Agent Guide: A Simple Agent Behavioral Watermarking Framework

• URL: http://arxiv.org/abs/2504.05871v1
• Date: Tue, 08 Apr 2025 09:54:49 GMT
• Title: Agent Guide: A Simple Agent Behavioral Watermarking Framework
• Authors: Kaibo Huang, Zhongliang Yang, Linna Zhou,
• Abstract summary: Agent Guide is a behavioral watermarking framework that embeds watermarks by guiding the agent's high-level decisions (behavior) through probability biases.<n>We employ a z-statistic-based statistical analysis to detect the watermark, ensuring reliable extraction over multiple rounds.<n>Our framework provides a practical and robust solution for agent watermarking, with applications in identifying malicious agents and protecting proprietary agent systems.
• Score: 9.434003291783858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing deployment of intelligent agents in digital ecosystems, such as social media platforms, has raised significant concerns about traceability and accountability, particularly in cybersecurity and digital content protection. Traditional large language model (LLM) watermarking techniques, which rely on token-level manipulations, are ill-suited for agents due to the challenges of behavior tokenization and information loss during behavior-to-action translation. To address these issues, we propose Agent Guide, a novel behavioral watermarking framework that embeds watermarks by guiding the agent's high-level decisions (behavior) through probability biases, while preserving the naturalness of specific executions (action). Our approach decouples agent behavior into two levels, behavior (e.g., choosing to bookmark) and action (e.g., bookmarking with specific tags), and applies watermark-guided biases to the behavior probability distribution. We employ a z-statistic-based statistical analysis to detect the watermark, ensuring reliable extraction over multiple rounds. Experiments in a social media scenario with diverse agent profiles demonstrate that Agent Guide achieves effective watermark detection with a low false positive rate. Our framework provides a practical and robust solution for agent watermarking, with applications in identifying malicious agents and protecting proprietary agent systems.

Related papers

• Agent-as-a-Judge: Evaluate Agents with Agents [61.33974108405561]
  We introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems. This is an organic extension of the LLM-as-a-Judge framework, incorporating agentic features that enable intermediate feedback for the entire task-solving process. We present DevAI, a new benchmark of 55 realistic automated AI development tasks.
  arXiv  Detail & Related papers  (2024-10-14T17:57:02Z)
• Robustness of Watermarking on Text-to-Image Diffusion Models [9.277492743469235]
  We investigate the robustness of generative watermarking, which is created from the integration of watermarking embedding and text-to-image generation processing. We found that generative watermarking methods are robust to direct evasion attacks, like discriminator-based attacks, or manipulation based on the edge information in edge prediction-based attacks but vulnerable to malicious fine-tuning.
  arXiv  Detail & Related papers  (2024-08-04T13:59:09Z)
• Watermarking Recommender Systems [52.207721219147814]
  We introduce Autoregressive Out-of-distribution Watermarking (AOW), a novel technique tailored specifically for recommender systems. Our approach entails selecting an initial item and querying it through the oracle model, followed by the selection of subsequent items with small prediction scores. To assess the efficacy of the watermark, the model is tasked with predicting the subsequent item given a truncated watermark sequence.
  arXiv  Detail & Related papers  (2024-07-17T06:51:24Z)
• Dissecting Adversarial Robustness of Multimodal LM Agents [70.2077308846307]
  We manually create 200 targeted adversarial tasks and evaluation scripts in a realistic threat model on top of VisualWebArena. We find that we can successfully break latest agents that use black-box frontier LMs, including those that perform reflection and tree search. We also use ARE to rigorously evaluate how the robustness changes as new components are added.
  arXiv  Detail & Related papers  (2024-06-18T17:32:48Z)
• DIP-Watermark: A Double Identity Protection Method Based on Robust Adversarial Watermark [13.007649270429493]
  Face Recognition (FR) systems pose privacy risks. One countermeasure is adversarial attack, deceiving unauthorized malicious FR. We propose the first double identity protection scheme based on traceable adversarial watermarking.
  arXiv  Detail & Related papers  (2024-04-23T02:50:38Z)
• Reliable Model Watermarking: Defending Against Theft without Compromising on Evasion [15.086451828825398]
  evasion adversaries can readily exploit the shortcuts created by models memorizing watermark samples. By learning the model to accurately recognize them, unique watermark behaviors are promoted through knowledge injection.
  arXiv  Detail & Related papers  (2024-04-21T03:38:20Z)
• Unbiased Watermark for Large Language Models [67.43415395591221]
  This study examines how significantly watermarks impact the quality of model-generated outputs. It is possible to integrate watermarks without affecting the output probability distribution. The presence of watermarks does not compromise the performance of the model in downstream tasks.
  arXiv  Detail & Related papers  (2023-09-22T12:46:38Z)
• Safe and Robust Watermark Injection with a Single OoD Image [90.71804273115585]
  Training a high-performance deep neural network requires large amounts of data and computational resources. We propose a safe and robust backdoor-based watermark injection technique. We induce random perturbation of model parameters during watermark injection to defend against common watermark removal attacks.
  arXiv  Detail & Related papers  (2023-09-04T19:58:35Z)
• Explaining Reinforcement Learning Policies through Counterfactual Trajectories [147.7246109100945]
  A human developer must validate that an RL agent will perform well at test-time. Our method conveys how the agent performs under distribution shifts by showing the agent's behavior across a wider trajectory distribution. In a user study, we demonstrate that our method enables users to score better than baseline methods on one of two agent validation tasks.
  arXiv  Detail & Related papers  (2022-01-29T00:52:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.