The article dives into how large language models (LLMs) enhance reverse engineering (RE) tasks, specifically in tracing a use-after-free (UAF) vulnerability in Windows' Common Log File System (CLFS). By leveraging LLMs alongside tools like pyghidra-mcp, the author demonstrates a more efficient analysis process. The focus is on how LLMs can help streamline complex investigations, turning vague starting points into actionable insights. Understanding CLFS is key. It acts as a kernel-level logging system in Windows, allowing user-mode applications to interact with it through specific APIs. The vulnerability in question, CVE-2025-29824, arises from a race condition that occurs when a log file handle closes while another operation is still in progress. This oversight can lead to critical security risks. The author emphasizes using patch diffs to uncover code changes and understand the underlying reasons for these modifications, which is where LLMs come into play. The article outlines the technical setup needed for analysis, including using local LLMs for privacy and pyghidra-mcp for multi-binary analysis. It details the steps to import the vulnerable CLFS binary and how to interact with the LLMs to gain insights on the vulnerability. The author illustrates that the LLM can generate accurate summaries and insights based on the patch diff, reinforcing its role as a tool for maintaining momentum in reverse engineering work.