Currently, large models are prone to generating harmful content when faced with complex attack instructions, significantly reducing their defensive capabilities. To address this issue, this paper proposes a method based on constructing data aligned with multi-dimensional attack defense to enhance the generative security of large models. The core of our method lies in improving the effectiveness of safe alignment learning for large models by innova-tively increasing the diversity of attack instruction dimensions and the accuracy of generat-ing safe responses. To validate the effectiveness of our method, beyond existing security evaluation benchmarks, we additionally designed new security evaluation benchmarks and conducted comparative experiments using Llama3.2 as the baseline model. The final ex-perimental results demonstrate that our method can significantly improve the generative security of large models under complex instructional attacks, while also maintaining and enhancing the models' general capabilities.
SAID: Safety-Aware Intent Defense via Prefix Probing for Large Language Models
Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Harbin Institute of Technology, Shenzhen, Shenzhen, China（深圳视觉目标检测与识别重点实验室，哈尔滨工业大学，哈尔滨工业大学，深圳，深圳，中国）
Enhancing Model Defense Against Jailbreaks with Proactive Safety Reasoning
School of Computing（计算学院）；National University of Singapore（新加坡国立大学）；School of Computer Science and Engineering（计算机科学与工程学院）；Nanyang Technological University（南洋理工大学）；Department of Computing（计算系）；The Hong Kong Polytechnic University（香港理工大学）
Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval
School of EECS, Peking University（电子工程系，北京大学）；School of Mathematical Sciences, Peking University（数学系，北京大学）；State Key Lab of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（通用人工智能国家重点实验室，智能科学与技术学校，北京大学）；Institute for Artificial Intelligence, Peking University（人工智能研究院，北京大学）
Model-Agnostic Lifelong LLM Safety via Externalized Attack-Defense Co-Evolution
City University of Hong Kong（香港城市大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Wuhan University（武汉大学）；Beihang University（北京航空航天大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
DELMAN: Dynamic Defense Against Large Language Model Jailbreaking with Model Editing
ShanghaiTech University（上海科技大学）；The State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全全国重点实验室）；The CoAI group, Tsinghua University（清华大学CoAI课题组）
ChannelGuard: From Borrowed Safety to Application-Controlled Defenses in Multi-Agent LLMs
College of Engineering and Computer Science, University of Central Florida（工程与计算机科学学院，中央佛罗里达大学）；Department of Computer Science and Engineering, North South University（计算机科学与工程系，北南大学）；Computer Science and Engineering, Ahsanullah University of Science and Technology（计算机科学与工程，阿沙努拉科学与技术大学）；Department of Electrical and Computer Engineering, Purdue University Fort Wayne（电气与计算机工程系，普渡大学弗拉特沃恩分校）
TamperBench: Systematically Stress-Testing LLM Safety Under Fine-Tuning and Tampering
Critical ML Lab Waterloo Canada（Waterloo大学Critical ML实验室）；FAR.AI Berkeley USA（伯克利美国FAR.AI公司）；University of Toronto Toronto Canada（多伦多大学）；University of Waterloo Waterloo Canada（Waterloo大学）；ETH Zürich Zürich Switzerland（苏黎世联邦理工学院）；MIT CSAIL Cambridge USA（麻省理工学院CSAIL实验室）；University of Toronto, MPI, EuroSafeAI, Vector Institute Toronto Canada（多伦多大学、马克斯·普朗克研究所、EuroSafeAI、Vector Institute）；Critical ML Lab University of Waterloo Waterloo Canada（Waterloo大学Critical ML实验室）
Pharmacist: Safety Alignment Data Curation for Large Language Models against Harmful Fine-tuning
School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；School of Computer Science at Georgia Institute of Technology（佐治亚理工学院计算机科学系）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络科学与技术学院）；Second Affiliated Hospital of Guangzhou University of Chinese Medicine（广州中医药大学第二附属医院）；China and Pengcheng Laboratory（中 Pengcheng 实验室）
Moving the Safety Barrier: Dynamic Routing Adaptive Alignment Against White-Box Attacks
Nanjing University of Science and Technology（南京理工大学）；The University of Sydney（悉尼大学）；National University of Singapore（新加坡国立大学）；University of New South Wales（新南威尔士大学）；Nanjing University（南京大学）
SoK: The Security-Safety Continuum of Multimodal Foundation Models through Information Flow and Global Game-Theoretic Analysis of Asymmetric Threats
CSIRO’s Data61（CSIRO数据61）；University of New South Wales（新南威尔士大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Adelaide University（阿德莱德大学）；Responsible AI Research (RAIR) Centre, Adelaide University（阿德莱德大学负责任人工智能研究（RAIR）中心）
Recent Advances in Attack and Defense Approaches of Large Language Models
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Xidian University（西安电子科技大学）；StepFun（阶跃星辰）；Megvii Technology（旷视科技）
Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defenses
School of Computer and Information Engineering, Henan University（河南大学计算机与信息工程学院）；Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University（河南大学时空大数据工业技术研究院）
Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion
PandaGuard: Systematic Evaluation of LLM Safety against Jailbreaking Attacks
Beijing Institute of AI Safety and Governance (Beijing-AISI)（北京人工智能安全与治理研究院）；Beijing Key Laboratory of Safe AI and Superalignment（北京安全人工智能与超对齐重点实验室）；BrainCog Lab, CASIA（CASIA脑认知实验室）
