Agentic Generative AI for Automation of Cyber Security Attack Chains in Tactical MANETs

Automating cyberattack simulations is useful for evaluating the cyber resilience of mission-critical infrastructures, such as Tactical Mobile Ad-Hoc Networks (MANETs). Traditional red teaming tools lack adaptability, integration, and scalability, which limits their applicability in dynamic military environments. This paper presents an agentic generative AI system combining Cybersecurity Knowledge Graphs (CSKGs) and Large Language Model (LLM) agents. This system autonomously generates and executes context-aware cyberattack chains. The system analyzes Cyber Threat Intelligence (CTI) data, attack tool documentation, and telemetry data to plan and orchestrate multi-stage attacks using tools such as Metasploit and Sliver within an emulated environment. This approach aligns with the Software-defined Defence (SDD) paradigm by enabling software-driven, mission-adaptive simulation of Advanced Persistent Threats (APTs). We evaluate the agent system in a controlled emulated scenario, demonstrating semi-automation from semantic threat representation to system-level exploitation involving human operators.