Web services - agent based model for inter-enterprise collaboration

Web Services technology is an emerging paradigm in establishing a standard environment for legacy applications integration over the Internet. Using Web services and related technologies facilitate the implementation of a virtual enterprise across heterogeneous software platforms. However, Web services suffer from some shortcomings fulfilling requirements of setting up a reactive and autonomous collaboration among enterprises. The current technology' of Web services registry, known as UDDI, is in its infancy stage. The aim of this work is to integrate intelligent software agents and Web services in order to apply them to create a collaborative environment. We s ta rt with describing the concepts of virtual enterprise, Web services, and software agents as well as their requirements, problems, and benefits. To this extent, we identify the existing problems w ith Web services technology and discuss the feasibility of using software agents and their abilities to prevail those difficulties. This thesis proposes a Web services / agent-based model for both the internal architecture of an individual enterprise and the UDDI registry as well. We define a multi-agent model in different levels of enterprise’s system architecture to accomplish a suitable selection of a registered service, to check the status of a process, to realize users’ requests, and to react to them in a collaborative way with other agent-based Web services. Moreover, the thesis proposes a multi-agent model to define a dynamic workflow capable of coordinating and monitoring the processes defined in the workflows

Exploiting Large Language Models (LLMs) through Deception Techniques and Persuasion Principles

With the recent advent of Large Language Models (LLMs), such as ChatGPT from OpenAI, BARD from Google, Llama2 from Meta, and Claude from Anthropic AI, gain widespread use, ensuring their security and robustness is critical. The widespread use of these language models heavily relies on their reliability and proper usage of this fascinating technology. It is crucial to thoroughly test these models to not only ensure its quality but also possible misuses of such models by potential adversaries for illegal activities such as hacking. This paper presents a novel study focusing on exploitation of such large language models against deceptive interactions. More specifically, the paper leverages widespread and borrows well-known techniques in deception theory to investigate whether these models are susceptible to deceitful interactions. This research aims not only to highlight these risks but also to pave the way for robust countermeasures that enhance the security and integrity of language models in the face of sophisticated social engineering tactics. Through systematic experiments and analysis, we assess their performance in these critical security domains. Our results demonstrate a significant finding in that these large language models are susceptible to deception and social engineering attacks.Comment: 10 pages, 16 tables, 5 figures, IEEE BigData 2023 (Workshops