The evolution of Large Language Models (LLMs) into agentic systems that perform autonomous reasoning and tool use has created significant intellectual property (IP) value. We demonstrate that these systems are highly vulnerable to imitation attacks, where adversaries steal proprietary capabilities by training imitation models on victim outputs. Crucially, existing LLM watermarking techniques fail in this domain because real-world agentic systems often operate as grey boxes, concealing the internal reasoning traces required for verification. This paper presents AGENTWM, the first watermarking framework designed specifically for agentic models. AGENTWM exploits the semantic equivalence of action sequences, injecting watermarks by subtly biasing the distribution of functionally identical tool execution paths. This mechanism allows AGENTWM to embed verifiable signals directly into the visible action trajectory while remaining indistinguishable to users. We develop an automated pipeline to generate robust watermark schemes and a rigorous statistical hypothesis testing procedure for verification. Extensive evaluations across three complex domains demonstrate that AGENTWM achieves high detection accuracy with negligible impact on agent performance. Our results confirm that AGENTWM effectively protects agentic IP against adaptive adversaries, who cannot remove the watermarks without severely degrading the stolen model's utility.
Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attacks
SBA Research（SBA研究中心）；Vienna University of Technology（维也纳工业大学）
SSCL-BW: Sample-Specific Clean-Label Backdoor Watermarking for Dataset Ownership Verification
School of Control and Computer Engineering（控制与计算机工程学院）；North China Electric Power University（华北电力大学）；Research Institute（研究院）；China Unicom（中国联合网络通信集团）；Technical Department（技术部）
Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarking
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tsinghua University（清华大学）；The Hong Kong University of Science and Technology（香港科技大学）
Hot-Swap MarkBoard: An Efficient Black-box Watermarking Approach for Large-scale Model Distribution
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 the Chinese Academy of Sciences（中国科学院大学网络安全学院）；Nanyang Technological University（南洋理工大学）；ShanghaiTech University（上海科技大学）
4D-GSW: Kinematic-Aware Spatio-Temporal Consistent Watermarking for 4D Gaussian Splatting
Southeast University（东南大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy（广东人工智能与数字经济实验室）；University of Chinese Academy of Sciences（中国科学院大学）
VLA-Mark: A cross modal watermark for large vision-language alignment model
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；University of Toronto（多伦多大学）；Ant Group, Alibaba（蚂蚁集团，阿里巴巴）；New York University Shanghai（纽约大学上海分校）
