Based In-Context Learning for Joint Velocity Control and Data Collection Scheduling in Multi-UAV-Assisted Pipeline Monitoringcited by 1
  8. 2026Differential Privacy in Generative AI Agents: Analysis and Optimal Tradeoffscited by 1
  9. 2026Security of OpenClaw Agents: Fundamentals, Attacks, and Countermeasurescited by 1
  10. 2026FaithEyes: Towards Faithful Tool Use via Multi-Agent Process-Image Verificationcited by 1
  11. 2026Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futurescited by 0
  12. 2026Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMscited by 0
  13. 2026Agentic MPC for Semantic Control System Resynthesiscited by 0
  14. 2026SKILLLITE: Evidence-Guided Malicious Skill Auditing with Compact LLMscited by 0