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Inter-Agent Trust Models: A Comparative Study of Brief, Claim, Proof, Stake, Reputation and Constraint in Agentic Web Protocol Design-A2A, AP2, ERC-8004, and Beyond

Authors: Botao 'Amber' Hu, Helena Rong

Published: 2025-11-05

arXiv ID: 2511.03434v1

Added to Library: 2025-11-14 23:05 UTC

Red Teaming

📄 Abstract

As the "agentic web" takes shape-billions of AI agents (often LLM-powered) autonomously transacting and collaborating-trust shifts from human oversight to protocol design. In 2025, several inter-agent protocols crystallized this shift, including Google's Agent-to-Agent (A2A), Agent Payments Protocol (AP2), and Ethereum's ERC-8004 "Trustless Agents," yet their underlying trust assumptions remain under-examined. This paper presents a comparative study of trust models in inter-agent protocol design: Brief (self- or third-party verifiable claims), Claim (self-proclaimed capabilities and identity, e.g. AgentCard), Proof (cryptographic verification, including zero-knowledge proofs and trusted execution environment attestations), Stake (bonded collateral with slashing and insurance), Reputation (crowd feedback and graph-based trust signals), and Constraint (sandboxing and capability bounding). For each, we analyze assumptions, attack surfaces, and design trade-offs, with particular emphasis on LLM-specific fragilities-prompt injection, sycophancy/nudge-susceptibility, hallucination, deception, and misalignment-that render purely reputational or claim-only approaches brittle. Our findings indicate no single mechanism suffices. We argue for trustless-by-default architectures anchored in Proof and Stake to gate high-impact actions, augmented by Brief for identity and discovery and Reputation overlays for flexibility and social signals. We comparatively evaluate A2A, AP2, ERC-8004 and related historical variations in academic research under metrics spanning security, privacy, latency/cost, and social robustness (Sybil/collusion/whitewashing resistance). We conclude with hybrid trust model recommendations that mitigate reputation gaming and misinformed LLM behavior, and we distill actionable design guidelines for safer, interoperable, and scalable agent economies.

🔍 Key Points

  • The paper presents a comparative analysis of six distinct trust models—Brief, Claim, Proof, Stake, Reputation, and Constraint—in inter-agent protocol design, highlighting their strengths and weaknesses in the context of autonomous agents.
  • It identifies specific vulnerabilities related to large language models (LLMs) such as prompt injection and hallucination, advocating for a hybrid trust architecture that combines Proof and Stake with Brief and Reputation for enhanced security and resilience.
  • The authors propose a tiered trust system (T0-T3) tailored to different risk levels, where trust mechanisms can be escalated based on the potential impact of actions taken by AI agents.
  • The research evaluates existing protocols like Google’s A2A, AP2, and Ethereum’s ERC-8004 against trust models, providing actionable design guidelines and frameworks to enhance protocol effectiveness and security.
  • The paper emphasizes the need for continuous monitoring and auditability, ensuring trust is dynamic and context-aware, rather than static.

💡 Why This Paper Matters

This paper is crucial as it addresses the foundational shift in trust dynamics caused by the rise of the agentic web, where billions of AI agents operate autonomously. By dissecting trust models and their specific implications for AI protocols, this research lays the groundwork for developing robust inter-agent interactions that can secure AI operations in complex environments. The insights provided will significantly influence future protocols, enhancing their resilience against manipulation and ensuring they align with ethical standards.

🎯 Why It's Interesting for AI Security Researchers

This paper is of great interest to AI security researchers as it deals with the critical issue of trust in multi-agent systems, particularly in the rapidly evolving context of LLMs. It systematically addresses the vulnerabilities associated with these models and offers practical frameworks and guidelines for building safer protocols. The implications of this research extend to enhancing the security standards of AI interactions, making it a must-read for anyone interested in the intersection of AI, cybersecurity, and trust management.

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