Shadows in the Code: Exploring the Risks and Defenses of LLM-based Multi-Agent Software Development Systems

TL;DR

This paper investigates security vulnerabilities in LLM-based multi-agent software development systems, introduces a novel attack method called IMBIA, and proposes defenses, revealing significant risks and mitigation strategies.

Contribution

It introduces IMBIA, a new attack technique for multi-agent systems, and evaluates its effectiveness and defenses across multiple frameworks, highlighting critical security vulnerabilities.

Findings

IMBIA achieves high success rates in attack scenarios.

Defense mechanisms significantly reduce attack success.

Compromised agents in coding/testing pose higher risks.

Abstract

The rapid advancement of Large Language Model (LLM)-driven multi-agent systems has significantly streamlined software developing tasks, enabling users with little technical expertise to develop executable applications. While these systems democratize software creation through natural language requirements, they introduce significant security risks that remain largely unexplored. We identify two risky scenarios: Malicious User with Benign Agents (MU-BA) and Benign User with Malicious Agents (BU-MA). We introduce the Implicit Malicious Behavior Injection Attack (IMBIA), demonstrating how multi-agent systems can be manipulated to generate software with concealed malicious capabilities beneath seemingly benign applications, and propose Adv-IMBIA as a defense mechanism. Evaluations across ChatDev, MetaGPT, and AgentVerse frameworks reveal varying vulnerability patterns, with IMBIA achieving attack success rates of 93%, 45%, and 71% in MU-BA scenarios, and 71%, 84%, and 45% in BU-MA scenarios. Our defense mechanism reduced attack success rates significantly, particularly in the MU-BA scenario. Further analysis reveals that compromised agents in the coding and testing phases pose significantly greater security risks, while also identifying critical agents that require protection against malicious user exploitation. Our findings highlight the urgent need for robust security measures in multi-agent software development systems and provide practical guidelines for implementing targeted, resource-efficient defensive strategies.