East China Normal University（东华大学）；Shanghai Innovation Institute（上海创新研究院）；University of Southampton（南安普顿大学）；Shanghai Jiao Tong University（上海交通大学）；Software Engineering Institute, East China Normal University（东华大学软件工程研究院）
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks. However, their safety remains a critical concern due to their susceptibility to adversarial prompt-based attacks. In this paper, we present UNIATTACK, an adversarial testing framework designed from a defense-oriented perspective to systematically construct effective black-box attack prompts. Unlike prior approaches that rely on static templates or iterative model-specific tuning, UNIATTACK extracts minimal but high-impact attack features from diverse existing attacks, optimizes them via a specialized attacker LLM, and composes them into flexible templates through automated refinement process. This feature-centric construction enables one-shot attacks that generalize across multiple models and safety categories, providing a practical tool for assessing LLM robustness. Our evaluation results shows that compared to the baselines, UNIATTACK achieves an average attack success rate (ASR) improvement of 64.63\%-248.82\% on models deployed with multi-layered defense mechanisms and it only takes 0.03\%-4.96\% cost of the baselines. UNIATTACK artifact is available at https://anonymous.4open.science/r/UniAttack-Artifact-30F1.
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）
GUARD-SLM: Token Activation-Based Defense Against Jailbreak Attacks for Small Language Models
Knight Foundation School of Computing and Information Sciences（奈特基金会计算与信息科学学院）；Security, Optimization, and Learning for InterDependent networks laboratory (solid lab)（安全、优化与相互依赖网络学习实验室）；Florida International University（佛罗里达国际大学）
SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner
The Hong Kong University of Science and Technology（香港科技大学）；University of Oregon（俄勒冈大学）；Nanyang Technological University（南洋理工大学）；City University of Hong Kong（香港城市大学）；HSBC（汇丰银行）
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）
FRACTURED-SORRY-Bench: Framework for Revealing Attacks in Conversational Turns Undermining Refusal Efficacy and Defenses over SORRY-Bench (Automated Multi-shot Jailbreaks)
Activation-Guided Local Editing for Jailbreaking Attacks
Beihang University（北航）；Hangzhou Innovation Institute（杭州创新研究院）；HKUST（香港科技大学）；South China University of Technology（华南理工大学）；Nanyang Technological University（南洋理工大学）；Kunming University of Science and Technology（昆明理工大学）
Mind the Trojan Horse: Image Prompt Adapter Enabling Scalable and Deceptive Jailbreaking
School of Computer Science and Engineering, Sun Yat-Sen University（中山大学计算机科学与工程学院）；Nanyang Technological University（南洋理工大学）；Guangdong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）
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）
Test-Time Immunization: A Universal Defense Framework Against Jailbreaks for (Multimodal) Large Language Models
NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences（国家工程实验室与人工智能研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Data to Defense: The Role of Curation in Customizing LLMs Against Jailbreaking Attacks
Stony Brook University（石溪大学）；Binghamton University（宾汉姆顿大学）；University of Iowa（爱荷华大学）；Dartmouth College（达特茅斯学院）；Georgia Institute of Technology（佐治亚理工学院）
CCFC: Core & Core-Full-Core Dual-Track Defense for LLM Jailbreak Protection
Department. of Math & Statistics, Boston University（数学与统计学系，波士顿大学）；Department. of Computer Science, University at Albany（计算机科学系，阿尔巴尼大学）；Department. of ECE & Systems Eng., Department. of Biomedical Eng., Faculty of Computing & Data Sciences, Boston University（电子工程与系统工程系，生物医学工程系，计算与数据科学学院，波士顿大学）
