MAADBench: The Refreshable Paradigm for Anomaly Detection in Multi-Agent Systems

2026-09-28

AI Summary

This study addresses the susceptibility of existing benchmarks for anomaly detection in LLM-based multi-agent systems (MAS) to data leakage and their inability to be refreshed. To overcome these limitations, this work proposes the first refreshable MAS anomaly detection benchmark. Methodologically, it constructs a sampling-coupled generation paradigm alongside a task space of 10^37 scale. By integrating configurable LLM backend tracing with automated annotation techniques, the framework enables deterministic, step-level fine-grained evaluation. The authors release a dataset comprising 5,200 annotated trajectories. Empirical evaluations leveraging this benchmark reveal significant limitations in current methods regarding robustness and the identification of subtle anomalies.

Abstract

Recent studies report that LLM-based multi-agent systems (MAS) fail at rates of 41%-87%, yet to our knowledge, no benchmark to date supports systematic anomaly detection (AD) for them. Building MAS AD benchmarks is hard because they must remain fresh as LLM systems evolve: tasks may leak into training data and thus be memorized by LLMs, traces and anomaly patterns expire as backbones evolve, and labels must be provided reliably for each refresh. To address these challenges, we present MAADBench (MA: multi-agent; AD: anomaly detection), the first refreshable MAS AD benchmark designed for diverse, evolving LLM backbones underlying the agents. MAADBench combines (1) sampled-and-coupled generative tasks over an approximately 10^37-task space to mitigate task leakage, (2) refreshable trace generation under configurable LLM backbones, and (3) automated provision of cost-free, deterministic step-level labels for fine-grained AD evaluation. Beyond offering the paradigm itself, we run MAADBench with five state-of-the-art LLM backbones and release the MAADBench-Full dataset with 5,200 step-labeled traces. Benchmarking 25 AD methods on the MAADBench dataset reveals substantial limitations in current approaches: they rely heavily on supervision, struggle with subtle MAS-specific anomalies, and lack robustness across LLM backbones. These gaps point to a rich research agenda for MAS-specific anomaly detection, with MAADBench providing a systematic and refreshable testbed for method development and evaluation. We open-source MAADBench-Full at https://huggingface.co/datasets/hww123/MAADBench-full.

Problem

• multi-agent systems
• anomaly detection
• benchmark
• large language models
• task leakage

Innovation

• Multi-Agent Systems
• Anomaly Detection
• Refreshable Benchmark
• Large Language Models
• Step-level Labels