Large language models (LLMs) are increasingly used in cybersecurity workflows, yet it remains unclear whether they can perform structured security reasoning or merely rely on superficial cues and prior knowledge. We study this question in the context of defence selection over attack graphs derived from real-world threat scenarios, including ransomware, supply-chain compromise, cloud abuse, Kubernetes attacks, POS malware, and ICS/OT intrusion. Given a budget constraint, LLMs must select security controls to minimise attacker success. We compare their strategies against each other and against a game-theoretic optimization baseline used as a normative reference for structured reasoning. Our results show that LLMs exhibit conditional competence. When explicit attack-graph structure is provided, they often produce coherent strategies close to the optimization baseline. However, their capabilities are fragile. LLM behaviour becomes increasingly fragile with graph complexity and is highly sensitive to framing. Small prompt changes can substantially alter rankings, and merely relabeling a poor strategy as ``optimal'' dramatically improves its evaluation. We further observe a non-monotonic relationship between formal risk and LLM judgement: strategies closest to the optimum are not necessarily ranked highest by LLM evaluators. To further probe reasoning ability, we ask LLMs to generate solvers for the same optimization problem. While the generated implementations recover the correct high-level formulation, they scale poorly compared to a purpose-built solver. Overall, our findings show that LLMs can approximate structured cybersecurity reasoning under controlled representations, but do not apply it robustly. This has important implications for the design and evaluation of AI-assisted security decision-support systems.
Microsoft's Active Directory (AD) is a directory service that enables the IT admin to manage security permissions and control access within a Windows domain network. As a core management system in many of organisation, AD has become a primary target for adversaries. While many solutions for hardening attack graphs exist, these efforts fall short in addressing several key practical challenges specific to the AD attack graph. First, existing models often assume the graph is static, whereas a real-world AD environment is highly dynamic. Second, most proposed solutions are limited to the defensive measure of revoking vulnerabilities (edge removal), while more active defence mechanisms are largely unstudied. Third, because not all remediations are implementable, a practical end-to-end model must incorporate system admin feedback into the prioritisation process. This thesis aims to address these limitations by studying and proposing a number of game-theoretic and optimisation-based decision-making models. First, we propose a honeypot/decoy placement model based on the principle of minimising the number of shortest paths and the number of Domain Admin-reachable nodes. Second, building on this model, we introduce a defence strategy that considers the dynamic/temporal nature of the AD graph, where the objective is to find the location to deploy decoys that maximises the worst-case incident response time. Third, we introduce an adaptive prioritisation model that queries each high-risk attack path to the IT administrator for mediation. Finally, we introduce an end-to-end adaptive prioritisation model that minimises the approval effort of the system admin by finding a general adaptive edge-removal policy that generalises the system admin's decisions to edges with similar risk features. We show that the problems underlying all of the contributed models are computationally intractable.
i-EXAM is a planning-powered tool that helps system administrators to create security profiles of complex networks and perform what-if analyses to identify network hardening strategies. It leverages planning compilation that provides soundness and completeness guarantees to identify attack paths, evaluate security metrics, generate diverse hardening strategies, and explain these strategies in natural language using Large Language Models.