[2601.03294] AgentMark: Utility-Preserving Behavioral Watermarking for Agents

Computer Science > Cryptography and Security arXiv:2601.03294 (cs) [Submitted on 5 Jan 2026 (v1), last revised 24 Apr 2026 (this version, v2)] Title:AgentMark: Utility-Preserving Behavioral Watermarking for Agents Authors:Kaibo Huang, Jin Tan, Yukun Wei, Wanling Li, Zipei Zhang, Hui Tian, Zhongliang Yang, Linna Zhou View a PDF of the paper titled AgentMark: Utility-Preserving Behavioral Watermarking for Agents, by Kaibo Huang and 7 other authors View PDF HTML (experimental) Abstract:LLM-based agents are increasingly deployed to autonomously solve complex tasks, raising urgent needs for IP protection and regulatory provenance. While content watermarking effectively attributes LLM-generated outputs, it fails to directly identify the high-level planning behaviors (e.g., tool and subgoal choices) that govern multi-step execution. Critically, watermarking at the planning-behavior layer faces unique challenges: minor distributional deviations in decision-making can compound during long-term agent operation, degrading utility, and many agents operate as black boxes that are difficult to intervene in directly. To bridge this gap, we propose AgentMark, a behavioral watermarking framework that embeds multi-bit identifiers into planning decisions while preserving utility. It operates by eliciting an explicit behavior distribution from the agent and applying distribution-preserving conditional sampling, enabling deployment under black-box APIs while remaining compatible with action-la...