MAStitch: Unifying Local and Global Perspectives for Anomaly Detection in Multi-Agent Systems

Large language model (LLM)-based multi-agent systems (MASs) increasingly serve as decision-making and automation pipelines in diverse application domains. Although these systems are highly relied upon in many settings, they have been shown to be vulnerable to various threats, including threats exploited by malicious actors (e.g., adversarial attacks) and threats stemming from dangerous behavior of the system (e.g., insecure code generation). Those threats can be viewed as deviations from normal behavior (anomalies), requiring a robust anomaly detection solution. Most approaches for detecting such anomalies concentrate on individual agents, specific failure scenarios, or particular use cases, and often require additional training phases, limiting their applicability and ease of adoption across diverse MASs. To address these limitations, we introduce MAStitch, a platform- and threat-agnostic method for threat detection in MASs that does not require any training, making it well-suited for diverse environments and scenarios requiring plug-and-play detection capabilities. MAStitch leverages local and global perspectives, enabling comprehensive analysis of inter-agent interactions and intra-agent processes to detect anomalies. These perspectives are acquired by a pair of LLM-based agents: a Local Analyzer Agent (LAA), which evaluates each agent’s execution process, and a Global Analyzer Agent (GAA), which aggregates evidence from multiple agents to detect cross-agent failures. The GAA summarizes the anomalous patterns identified, classifies the entire execution log, and provides an explanation when threats are detected. We evaluate our method’s detection performance on six different MAS applications which were developed on two widely used MAS platforms. The evaluation results demonstrate MAStitch’s ability to detect a variety of threats, with an average F1 score of 0.9 and a minimal false positive rate of 0.078.