用大模型打造可自适应的在线网络安全演练系统
AgentBnB: A Browser-Based Cybersecurity Tabletop Exercise with Large Language Model Support and Retrieval-Aligned Scaffolding
- 引入大模型队友与检索增强提示器，动态提供认知支持
- 渐进式提示阶梯随学习者信心降低难度，提升自主性
- 小规模测试显示比传统方式更易扩展，适合个人反复练习
传统网络安全桌面推演（TTX）虽有价值，但多为脚本化、资源消耗大且难规模化。我们提出AgentBnB，一个基于浏览器的Backdoors & Breaches游戏重构版本，集成大语言模型队友与基于Bloom对齐的检索增强型协作者（C2D2）。系统将精选语料扩展为事实、概念、流程及元认知片段，按需提供认知导向提示。通过提示工程实现渐进式支架机制，随学习者信心增长逐步弱化支持。在四位研究生的单人试点中，参与者更倾向使用该代理版本，认为其更具可扩展性，但在简单知识测验中出现天花板效应。尽管样本量小、仅支持单人且语料范围有限，初步结果表明大模型增强的TTX可实现轻量、可重复的训练，无需传统推演的后勤负担。后续计划包括多人模式、基于追踪数据的个性化指导及更大规模对比研究。
原文摘要 · Abstract (English)
Traditional cybersecurity tabletop exercises (TTXs) provide valuable training but are often scripted, resource-intensive, and difficult to scale. We introduce AgentBnB, a browser-based re-imagining of the Backdoors & Breaches game that integrates large language model teammates with a Bloom-aligned, retrieval-augmented copilot (C2D2). The system expands a curated corpus into factual, conceptual, procedural, and metacognitive snippets, delivering on-demand, cognitively targeted hints. Prompt-engineered agents employ a scaffolding ladder that gradually fades as learner confidence grows. In a solo-player pilot with four graduate students, participants reported greater intention to use the agent-based version compared to the physical card deck and viewed it as more scalable, though a ceiling effect emerged on a simple knowledge quiz. Despite limitations of small sample size, single-player focus, and narrow corpus, these early findings suggest that large language model augmented TTXs can provide lightweight, repeatable practice without the logistical burden of traditional exercises. Planned extensions include multi-player modes, telemetry-driven coaching, and comparative studies with larger cohorts.
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