Large Language Models are rapidly becoming core components of modern software development workflows, yet ensuring code security remains challenging. Existing vulnerability detection pipelines either rely on static analyzers or use LLM/GNN-based detectors trained with coarse program-level supervision. Both families often require complete context, provide sparse end-of-completion feedback, and can degrade as code length grows, making them ill-suited for real-time, prefix-level assessment during interactive coding and streaming generation. We propose SecCodePRM, a security-oriented process reward model that assigns a context-aware, step-level security score along a code trajectory. To train the model, we derive step-level supervision labels from static analyzers and expert annotations, allowing the model to attend more precisely to fine-grained regions associated with inter-procedural vulnerabilities. SecCodePRM has three applications: full-code vulnerability detection (VD), partial-code VD, and secure code generation (CG). For VD, SecCodePRM uses risk-sensitive aggregation that emphasizes high-risk steps; for CG, SecCodePRM supports inference-time scaling by ranking candidate continuations and favoring higher cumulative reward. This design yields dense, real-time feedback that scales to long-horizon generation. Empirically, SecCodePRM outperforms prior approaches in all three settings, while preserving code functional correctness, suggesting improved security without a safety-utility tradeoff.
SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design
School of Computing, Mathematics and Engineering, Charles Sturt University（查尔斯史都华大学计算、数学与工程学院）；AI and Cyber Futures Center, Charles Sturt University（查尔斯史都华大学人工智能与网络未来中心）
Applying Action Masking and Curriculum Learning Techniques to Improve Data Efficiency and Overall Performance in Operational Technology Cyber Security using Reinforcement Learning
Google Research（谷歌研究院）；Department of Computer Science, University of British Columbia（不列颠哥伦比亚大学计算机科学系）；Department of Economics and Innocenzo Gasparini Institute for Economic Research, Università Bocconi（博科尼大学经济学系和伊诺琴佐·加斯帕里尼经济研究所）
Towards Secure and Explainable Smart Contract Generation with Security-Aware Group Relative Policy Optimization
Institute for Clarity in Documentation（文档清晰研究所）；Inria Paris-Rocquencourt（巴黎-罗克琴特研究所）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒研究中心）；Institute of Software, Chinese Academy of Sciences（软件研究所，中国科学院）
GuardSec: A Multi-Modal Web Platform for Real-Time Digital Fraud Detection, Entity Verification, and Connection Security Analysis in the African Context
Université Marien Ngouabi（马里安·恩古瓦比大学）；Société Informatique du Congo (SIC)（刚果计算机公司）
