Analyzing Structural and Semantic Barriers to Automated Adversary Emulation in Active Directory

Abstract

Active Directory (AD) underpins enterprise identity management and is among the most heavily targeted assets in modern enterprise networks. When red teams emulate attackers targeting it, they typically chain BloodHound for relationship mapping, Impacket for protocol manipulation, and Responder for credential interception. Despite rapid advances in Large Language Models (LLMs), reliably automating this BIR workflow is still difficult, and the difficulty is not primarily one of reasoning. We analyze three sources of integration friction that block automation. The first is data dependency and contextual fragility, the second is temporal and procedural synchronization, and the third is semantic and operational inconsistency. We propose three architectural responses. They are tool wrappers, sidecar validators, and a shared Blackboard. Throughout, we carefully separate what we validate from what we propose. We empirically evaluate the sidecar pattern across four failure modes $(N=80)$. The wrapper and Blackboard layers are presented as architecture whose end-to-end validation remains future work. For the sidecar, deterministic pre-validation lets code, rather than the LLM, route around connectivity failures, with equivalent latency and only a modest token overhead in the structured output. The same experiment serves as a component-level ablation. It contrasts workflow behavior with and without the sidecar, converting ambiguous tool errors into structured, actionable directives.