Dynamic Cyber Ranges

TL;DR

This paper introduces Dynamic Cyber Ranges with LLM-driven defender agents that adaptively harden infrastructure and respond to threats, significantly improving cybersecurity evaluation realism and effectiveness.

Contribution

It proposes a novel dynamic cyber range framework with AI-powered defenders, demonstrating improved attack prevention and faster detection compared to static environments.

Findings

Defender agents reduced attacker success to 0-55%.

A smaller on-premise model matched large model defenses.

Defenders detected attackers 10x faster in complex scenarios.

Abstract

As LLM-driven agents advance in cybersecurity, Jeopardy CTF benchmarks are approaching saturation and cyber ranges, the natural next evaluation frontier, offer diminishing resistance under their current static design. We validate this observation by deploying an LLM-driven Advanced Persistent Threat (APT) agent across three tiers of increasingly realistic infrastructure (PRO Labs, MHBench, military-grade CYBER RANGES). To counteract this trend, we propose Dynamic Cyber Ranges: cyber range environments augmented with LLM-driven Defender agents that harden infrastructure, monitor for intrusions, and respond in real time. Across evaluated scenarios, Defender agents reduce attacker success to 0-55%, achieving complete prevention on multiple configurations. Since attacker and defender agents draw from the same underlying model capabilities, Dynamic Cyber Ranges preserve evaluation headroom as models improve. Notably, a smaller, specialized on-premise model (alias2-mini) matched the frontier model's defensive outcomes on multiple scenarios under identical, untuned prompts, and detected the attacker 10x faster on a complex enterprise scenario, suggesting that privacy-preserving on-premise models can serve as competent defenders against frontier-class attackers. The experiments further surface emergent agent behaviors, including scope expansion and prompt exfiltration, with implications for AI benchmark integrity and agentic system design.