While prior red-teaming efforts have focused on eliciting harmful text outputs from large language models (LLMs), such approaches fail to capture agent-specific vulnerabilities that emerge through multi-step tool execution, particularly in rapidly growing ecosystems such as the Model Context Protocol (MCP). To address this gap, we propose a trajectory-aware evolutionary search method, T-MAP, which leverages execution trajectories to guide the discovery of adversarial prompts. Our approach enables the automatic generation of attacks that not only bypass safety guardrails but also reliably realize harmful objectives through actual tool interactions. Empirical evaluations across diverse MCP environments demonstrate that T-MAP substantially outperforms baselines in attack realization rate (ARR) and remains effective against frontier models, including GPT-5.2, Gemini-3-Pro, Qwen3.5, and GLM-5, thereby revealing previously underexplored vulnerabilities in autonomous LLM agents. Code is available at https://github.com/pwnhyo/T-MAP.
REPRO: Reflective Evolutionary Prompt Optimizer for Automated LLM Red-Teaming
King Fahd University of Petroleum & Minerals（法赫德国王石油与矿产大学）；Imam Abdulrahman Bin Faisal University（伊玛目阿卜杜勒拉赫曼·本·费萨尔大学）；SDAIA-KFUPM Joint Research Center for Artificial Intelligence（SDAIA-KFUPM人工智能联合研究中心）
Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn Interaction
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
Vijil / AI Risk and Vulnerability Alliance（Vijil / AI风险与漏洞联盟）；AI Risk and Vulnerability Alliance（AI风险与漏洞联盟）；Montreal AI Ethics Institute（蒙特利尔人工智能伦理研究所）
RedAgent: Red Teaming Large Language Models with Context-aware Autonomous Language Agent
The State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全国家重点实验室）；School of Cyber Science and Technology, Zhejiang University（浙江大学网络科学与技术学院）；Palo Alto Networks（派拓网络）；University of North Texas（北得克萨斯大学）
Red Teaming LLMs as Socio-Technical Practice: From Exploration and Data Creation to Evaluation
Responsible Tech Research IBM Research Yorktown Heights New York USA（负责技术研究 IBM 研究 Yorktown Heights 新 York 美国）；IBM Research（IBM 研究）；The Pennsylvania State University（宾夕法尼亚州立大学）；University of Notre Dame（诺特达美大学）
Capability-Based Scaling Trends for LLM-Based Red-Teaming
ELLIS Institute Tübingen（图宾根ELLIS研究所）；Max Planck Institute for Intelligent Systems（智能系统马克斯·普朗克研究所）；Tübingen AI Center（图宾根人工智能中心）；Foundation AI – Cisco Systems Inc.（AI基础研究机构——思科系统公司）；EPFL（苏黎世联邦理工学院）
HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Center for AI Safety（人工智能安全中心）；Carnegie Mellon University（卡内基梅隆大学）；UC Berkeley（加州大学伯克利分校）；Microsoft（微软）
RedTopic: Toward Topic-Diverse Red Teaming of Large Language Models
Fudan University（复旦大学）；City University of Hong Kong（香港城市大学）；Deakin University（德克萨斯大学）；Hon Hai Research Institute（鸿海研究室）；Hong Kong University of Science and Technology（香港理工大学）
Summon a Demon and Bind it: A Grounded Theory of LLM Red Teaming
Paul G. Allen School of Computer Science & Engineering（保罗·G·艾伦计算机科学与工程学院）；IT University of Copenhagen（哥本哈根信息技术大学）；Berkeley Center for Human-Compatible AI, UC Berkeley（加州大学伯克利分校伯克利人类兼容人工智能中心）；NVIDIA Corporation（英伟达公司）
Automatic Red Teaming LLM-based Agents with Model Context Protocol Tools
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Palo Alto Networks（帕洛阿尔托网络公司）；School of Software Technology, Zhejiang University（浙江大学软件技术学院）
University of Toronto（多伦多大学）；Vector Institute for AI（人工智能向量研究所）；Johns Hopkins Medical Institutions（约翰霍普金斯医学机构）；University Health Network（医疗网络）；Algoma University（阿尔戈马大学）；University of Iowa Hospitals & Clinics（爱荷华大学医院与诊所）；University of Edinburgh（爱丁堡大学）
Learning diverse attacks on large language models for robust red-teaming and safety tuning
KAIST（韩国科学技术院）；Mila – Québec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）；National University of Singapore（新加坡国立大学）；University of Edinburgh（爱丁堡大学）；CIFAR（加拿大高级研究所）
Prompt Optimization and Evaluation for LLM Automated Red Teaming
Fuel iX Applied Research（Fuel iX应用研究）；North Carolina State University（北卡罗来纳州立大学）；University of Minnesota（明尼苏达大学）；TELUS Digital（TELUS数字）；Learn Prompting
