ASTRIDE: A Security Threat Modeling Platform for Agentic-AI Applications
Old Dominion University（老奥布里恩大学）；Accenture Technology Labs（埃森哲技术实验室）；Florida International University（佛罗里达国际大学）；Nanyang Technological University（南洋理工大学）；University of Colombo（科伦坡大学）
Eranga Bandara, Amin Hass, Ross Gore, Sachin Shetty, Ravi Mukkamala, Safdar H. Bouk, Xueping Liang, Ng Wee Keong, Kasun De Zoysa, Aruna Withanage, Nilaan Loganathan
AI agent-based systems are becoming increasingly integral to modern software architectures, enabling autonomous decision-making, dynamic task execution, and multimodal interactions through large language models (LLMs). However, these systems introduce novel and evolving security challenges, including prompt injection attacks, context poisoning, model manipulation, and opaque agent-to-agent communication, that are not effectively captured by traditional threat modeling frameworks. In this paper, we introduce ASTRIDE, an automated threat modeling platform purpose-built for AI agent-based systems. ASTRIDE extends the classical STRIDE framework by introducing a new threat category, A for AI Agent-Specific Attacks, which encompasses emerging vulnerabilities such as prompt injection, unsafe tool invocation, and reasoning subversion, unique to agent-based applications. To automate threat modeling, ASTRIDE combines a consortium of fine-tuned vision-language models (VLMs) with the OpenAI-gpt-oss reasoning LLM to perform end-to-end analysis directly from visual agent architecture diagrams, such as data flow diagrams(DFDs). LLM agents orchestrate the end-to-end threat modeling automation process by coordinating interactions between the VLM consortium and the reasoning LLM. Our evaluations demonstrate that ASTRIDE provides accurate, scalable, and explainable threat modeling for next-generation intelligent systems. To the best of our knowledge, ASTRIDE is the first framework to both extend STRIDE with AI-specific threats and integrate fine-tuned VLMs with a reasoning LLM to fully automate diagram-driven threat modeling in AI agent-based applications.
A Security Analysis of Long-Horizon Agentic AI Systems: Threats, Evaluation, and Framework Development
Department of Computer Science, College of Computers and Information Technology, Taif University, KSA (Summer 2026)（计算机科学系，计算机与信息科技学院，泰夫大学，沙特阿拉伯（2026年夏季））
How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfare
Department of Sustainable Communication Technologies, SINTEF Digital（可持续通信技术系，SINTEF数字）；Max Planck Institute for Security and Privacy（安全与隐私研究所）；Department of Mathematics, City St George’s University of London（数学系，圣乔治大学）；Macrostrategy Research Initiative（战略研究计划）；Human Nature Lab, Yale University（人性实验室，耶鲁大学）；Department of Politics and Public Administration, University of Konstanz（政治与公共管理系，康斯坦茨大学）；Harvard Business School, Harvard University（哈佛商学院，哈佛大学）；Department of Psychology, University of Cambridge（心理学系，剑桥大学）；Department of Computer Science, University of British Columbia（计算机科学系，不列颠哥伦比亚大学）；Department of Human Centered Design & Engineering, University of Washington（以人为本设计与工程系，华盛顿大学）；Department of Psychology, New York University（心理学系，纽约大学）；Observatory on Social Media and Luddy School of Informatics, Computing, and Engineering, Indiana University（社交媒体观察所和信息、计算与工程学院，印第安纳大学）
Systematization of Knowledge: Security and Safety in the Model Context Protocol Ecosystem
Tribhuvan University（特里布文大学）；University of North Dakota（北达科他大学）；Youngstown State University（亚当斯州立大学）；University of Missouri（密苏里大学）；University of Toledo（托莱多大学）
Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety
School of Computing and Communications（计算与通信学院）；University of Cambridge（剑桥大学）；School of Software（软件学院）；Nanjing University of Information Science and Technology（南京信息科技大學）；TU Wien（维也纳技术大学）；ICREA
MATRA: Modeling the Attack Surface of Agentic AI Systems -- OpenClaw Case Study
DistriNet, KU Leuven（DistriNet，根特大学）；IBM Research（IBM研究院）；CISPA Helmholtz Center for Information Security（CISPA海德堡信息安全中心）；Imperial College London（伦敦帝国理工学院）；A*STAR Institute for Infocomm Research（A*STAR信息与通信研究机构）
What If Prompt Injection Never Left? Rethinking Agent Security through Cross-Session Stored Prompt Injection
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；AI Sec Lab, Beijing Chaitin Technology Co.,Ltd（北京柴坦科技有限公司AI安全实验室）
