Large Language Models (LLMs) have shown remarkable potential across various domains, including cybersecurity. Using commercial cloud-based LLMs may be undesirable due to privacy concerns, costs, and network connectivity constraints. In this paper, we present Hackphyr, a locally fine-tuned LLM to be used as a red-team agent within network security environments. Our fine-tuned 7 billion parameter model can run on a single GPU card and achieves performance comparable with much larger and more powerful commercial models such as GPT-4. Hackphyr clearly outperforms other models, including GPT-3.5-turbo, and baselines, such as Q-learning agents in complex, previously unseen scenarios. To achieve this performance, we generated a new task-specific cybersecurity dataset to enhance the base model's capabilities. Finally, we conducted a comprehensive analysis of the agents' behaviors that provides insights into the planning abilities and potential shortcomings of such agents, contributing to the broader understanding of LLM-based agents in cybersecurity contexts
BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction
Badji Mokhtar-Annaba University（巴吉·穆克塔尔-安纳巴大学）；University of La Rochelle（拉罗谢尔大学）；National Higher School of Technology and Engineering -Annaba（安纳巴国家技术与工程高等学校）
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）
Empirical Study of Code Large Language Models for Binary Security Patch Detection
National Key Laboratory for Novel Software Technology at Nanjing University（南京大学新型软件技术国家重点实验室）；Harbin Institute of Technology（哈尔滨工业大学）；Chinese University of Hong Kong（香港中文大学）；Hong Kong University of Science and Technology（香港科学大学）
FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model
Knight Foundation School of Computing and Information Sciences, Florida International University (FIU)（佛罗里达国际大学骑士基金会计算与信息科学学院）；Security, Optimization, and Learning for InterDependent networks laboratory (solid lab) at FIU（佛罗里达国际大学相互依赖网络的安全、优化与学习实验室）
LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems
School of Computer Science and Technology, Algoma University（计算机科学与技术学院，阿尔戈马大学）；School of Electrical Engineering and Computer Science, University of Ottawa（电气工程与计算机科学学院，渥太华大学）
Which Neurons Detect Malicious Code? A Probing Study of LLM Security Knowledge
RMIT University（皇家墨尔本理工大学）；Hanoi University（河内大学）；SOICT, Hanoi University of Science and Technology（越南科学技术大学信息与通信技术学院）；Università degli studi dell’Aquila（阿奎拉大学）
SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models
Department of Computer and Information Sciences, Fordham University（福特汉姆大学计算机与信息科学系）；Department of Electrical Engineering, Zhejiang University（浙江大学电子工程系）；Department of Systems Engineering, City University of Hong Kong（香港城市大学系统工程系）；Department of Electrical and Computer Engineering, University of Michigan-Dearborn（密歇根大学迪尔伯恩分校电气与计算机工程系）
Case Study: Fine-tuning Small Language Models for Accurate and Private CWE Detection in Python Code
Institute of Information and Communication Technology, Bangladesh University of Engineering Technology（孟加拉工程科技大学信息与通信技术研究所）；Hajee Mohammad Danesh Science and Technology University（海杰莫哈默德丹什科学与技术大学）
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）
Security in the Fine-Tuning Lifecycle of Large Language Models: Threats, Defenses,Evaluation, and Future Directions
Hangzhou Normal University（杭州师范大学）；Zhejiang University（浙江大学）；China Mobile (Zhejiang) Innovation Research Institute Co., Ltd.（中国移动（浙江）创新研究院有限公司）；Quantum Cloud Computing and Distributed Systems (qCLOUDS) Lab, School of Computing and Information Systems（量子云计算与分布式系统（qCLOUDS）实验室，计算与信息学院）；The University of Melbourne（墨尔本大学）
Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models
Department of Computer Science Texas A\&M University–Corpus Christi Corpus Christi, TX, USA；EECS Department University of Missouri Columbia, MO, USA；Department of Computer Engineering University of California–Riverside Riverside, CA, USA
Backdoor Vectors: a Task Arithmetic View on Backdoor Attacks and Defenses
Warsaw University of Technology（华沙技术大学）；NASK National Research Institute（国家研究机构NASK）；IDEAS NCBR；IDEAS Research Institute（IDEAS研究所）；Computer Vision Center, Universitat Autonoma de Barcelona（巴塞罗那自治大学计算机视觉中心）
