LLMs Killed the Script Kiddie: How Agents Supported by Large Language
  Models Change the Landscape of Network Threat Testing
Stephen Moskal Sam Laney Erik Hemberg Una-May O'Reilly
In this paper, we explore the potential of Large Language Models (LLMs) to
reason about threats, generate information about tools, and automate cyber
campaigns. We begin with a manual exploration of LLMs in supporting specific
threat-related actions and decisions. We proceed by automating the decision
process in a cyber campaign. We present prompt engineering approaches for a
plan-act-report loop for one action of a threat campaign and and a prompt
chaining design that directs the sequential decision process of a multi-action
campaign. We assess the extent of LLM's cyber-specific knowledge w.r.t the
short campaign we demonstrate and provide insights into prompt design for
eliciting actionable responses. We discuss the potential impact of LLMs on the
threat landscape and the ethical considerations of using LLMs for accelerating
threat actor capabilities. We report a promising, yet concerning, application
of generative AI to cyber threats. However, the LLM's capabilities to deal with
more complex networks, sophisticated vulnerabilities, and the sensitivity of
prompts are open questions. This research should spur deliberations over the
inevitable advancements in LLM-supported cyber adversarial landscape.