揭示隐藏接口：LLM引导的类型推断用于macOS私有框架逆向工程
Exposing Hidden Interfaces: LLM-Guided Type Inference for Reverse Engineering macOS Private Frameworks
AI总结：
MOTIF通过LLM引导的类型推断技术，显著提升了对macOS私有框架的逆向工程能力，实现了更高的签名恢复率和更稳定的推断结果。
摘要：
在macOS中，私有框架是关键服务和守护进程的基础，但它们仍然未被文档化，并且只以剥离二进制文件的形式分发，这给安全分析带来了困难。我们提出了MOTIF，一个集成了工具增强分析与经过微调的大型语言模型（专门用于Objective-C类型推断）的框架。该代理管理运行时元数据提取、二进制检查和约束检查，而模型生成候选方法签名，这些签名被验证和优化以生成可编译的头文件。在MOTIF-Bench上，一个从公共框架构建并具有真实头文件的基准测试中，MOTIF将签名恢复率从15%提升到86%，在工具使用准确性和推断稳定性方面也有持续的提升。对私有框架的案例研究显示，重建的头文件可以编译、链接，并促进后续的安全研究和漏洞研究。通过将模糊二进制文件转换为可分析的接口，MOTIF建立了一个可扩展的基础，为系统性审计macOS内部结构提供了基础。
英文摘要：
Private macOS frameworks underpin critical services and daemons but remain undocumented and distributed only as stripped binaries, complicating security analysis. We present MOTIF, an agentic framework that integrates tool-augmented analysis with a finetuned large language model specialized for Objective-C type inference. The agent manages runtime metadata extraction, binary inspection, and constraint checking, while the model generates candidate method signatures that are validated and refined into compilable headers. On MOTIF-Bench, a benchmark built from public frameworks with groundtruth headers, MOTIF improves signature recovery from 15% to 86% compared to baseline static analysis tooling, with consistent gains in tool-use correctness and inference stability. Case studies on private frameworks show that reconstructed headers compile, link, and facilitate downstream security research and vulnerability studies. By transforming opaque binaries into analyzable interfaces, MOTIF establishes a scalable foundation for systematic auditing of macOS internals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
