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Humphreys, D., Koay, A., Desmond, D. and Mealy, E. (2024) AI Hype as a Cyber Security Risk: The Moral Responsibility of Implementing Generative AI in Business. AI and Ethics, 4, 791-804.
https://doi.org/10.1007/s43681-024-00443-4

has been cited by the following article:

• TITLE: Information Security, Ethics, and Integrity in LLM Agent Interaction

  AUTHORS: Ying-Jung Chen, Vijay K. Madisetti

  KEYWORDS: Multi LLM Agents Systems, Blockchain, Cooperative Interactions, Fraud Detection, Ethics and Safety

  JOURNAL NAME: Journal of Information Security, Vol.16 No.1, January 26, 2025

  ABSTRACT: This study addresses security and ethical challenges in LLM-based Multi-Agent Systems, as exemplified in a blockchain fraud detection case study. Leveraging blockchain’s secure architecture, the framework involves specialized LLM Agents—ContractMining, Investigative, Ethics, and PerformanceMonitor, coordinated by a ManagerAgent. Baseline LLM models achieved 30% accuracy with a threshold method and 94% accuracy with a random-forest method. The Claude 3.5-powered LLM system reached an accuracy of 92%. Ethical evaluations revealed biases, highlighting the need for fairness-focused refinements. Our approach aims to develop trustworthy and reliable networks of agents capable of functioning even in adversarial environments. To our knowledge, no existing systems employ ethical LLM agents specifically designed to detect fraud, making this a novel contribution. Future work will focus on refining ethical frameworks, scaling the system, and benchmarking it against traditional methods to establish a robust, adaptable, and ethically grounded solution for blockchain fraud detection.