AI流量中的隐藏：利用MCP进行LLM驱动的代理红队攻击
Hiding in the AI Traffic: Abusing MCP for LLM-Powered Agentic Red Teaming
AI总结：
本文提出一种基于MCP的C2架构，用于LLM驱动的红队攻击，通过异步并行操作和实时情报共享，减少检测足迹并提升系统整体效能。
摘要：
生成式AI正在通过使能够自主执行渗透测试的红队代理而重塑进攻性网络安全。然而，现有方法在通用性和专门化之间存在权衡，并且实际部署揭示了诸如幻觉、上下文限制和伦理问题等挑战。在本工作中，我们介绍了一种新型的命令与控制（C2）架构，利用模型上下文协议（MCP）来协调分布式、适应性侦察代理在网络中的隐蔽操作。值得注意的是，我们的架构不仅提高了系统的整体目标导向行为，还消除了可用于检测和防止命令与控制行为的关键主机和网络痕迹。我们首先对最先进的生成式红队方法进行了全面回顾，从微调专家模型到模块化或代理框架，分析它们在任务特定准确性方面的自动化能力。然后详细说明了我们的基于MCP的C2如何通过启用异步、并行操作和实时情报共享而克服当前的限制，而无需周期性地发送信标。我们进一步探讨了该架构的高级对抗能力、检测规避技术，并讨论了双重用途的伦理影响，提出防御措施和实验室环境中的受控评估。与传统C2的实验比较显示，手动努力和检测足迹显著减少。我们最后讨论了未来方向，包括整合自主利用、防御LLM代理、预测规避动作和多代理群。所提出的MCP启用的C2框架在实现现实的、由AI驱动的红队操作方面迈出了重要一步，能够模拟高级持续威胁，同时促进下一代防御系统的发展。
英文摘要：
Generative AI is reshaping offensive cybersecurity by enabling autonomous red team agents that can plan, execute, and adapt during penetration tests. However, existing approaches face trade-offs between generality and specialization, and practical deployments reveal challenges such as hallucinations, context limitations, and ethical concerns. In this work, we introduce a novel command & control (C2) architecture leveraging the Model Context Protocol (MCP) to coordinate distributed, adaptive reconnaissance agents covertly across networks. Notably, we find that our architecture not only improves goal-directed behavior of the system as whole, but also eliminates key host and network artifacts that can be used to detect and prevent command & control behavior altogether. We begin with a comprehensive review of state-of-the-art generative red teaming methods, from fine-tuned specialist models to modular or agentic frameworks, analyzing their automation capabilities against task-specific accuracy. We then detail how our MCP-based C2 can overcome current limitations by enabling asynchronous, parallel operations and real-time intelligence sharing without periodic beaconing. We furthermore explore advanced adversarial capabilities of this architecture, its detection-evasion techniques, and address dual-use ethical implications, proposing defensive measures and controlled evaluation in lab settings. Experimental comparisons with traditional C2 show drastic reductions in manual effort and detection footprint. We conclude with future directions for integrating autonomous exploitation, defensive LLM agents, predictive evasive maneuvers, and multi-agent swarms. The proposed MCP-enabled C2 framework demonstrates a significant step toward realistic, AI-driven red team operations that can simulate advanced persistent threats while informing the development of next-generation defensive systems.
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。
