研究多智能体网络中子智能体继承的漏洞，揭示安全风险并提出防御方案。
When Child Inherits: Modeling and Exploiting Subagent Spawn in Multi-Agent Networks
- 从父智能体继承内存可能传播恶意指令
- 实测发现现有框架存在信任边界失效问题
- 适合关注AI系统安全与多智能体设计的研究者
自2022年ChatGPT发布以来，大型语言模型已从聊天机器人演变为可通过工具和新生成子智能体委派任务的代理系统。尽管提升了自动化与可扩展性，也带来了多智能体网络中的新安全风险。现有研究关注单个基于LLM的智能体如何通过提示注入、越狱、污染检索数据或恶意扩展被攻破，但较少关注一个智能体被攻破后在多智能体网络中的传播效应。特别是，父智能体的继承记忆可能携带恶意指令、过时状态或非预期行为规则，使局部入侵扩散至新生成的子智能体。本文通过子智能体继承视角建模当前多智能体网络，分析发现现有框架存在不安全的记忆继承、弱资源控制、孵化后状态过期及终止权限不当等问题。我们在真实代理框架中验证了这些风险，并提出了基于显式安全不变量的防御机制。结果表明，继承不仅是实现细节，更是影响多智能体系统安全的核心要素。
原文摘要 · Abstract (English)
Since the official release of ChatGPT in 2022, large language models (LLMs) have rapidly evolved from chatbot-style interfaces into agentic systems that can delegate work through tools and newly spawned subagents. While these capabilities improve automation and scalability, they also pose new security risks in multi-agent networks. Existing research has studied how individual LLM-based agents can be compromised through prompt injection, jailbreaking, poisoned retrieval data, or malicious extensions. Less is known about what happens after one agent is compromised inside a multi-agent network. In particular, inherited memory from parent agents can carry malicious instructions, outdated states, or unintended behavioral rules into newly created subagents, allowing a local compromise to spread across agent boundaries. In this paper, we model contemporary multi-agent networks through the lens of subagent inheritance. Our analysis shows that current frameworks can violate trust boundaries through insecure memory inheritance, weak resource control, stale post-spawn state, and improper termination authority. We demonstrate these risks in real agent frameworks and propose defenses based on explicit security invariants. Our findings show that inheritance is not merely an implementation detail, but a central component influencing the security of multi-agent systems.
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