Autonomous Active Directory hacking system outperforms standalone AI tools
Autonomous Active Directory Exploitation via Multi-Model Harness Orchestration: A Benchmark Study with NeuroSploit on GOAD
Cryptography and Security
Summary
Active Directory (AD) is a key system that controls access inside many companies, and breaking into it is a major goal for security testing. The authors show that using a carefully organized system with many specialized AI agents working together can hack AD much more reliably and precisely than simply using one AI model alone. Their system, NeuroSploit, was tested in a complex virtual AD environment and successfully performed nearly all attack techniques quickly and with few mistakes. This shows that structured collaboration among AI components can improve cybersecurity testing.
What this means in practice
- •For enterprise security teams: Use NeuroSploit to automate thorough Active Directory security assessments with higher accuracy and reduced false alerts.
- •For cloud infrastructure engineers: Adapt structured AI-based attack orchestration for evaluating identity systems in complex multi-domain cloud environments.
Authors
Joas Antonio dos Santos Barbosa
Abstract
Active Directory (AD) remains the predominant identity and access management infrastructure in enterprise environments, and its compromise represents the highest-impact outcome in internal penetration tests. Recent work has shown that large language models (LLMs) can autonomously conduct assumed-breach penetration testing against AD, but these studies employ standalone agents lacking structured guardrails, deterministic validation, and multi-stage chain orchestration. We present a benchmark evaluation of NeuroSploit v4.2.0, an open-source Rust-based autonomous pentest harness, against the Game of Active Directory (GOAD), a deliberately vulnerable multi-forest AD lab maintained by Orange Cyberdefense comprising five virtual machines, two forests, and three domains. The harness orchestrates 22 AD-specific agents and 7 multi-stage attack-chain playbooks covering the full AD kill chain: enumeration, Kerberoasting, AS-REP roasting, NTLM relay and coercion, Kerberos delegation abuse, AD CS exploitation (ESC1-ESC8), MSSQL linked-server pivoting, DCSync, cross-forest trust abuse, and persistence detection. We benchmark nine frontier LLMs (Claude Opus 4.6/4.7/4.8, GPT-6 Astra, GPT-5.6 Sol, Grok 4.6, Qwen 3.8, GLM 5.3, Kimi k3) within the harness, comparing against direct invocation across 14 technique categories and 7 chains. The harness achieves 96-100% technique coverage with 90-97% precision, while direct invocation covers only 21-54% and produces 3.2x more false positives. Time to full three-domain compromise with Opus 4.8 was 134 minutes with guardrail activations preventing lockout-triggering sprays, unauthorized DCSync dumps, and out-of-scope reconnaissance. Results demonstrate that structured harness orchestration with domain-specialized agents, POMDP belief tracking, and cross-model voting substantially outperforms unstructured LLM usage for AD penetration testing.