BazaarBench: Delegation Safety in Decentralized C2C Marketplaces Run by LLM Agents

2026-10-05

AI Summary

This study addresses the financial, privacy, and reputational security risks arising from delegation behaviors of LLM agents in decentralized C2C marketplaces, proposing the first transaction security evaluation benchmark. Methodologically, we construct a multi-agent simulated marketplace system that employs ownership and commitment state tracking mechanisms, combined with rule-based verification and LLM-as-a-judge evaluation, to systematically identify six failure modes and quantify risk disparities across standard, stress, and adversarial instructions. Experimental results demonstrate that adversarial instructions increase the violation transaction rate to 33.4% and inflate weekly revenue figures. The simulator and evaluation datasets have been released as open source.

Abstract

In decentralized consumer-to-consumer (C2C) marketplaces, people list goods, negotiate with strangers, and rate one another, so trust rests on reputation. Large language model (LLM) agents now act for users, raising risks to their money, privacy, and reputation. We introduce BazaarBench, a simulated C2C marketplace and benchmark for evaluating the safety of these agents. It tracks ownership, item condition, and commitments across transactions, combining record checks with rubric-based LLM judgments to identify six failure types across five stages. We run three base markets for 30 simulated days, each with 100 agents using one model and inventories drawn from a public eBay sample. Across 45 continuations, we evaluate five models under ordinary instructions, deadline pressure, or adversarial instructions to exploit other traders. Each continuation runs for seven simulated days from a copy of a market's day-30 state. The tested model controls the same 20 selected agents, retaining their personas, inventories, and histories, while the other 80 keep the base model. All five models attempt to promise the same item to multiple buyers under ordinary instructions. Adding targets and deadlines increases these attempts for every model. Under adversarial instructions, the share of tested sellers'committed transactions completed despite unavailable items or overstated conditions rises from 15.4% to 33.4%, reaching 55.5% for GPT-5.4. Averaged across models and markets, simulated weekly earnings per tested agent rise from USD 20 under ordinary instructions to USD 33 under adversarial instructions. Most of the increase comes from items the sellers never held. We release the simulator, saved market states, evaluation code, and records covering 357,608 agent model calls for evaluating new models and developing safer marketplace agents.

Problem

• LLM agents
• decentralized C2C marketplaces
• delegation safety
• agent trust
• adversarial behavior

Innovation

• Delegation Safety
• LLM Agents
• Benchmark
• Decentralized C2C Marketplace
• Adversarial Evaluation