扩展OWASP多智能体系统威胁建模指南：来自多智能体安全研究的洞察
Extending the OWASP Multi-Agentic System Threat Modeling Guide: Insights from Multi-Agent Security Research
AI总结：
本文扩展了OWASP多智能体系统威胁建模指南，通过引入推理崩溃、不安全委派升级等新威胁类别及评估策略，填补了LLM驱动的多智能体架构在安全建模上的空白，提升了复杂自适应系统的安全性与弹性。
摘要：
我们提议对OWASP Multi-Agentic System (MAS) Threat Modeling Guide进行扩展，将多智能体安全（MASEC）领域的最新预期性研究转化为实践指导，以应对大型语言模型（LLM）驱动的多智能体架构所独有的挑战。尽管OWASP现有的分类法涵盖了多种攻击向量，但我们的分析识别出在建模故障方面存在的空白，包括但不限于：planner-executor链中的推理崩溃、指标过拟合、不安全的委派升级、涌现的隐蔽协调以及异构多智能体漏洞利用。我们引入了基于实际MAS部署的额外威胁类别和场景，强调了良性目标漂移、跨智能体幻觉传播、情感提示框架和多智能体后门带来的风险。我们还概述了评估策略，包括鲁棒性测试、协调评估、安全执行和涌现行为监控，以确保全面覆盖。这项工作通过将OWASP框架的适用性扩展到日益复杂、自主和自适应的多智能体系统，补充了OWASP框架，旨在改善现实世界部署中的安全态势和弹性。
英文摘要：
We propose an extension to the OWASP Multi-Agentic System (MAS) Threat Modeling Guide, translating recent anticipatory research in multi-agent security (MASEC) into practical guidance for addressing challenges unique to large language model (LLM)-driven multi-agent architectures. Although OWASP's existing taxonomy covers many attack vectors, our analysis identifies gaps in modeling failures, including, but not limited to: reasoning collapse across planner-executor chains, metric overfitting, unsafe delegation escalation, emergent covert coordination, and heterogeneous multi-agent exploits. We introduce additional threat classes and scenarios grounded in practical MAS deployments, highlighting risks from benign goal drift, cross-agent hallucination propagation, affective prompt framing, and multi-agent backdoors. We also outline evaluation strategies, including robustness testing, coordination assessment, safety enforcement, and emergent behavior monitoring, to ensure complete coverage. This work complements the framework of OWASP by expanding its applicability to increasingly complex, autonomous, and adaptive multi-agent systems, with the goal of improving security posture and resilience in real world deployments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
