AgentBnB: A Browser-Based Cybersecurity Tabletop Exercise with Large Language Model Support and Retrieval-Aligned Scaffolding
This addresses the need for lightweight, repeatable cybersecurity training for practitioners, but it is incremental as it builds on existing game frameworks with LLM integration.
The authors tackled the problem of resource-intensive and unscalable traditional cybersecurity tabletop exercises by introducing AgentBnB, a browser-based system with large language model support, which in a pilot with four graduate students increased reported intention to use and scalability compared to physical versions, though a ceiling effect was observed on a knowledge quiz.
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.