The integration of Large Language Model (LLM)-based conversational agents into vehicles creates novel security challenges at the intersection of agentic AI, automotive safety, and inter-agent communication. As these intelligent assistants coordinate with external services via protocols such as Google's Agent-to-Agent (A2A), they establish attack surfaces where manipulations can propagate through natural language payloads, potentially causing severe consequences ranging from driver distraction to unauthorized vehicle control. Existing AI security frameworks, while foundational, lack the rigorous "separation of concerns" standard in safety-critical systems engineering by co-mingling the concepts of what is being protected (assets) with how it is attacked (attack paths). This paper addresses this methodological gap by proposing a threat modeling framework called AgentHeLLM (Agent Hazard Exploration for LLM Assistants) that formally separates asset identification from attack path analysis. We introduce a human-centric asset taxonomy derived from harm-oriented "victim modeling" and inspired by the Universal Declaration of Human Rights, and a formal graph-based model that distinguishes poison paths (malicious data propagation) from trigger paths (activation actions). We demonstrate the framework's practical applicability through an open-source attack path suggestion tool AgentHeLLM Attack Path Generator that automates multi-stage threat discovery using a bi-level search strategy.
An AI-Powered Research Assistant in the Lab: A Practical Guide for Text Analysis Through Iterative Collaboration with LLMs
Universidad Externado de Colombia（哥伦比亚外部大学）；Universidad de los Andes（安第斯大学）；University of Michigan（密歇根大学）；Rice University（莱斯大学）；University of Toronto（多伦多大学）；University of Southampton（南安普顿大学）
Taxonomy, Evaluation and Exploitation of IPI-Centric LLM Agent Defense Frameworks
The Hong Kong University of Science and Technology（香港科技大学）；Zhejiang University of Technology（浙江工业大学）；Lingnan University（岭南大学）；School of Cyber Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；ZTE Corporation（中兴通讯有限公司）
A Prompt-Engineering Approach to Develop Scalable, Flexible, and Real-Time Hybrid Micro-Level Personalization in a General Purpose AI Teaching Assistant
Towards Compositional Generalization of LLMs via Skill Taxonomy Guided Data Synthesis
State Key Laboratory of Complex & Critical Software Environment, Beihang University（复杂与关键软件环境国家重点实验室，北京航空航天大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Beijing Institute of Technology（北京理工大学）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）
LLM Agents in Law: Taxonomy, Applications, and Challenges
Carnegie Mellon University（卡内基梅隆大学）；National University of Singapore（国立新加坡大学）；Stanford University（斯坦福大学）；University of Washington（华盛顿大学）；The University of Chicago（芝加哥大学）；Rutgers University（罗格斯大学）；Columbia University（哥伦比亚大学）；New Jersey Institute of Technology（新泽西理工学院）
University of Perugia（佩鲁贾大学）；Sapienza University of Rome（罗马第一大学）
In this work, a re-design of the Moodledata module functionalities is presented to share learning objects between e-learning content platforms, e.g., Moodle and G-Lorep, in a linkable object format. The e-learning courses content of the Drupal-based Content Management System G-Lorep for academic learning is exchanged designing an object incorporating metadata to support the reuse and the classification in its context. In such an Artificial Intelligence environment, the exchange of Linkable Learning Objects can be used for dialogue between Learning Systems to obtain information, especially with…
When AI Fails, What Works? A Data-Driven Taxonomy of Real-World AI Risk Mitigation Strategies
Faculty of Computer Science and Engineering（计算机科学与工程系）；Ss. Cyril and Methodius University（西里尔和方法ius大学）；Department of Computer Science（计算机科学系）；Metropolitan College（Metropolitan学院）
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions
Peking University（北京大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；National University of Singapore（新加坡国立大学）；Nankai University（南开大学）；Tongji University（同济大学）；Nanjing University（南京大学）；University of Wollongong（沃林根大学）；Shanghai Jiao Tong University（上海交通大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
Bridge-AI Lab（Bridge-AI实验室）；University of Central Florida（中佛罗里达大学）；Auburn University（奥本大学）；Wichita State University（威奇托州立大学）；Gustavus Adolphus College（古斯塔夫斯阿道夫学院）
Agentic AI in Healthcare & Medicine: A Seven-Dimensional Taxonomy for Empirical Evaluation of LLM-based Agents
Department of Computer Science, New York University, CIMS, New York, USA（纽约大学计算机科学系，CIMS，纽约，美国）；Department of Computer Science, Cleveland State University, Cleveland, USA（克利夫兰州立大学计算机科学系，克利夫兰，美国）
Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges
University of Tartu（塔尔图大学）；University of Hradec Kralove（赫拉德茨-克拉洛韦大学）；UAEM（墨西哥州自治大学）；University of Twente（特文特大学）；TU Delft（代尔夫特理工大学）；University of Warsaw（华沙大学）；Fortinet（飞塔公司）
A Comprehensive Data-centric Overview of Federated Graph Learning
Beijing Institute of Technology（北京理工大学）；Sun Yat-sen University（中山大学）；Peking University（北京大学）；Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学学院）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）
Virtual assistants make use of automatic speech recognition (ASR) to help users answer entity-centric queries. However, spoken entity recognition is a difficult problem, due to the large number of frequently-changing named entities. In addition, resources available for recognition are constrained when ASR is performed on-device. In this work, we investigate the use of probabilistic grammars as language models within the finite-state transducer (FST) framework. We introduce a deterministic approximation to probabilistic grammars that avoids the explicit expansion of non-terminals at model creat…
