Title: Agent Guide: A Streamlined Framework for Behavioral Watermarking in Agents

Abstract:

As intelligent agents become increasingly integrated into digital environments like social media, the need for robust traceability and accountability has grown, especially within the realms of cybersecurity and digital content safeguarding. Conventional watermarking methods designed for large language models (LLMs) typically depend on manipulating individual tokens; however, these techniques are inadequate for agents. This inadequacy stems from the difficulties associated with tokenizing behaviors and the potential for information loss when translating behaviors into actions.

To overcome these limitations, we introduce Agent Guide, an innovative framework for behavioral watermarking. This system embeds watermarks by introducing probability biases into an agent’s high-level decision-making processes (behavior), thereby maintaining the natural flow of specific executions (actions). The methodology separates agent activity into two distinct tiers: "behavior," such as the choice to bookmark content, and "action," such as bookmarking with particular tags. Watermark-guided biases are then applied to the probability distribution governing these behavioral choices.

For detection, we utilize a statistical analysis based on the z-statistic, which allows for reliable watermark extraction across multiple interaction rounds. Testing in a social media context featuring various agent profiles indicates that Agent Guide enables effective watermark detection while maintaining a low false positive rate. This framework offers a practical and resilient approach to agent watermarking, with significant potential for identifying malicious entities and securing proprietary agent systems.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC