# Can Trustless Agents Be Trusted? An Empirical Study of the ERC-8004 Decentralized AI Agent Ecosystem

arXiv: https://arxiv.org/abs/2606.26028 · HTML: https://mlsanity.com/paper/2606.26028/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Builds on (most cited first, 14 of 14)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025)
- [SoK: Agentic Skills -- Beyond Tool Use in LLM Agents](https://mlsanity.com/paper/2602.20867/) (2026)
- [A Survey on Decentralized Identifiers and Verifiable Credentials](https://mlsanity.com/paper/2402.02455/) (2024)
- [DRF: LLM-AGENT Dynamic Reputation Filtering Framework](https://mlsanity.com/paper/2509.05764/) (2025)
- [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](https://mlsanity.com/paper/2511.03434/) (2025)
- [SoK: Security of Autonomous LLM Agents in Agentic Commerce](https://mlsanity.com/paper/2604.15367/) (2026)
- [SoK: Blockchain-Based Decentralized AI (DeAI)](https://mlsanity.com/paper/2411.17461/) (2024)
- [A402: Binding Cryptocurrency Payments to Service Execution for Agentic Commerce](https://mlsanity.com/paper/2603.01179/) (2026)
- [Free-Riding the Agentic Web: A Systematic Security Analysis of x402 Payments](https://mlsanity.com/paper/2605.30998/) (2026)
- [Detecting Sybil Addresses in Blockchain Airdrops: A Subgraph-based Feature Propagation and Fusion Approach](https://mlsanity.com/paper/2505.09313/) (2025)
- [Five Attacks on x402 Agentic Payment Protocol](https://mlsanity.com/paper/2605.11781/) (2026)
- [A dataset of early blockchain-registered AI agents on Ethereum](https://mlsanity.com/paper/2604.22652/) (2026)
- [The Economic Limits of Permissionless Consensus](https://mlsanity.com/paper/2405.09173/) (2024)

## Built on by (most cited first, 2 of 2)

- [MiniRep: Robust Reputation-Based Aggregation for Multi-Agent Debate](https://mlsanity.com/paper/2609.39297/) (2026)
- [SoK: Decentralized Agent Economic Infrastructure](https://mlsanity.com/paper/2610.01756/) (2026)

## Abstract (arXiv)

As autonomous AI agents increasingly transact across organizational boundaries, a fundamental trust challenge emerges: how can an agent assess whether an unknown counterpart is trustworthy? The ERC-8004 protocol addresses this challenge with the first permissionless trust layer for AI agent economies, built around three on-chain registries for Identity, Reputation, and Validation. Despite its rapid adoption, the protocol has not been studied empirically, leaving it unclear whether the information it records provides a trustworthy basis for decision-making. To address this gap, we present the first empirical study of ERC-8004 across three chains: Ethereum, BNB Smart Chain (BSC), and Base, covering the period from protocol deployment through May 13, 2026. We crawl on-chain Identity and Reputation events, off-chain files, and x402 payment transactions. On the identity side, we find that most registrations are placeholders rather than active agents, with only a small fraction (3%, 4%, and 15% across Ethereum, BSC, and Base) exposing a valid ERC-8004 registration file with at least one live service endpoint. On the reputation side, we show that the Registry, as currently deployed, cannot function as a trust signal: values are not commensurable, feedback records are rarely grounded in verifiable interactions, and reputation can be manipulated at minimal cost. Consistent with these design weaknesses, we find that a substantial fraction of reviewers (73.5%, 59.2%, and 90.6% across Ethereum, BSC, and Base) exhibit coordinated Sybil behavior. After removing Sybil-flagged feedback, 15.8%, 77.9%, and 86.8% of rated agents, respectively, are left with no valid feedback. We then turn these findings into concrete recommendations for future revisions of ERC-8004. Our study yields actionable protocol-design implications and establishes an empirical baseline for research on AI agent markets.