Title: MASpi: A Unified Environment for Evaluating Prompt Injection Robustness in LLM-Based Multi-Agent Systems
Primary area: datasets and benchmarks
Keywords: Large Language Models, Multi-Agent Systems, Prompt Injection, Benchmark

Abstract: LLM-based Multi-Agent Systems (LLM-MAS) leverage inter-agent collaboration to tackle complex tasks, yet the dense interactions among agents also make them vulnerable to prompt injection attacks. Such attacks often originate from a few compromised agents and rapidly propagate across the system, posing significant security threats. Existing studies mainly focus on a limited set of attack strategies and rely on researcher-specific implementations of LLM-MAS, which makes it difficult to adapt attacks across different systems and hinders comprehensive evaluation. To bridge this gap, we introduce MASpi, a unified environment for evaluating the prompt injection robustness of LLM-MAS. MASpi offers systematic evaluation suites spanning multiple attack surfaces (i.e., external inputs, agent profiles, inter-agent messages) and attack objectives (i.e., instruction hijacking, task disruption, information disclosure). Specifically, MASpi provides interfaces for executing 23 prompt injection attacks tailored to LLM-MAS. Its modular design enables researchers to easily integrate new LLM-MAS approaches and develop novel attack strategies on top of it. Our benchmarking results reveal that increasing the topological complexity of LLM-MAS does not guarantee security. Instead, the risks are distributed across agents, with the most harmful agent varying depending on the specific attack objective. Moreover, defenses designed for single-agent prompt injection do not reliably transfer to LLM-MAS; in fact, narrowly scoped defenses may inadvertently increase vulnerabilities to other types of attacks. MASpi aims to provide a solid foundation for the community to advance deeper exploration of security design principles in LLM-MAS.