LLMs are increasingly used for code generation, but their outputs often follow recurring templates that can induce predictable vulnerabilities. We study vulnerability persistence in LLM-generated software and introduce Feature--Security Table (FSTab) with two components. First, FSTab enables a black-box attack that predicts likely backend vulnerabilities from observable frontend features and knowledge of the source LLM, without access to the backend or source code. Second, FSTab provides a model-centric evaluation that quantifies how consistently a model reproduces the same vulnerabilities across programs, semantics-preserving rephrasings, and application domains. We evaluate FSTab on state-of-the-art code LLMs, including GPT-5.2, Claude-4.5 Opus, and Gemini-3 Pro, across diverse application domains. Our results show strong cross-domain transfer: even when the target domain is excluded from training, FSTab achieves up to 94% attack success and 93% vulnerability coverage on Internal Tools (Claude-4.5 Opus). These findings expose an underexplored attack surface in LLM-generated software and highlight the security risks of code generation. Our code is available at https://github.com/fstabicml2026/FSTab
LLM-HyPZ: Hardware Vulnerability Discovery using an LLM-Assisted Hybrid Platform for Zero-Shot Knowledge Extraction and Refinement
Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, USA（电气与计算机工程系，亚利桑那大学）；Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ, USA（系统与工业工程系，亚利桑那大学）；College of Information Science, University of Arizona, Tucson, AZ, USA（信息科学学院，亚利桑那大学）；School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, USA（电气工程与计算机科学学院，北达科他大学）；Department of Computer Science, University of California Davis, CA, USA（计算机科学系，加州大学戴维斯分校）
Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs
Department of Computer Science, University of South Dakota（南达科他大学计算机科学系）；School of Information and Artificial Intelligence, Yangzhou University（扬州大学信息与人工智能学院）
DeFiFusion: Combining Transaction Events with Smart Contracts to Detect Price Manipulation Attacks
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；National University of Singapore（新加坡国立大学）；Zhejiang University of Technology（浙江工业大学）；Zhejiang University（浙江大学）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新区（滨江）区块链与数据安全研究院）
Aplicacion de analitica de datos para la deteccion de anomalias y fortalecimiento de la seguridad en la red WiFi del campus universitario de la Universidad Nacional del Altiplano
PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization
Alibaba Group（阿里巴巴集团）；Beijing Electronic Science and Technology Institute（北京电子科技研究所）；Nanjing University（南京大学）；Renmin University of China（中国人民大学）；Northeastern University（东北大学）；BraneMatrix AI；Nanyang Technological University（南洋理工大学）
