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

Authors: Xiaoqing Wang, Keman Huang, Bin Liang, Hongyu Li, Xiaoyong Du

Published: 2025-11-23

arXiv ID: 2511.18467v1

Added to Library: 2025-11-25 03:01 UTC

Safety

📄 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.

🔍 Key Points

  • Introduction of the Implicit Malicious Behavior Injection Attack (IMBIA) targeting multi-agent software development systems, demonstrating how malicious behaviors can be concealed within seemingly harmless applications.
  • Proposal of the Adv-IMBIA defense mechanism, showing significant reduction in attack success rates across various multi-agent frameworks.
  • Evaluation of the attack and defense methodologies across three different software development systems (ChatDev, MetaGPT, and AgentVerse), providing empirical evidence of vulnerabilities and effective countermeasures.
  • Identification of critical phases (coding and testing) and agents important for cybersecurity within multi-agent systems, highlighting the need for targeted defenses during these stages.
  • Provision of practical guidelines for implementing resource-efficient security mechanisms, making it feasible for organizations to bolster their software development processes against malicious exploitation.

💡 Why This Paper Matters

This paper presents crucial insights into the security vulnerabilities associated with LLM-driven multi-agent systems in software development. By introducing novel attack methodologies and defensive strategies, it underscores the urgent need for enhanced security protocols in software development processes, particularly in phases where risks are significantly heightened. The findings serve as a foundation for future research and practical implementations aimed at mitigating risks in increasingly complex software development environments.

🎯 Why It's Interesting for AI Security Researchers

This paper is of significant interest to AI security researchers as it not only addresses a critical and emerging area of study in the context of LLMs and multi-agent systems but also introduces new attack and defense methodologies. The detailed empirical analysis and the identification of vulnerable phases and roles within a software development pipeline provide a critical reference point for developing robust security measures in AI applications, enhancing the understanding of how malicious exploitation can occur in complex systems.

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