Sybil-Resistant Service Discovery for Agent Economies

TL;DR

This paper presents TraceRank, a reputation-based ranking algorithm for service discovery in agent economies, which resists Sybil attacks by leveraging payment endorsements to identify trustworthy services.

Contribution

It introduces TraceRank, a novel reputation propagation method that enhances service discovery by integrating payment data and semantic search, improving resistance to Sybil attacks.

Findings

TraceRank effectively surfaces high-reputation services over high-volume ones.

Reputation propagation resists Sybil attacks by prioritizing trusted payers.

Combining TraceRank with semantic search yields high-quality, natural language query results.

Abstract

x402 enables Hypertext Transfer Protocol (HTTP) services like application programming interfaces (APIs), data feeds, and inference providers to accept cryptocurrency payments for access. As agents increasingly consume these services, discovery becomes critical: which swap interface should an agent trust? Which data provider is the most reliable? We introduce TraceRank, a reputation-weighted ranking algorithm where payment transactions serve as endorsements. TraceRank seeds addresses with precomputed reputation metrics and propagates reputation through payment flows weighted by transaction value and temporal recency. Applied to x402's payment graph, this surfaces services preferred by high-reputation users rather than those with high transaction volume. Our system combines TraceRank with semantic search to respond to natural language queries with high quality results. We argue that reputation propagation resists Sybil attacks by making spam services with many low-reputation payers rank below legitimate services with few high-reputation payers. Ultimately, we aim to construct a search method for x402 enabled services that avoids infrastructure bias and has better performance than purely volume based or semantic methods.