Labels Predicted by AI
Attack Strategy Analysis Indirect Prompt Injection Prompt Injection
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Abstract
Enterprise penetration-testing is often limited by high operational costs and the scarcity of human expertise. This paper investigates the feasibility and effectiveness of using Large Language Model (LLM)-driven autonomous systems to address these challenges in real-world Active Directory (AD) enterprise networks. We introduce a novel prototype designed to employ LLMs to autonomously perform Assumed Breach penetration-testing against enterprise networks. Our system represents the first demonstration of a fully autonomous, LLM-driven framework capable of compromising accounts within a real-life Microsoft Active Directory testbed, GOAD. We perform our empirical evaluation using five LLMs, comparing reasoning to non-reasoning models as well as including open-weight models. Through quantitative and qualitative analysis, incorporating insights from cybersecurity experts, we demonstrate that autonomous LLMs can effectively conduct Assumed Breach simulations. Key findings highlight their ability to dynamically adapt attack strategies, perform inter-context attacks (e.g., web-app audits, social engineering, and unstructured data analysis for credentials), and generate scenario-specific attack parameters like realistic password candidates. The prototype exhibits robust self-correction mechanisms, installing missing tools and rectifying invalid command generations. We find that the associated costs are competitive with, and often significantly lower than, those incurred by professional human pen-testers, suggesting a path toward democratizing access to essential security testing for organizations with budgetary constraints. However, our research also illuminates existing limitations, including instances of LLM “going down rabbit holes”, challenges in comprehensive information transfer between planning and execution modules, and critical safety concerns that necessitate human oversight.