Large Language Model (LLM) applications are vulnerable to prompt injection and context manipulation attacks that traditional security models cannot prevent. We introduce two novel primitives--authenticated prompts and authenticated context--that provide cryptographically verifiable provenance across LLM workflows. Authenticated prompts enable self-contained lineage verification, while authenticated context uses tamper-evident hash chains to ensure integrity of dynamic inputs. Building on these primitives, we formalize a policy algebra with four proven theorems providing protocol-level Byzantine resistance--even adversarial agents cannot violate organizational policies. Five complementary defenses--from lightweight resource controls to LLM-based semantic validation--deliver layered, preventative security with formal guarantees. Evaluation against representative attacks spanning 6 exhaustive categories achieves 100% detection with zero false positives and nominal overhead. We demonstrate the first approach combining cryptographically enforced prompt lineage, tamper-evident context, and provable policy reasoning--shifting LLM security from reactive detection to preventative guarantees.
Joint Optimization of Prompt Security and System Performance in Edge-Cloud LLM Systems
United International College (UIC)（联合国际学院）；Institute of Artificial Intelligence and Future Networks (BNU-Zhuhai)（人工智能与未来网络研究院（北京师范大学珠海校区））；Interdisciplinary Intelligence SuperComputer Center of Beijing Normal University (Zhuhai)（北京师范大学（珠海）跨学科智能超级计算中心）
An Empirical Evaluation of LLM-Generated Code Security Across Prompting Methods
Department of Computer Science, Birzeit University（计算机科学系，巴勒斯坦比泽大学）；King Fahd University of Petroleum and Minerals（国王法赫德石油和矿物大学）；University of Central Florida（中央佛罗里达大学）
Breaking the Protocol: Security Analysis of the Model Context Protocol Specification and Prompt Injection Vulnerabilities in Tool-Integrated LLM Agents
A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models
ELLIS Alicante（欧洲学习与智能系统实验室阿利坎特分部）；Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague（捷克布拉格技术大学捷克信息学、机器人学与控制论研究所）；Delft University of Technology（代尔夫特理工大学）
Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems
School of Information Technologies（信息科技学院）；Tallinn University of Technology（塔林技术大学）；School of Informatics, Computing, and Cyber Systems（信息学、计算与网络安全学院）；Northern Arizona University（北亚利桑那大学）
When Prompts Become Payloads: A Framework for Mitigating SQL Injection Attacks in Large Language Model-Driven Applications
Hasso Plattner Institute, University of Potsdam, Germany（波茨坦大学霍斯曼-普拉特研究所, 德国）；Chemnitz University of Technology, Chemnitz, Germany（Chemnitz技术大学, Chemnitz, 德国）
PrompTrend: Continuous Community-Driven Vulnerability Discovery and Assessment for Large Language Models
University of Manouba（曼努巴大学）；University of Jendouba（杰努布大学）；LETI Laboratory, University of Sfax（萨克斯大学LETI实验室）；DataDoIt（DataDoIt公司）；South Mediterranean University（南地中海大学）
An Insight into Security Code Review with LLMs: Capabilities, Obstacles, and Influential Factors
School of Computer Science, Wuhan University, China（武汉大学计算机科学学院）；School of Mathematical and Computational Sciences, Massey University（梅西大学数学与计算科学学院）；School of Computing Technologies, RMIT University, Australia（皇家墨尔本理工学院计算技术学院）
Arondight: Red Teaming Large Vision Language Models with Auto-generated Multi-modal Jailbreak Prompts
City University of Hong Kong（香港城市大学）；City University of Hong Kong (Dongguan)（香港城市大学（东莞））；University of Science and Technology Beijing（北京科技大学）；The University of Melbourne（墨尔本大学）
The Art of the Jailbreak: Formulating Jailbreak Attacks for LLM Security Beyond Binary Scoring
Department of Computer Science, University of Texas at El Paso（德克萨斯大学埃尔帕索分校计算机科学系）；School of Computing, Southern Illinois University Carbondale（南方伊利诺伊大学卡罗尔丹分校计算学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
