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

Original 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 bridges the abstraction gap between reconnaissance and exploitation by autonomously exfiltrating executables and contextual intelligence across multiple protocols, using this data to fuel both logical and binary attack chains. The framework introduces an adaptive safety architecture to mitigate DoS risks. While it natively resolves logical and web-based vulnerabilities, it employs a conditional isomorphic validation for high-risk memory-corruption flaws: if the target binary is successfully exfiltrated, it dynamically instantiates a cross-platform digital twin. By enforcing strict state synchronization, including libc alignment and runtime file descriptor hooking, potentially destructive payloads are iteratively debugged in an isolated replica. This enables a highly risk-mitigated "one-shot" execution on the physical target. Empirical evaluations across eight scenarios, including undocumented zero-day environments to rule out LLM data contamination, validate the framework's architectural resilience, demonstrating its ability to prevent "live fire" crashes and execute risk-mitigated compromises on actual targets.