Automation-Exploit: A Multi-Agent LLM Framework for Adaptive Offensive Security with Digital Twin-Based Risk-Mitigated Exploitation

Abstract

The offensive security landscape is highly fragmented: enterprise platforms avoid memory-corruption vulnerabilities due to Denial of Service (DoS) risks, Automatic Exploit Generation (AEG) systems suffer from semantic blindness, and Large Language Model (LLM) agents face safety alignment filters and "Live Fire" execution hazards. We introduce Automation-Exploit, a fully autonomous Multi-Agent System (MAS) framework designed for adaptive offensive security in complex black-box scenarios. It bridg

Categories

@misc{andreucci2026automationexploit,
  title = {{Automation-Exploit: A Multi-Agent LLM Framework for Adaptive Offensive Security with Digital Twin-Based Risk-Mitigated Exploitation}},
  author = {Biagio Andreucci and Arcangelo Castiglione},
  year = {2026},
  month = apr,
  eprint = {2604.22427},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2604.22427}
}