Across large enterprises, a single question keeps surfacing when teams want to put customer data to work. Can this record be used for a given purpose, and does the consent behind it still hold? The data sits in warehouses and customer databases, and the ability to answer that question often lags behind. That delay carries a cost. Many enterprises have seen AI initiatives stall over the past year, and the ones stalling most often carry … More →
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Android Vulnerability Researcher Byteria | USA | Remote – View job details As an Android Vulnerability Researcher, you will analyze the Android attack surface, including the Linux kernel, system services, drivers, firmware, applications, and Trusted Execution Environment (TEE). You will reverse engineer native binaries and mobile software, identify vulnerabilities through code review, fuzzing, and static and dynamic analysis, and develop proof-of-concept exploits to validate findings. Application Security Engineer Millennium | Ireland | On-site – View … More →
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这份文档《AI辅助漏洞挖掘系统设计指南》系统阐述了一套基于大语言模型的自动化安全测试平台构建方法论。其核心设计哲学是"不束缚AI，只设边界"——不教AI如何做渗透测试，而是为其划定行为禁区与报告标准，充分利用模型自带的海量安全知识。
文档提出与传统扫描器截然不同的架构：以单一"核心技能文件"替代繁杂规则库，通过"垃圾洞清单"过滤噪音、"七问验证门"把控报告质量、"速查卡"对抗AI遗忘机制，并采用决策树引导AI动态选择测试路径。系统架构涵盖前端仪表盘、后端调度服务、AI工作引擎及LLM API四层，支持会话生命周期管理、并发控制与实时通信。
关键创新在于将"现象"与"漏洞"严格区分——只报可证明的结果（如越权、RCE），不报过程性发现（如配置缺失、信息泄露）。文档还详细设计了安全防护、磁盘监控、自动重连等机制，并提供从零搭建的三阶段路线图，强调核心技能文件的持续迭代是系统竞争力的根本来源。