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.

翻译：进攻性安全领域高度碎片化：企业平台因拒绝服务风险规避内存损坏漏洞，自动漏洞利用生成系统存在语义盲区，大语言模型智能体则面临安全对齐过滤与"实弹"执行隐患。我们提出自动化利用——一种专为复杂黑盒场景中自适应进攻性安全设计的全自主多智能体系统框架。该框架通过跨多协议自主窃取可执行文件与上下文情报，利用数据驱动逻辑与二进制攻击链，弥合侦察与漏洞利用间的抽象鸿沟。框架引入自适应安全架构以缓解拒绝服务风险，在原生解决逻辑与Web漏洞的同时，对高风险内存损坏缺陷采用条件同构验证：若目标二进制文件成功被窃取，则动态实例化跨平台数字孪生体。通过实施严格状态同步（含libc对齐与运行时文件描述符挂钩），可在隔离副本中迭代调试潜在破坏性载荷，从而在物理目标上实现高风险缓解的"一次性"执行。涵盖未公开零日环境（以排除大语言模型数据污染）的八组场景实证评估验证了框架架构弹性，证明其能防止"实弹"崩溃并在真实目标上执行风险缓解式妥协。