Large language models remain vulnerable to jailbreak attacks, and single-layer defenses often trade security for usability. We present TRYLOCK, the first defense-in-depth architecture that combines four heterogeneous mechanisms across the inference stack: weight-level safety alignment via DPO, activation-level control via Representation Engineering (RepE) steering, adaptive steering strength selected by a lightweight sidecar classifier, and input canonicalization to neutralize encoding-based bypasses. On Mistral-7B-Instruct evaluated against a 249-prompt attack set spanning five attack families, TRYLOCK achieves 88.0% relative ASR reduction (46.5% to 5.6%), with each layer contributing unique coverage: RepE blocks 36% of attacks that bypass DPO alone, while canonicalization catches 14% of encoding attacks that evade both. We discover a non-monotonic steering phenomenon -- intermediate strength (alpha=1.0) degrades safety below baseline -- and provide mechanistic hypotheses explaining RepE-DPO interference. The adaptive sidecar reduces over-refusal from 60% to 48% while maintaining identical attack defense, demonstrating that security and usability need not be mutually exclusive. We release all components -- trained adapters, steering vectors, sidecar classifier, preference pairs, and complete evaluation methodology -- enabling full reproducibility.
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）
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）
Jailbreaking LLMs & VLMs: Mechanisms, Evaluation, and Unified Defense
School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络空间安全学院）；China Academy of Information and Communications Technology（中国信息通信研究院）
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）
DoubtProbe: Black-Box Jailbreak Defense via Structural Verification and Semantic Auditing
East China Normal University（东华大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
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）
TeleAI-Safety: A comprehensive LLM jailbreaking benchmark towards attacks, defenses, and evaluations
Institute of Artificial Intelligence (TeleAI) of China Telecom（中国电信人工智能研究院（TeleAI））；Shanghai Jiao Tong University（上海交通大学）；Sichuan University（四川大学）；University of Chinese Academy of Sciences（中国科学院大学）；Chinese Academy of Sciences（中国科学院）；University of Science and Technology of China（中国科学技术大学）；Peking University（北京大学）；Harbin Institute of Technology（哈尔滨工业大学）
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）
ShieldLearner: A New Paradigm for Jailbreak Attack Defense in LLMs
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Institute of Artificial Intelligence, Beijing University of Aeronautics and Astronautics（北京航空航天大学人工智能研究院）
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）
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）
