@realfxw: AI is completely reshaping the game rules of low-level reverse engineering. The recently open-sourced REA (Reverse Engi…

AI is completely reshaping the game rules of low-level reverse engineering. The recently open-sourced REA (Reverse Engineer Anything) showcases the hardcore capabilities of AI Agents in binary analysis and code reproduction. In the past, analyzing a core function of an App often required senior reverse engineers to jump back and forth between decompilation tools, disassembled code, and debuggers. But REA encapsulates the reverse engineering toolchain directly into standard plug-ins for AI Agents via MCP (Model Context Protocol) and CLI. Its core gameplay mainly focuses on three things: Deep native binary decompilation: No longer limited to surface-level interface packet capture or frontend code parsing, but directly disassembling Native binary files and application logic. Dynamic execution tracking and evidence forensics: Supports tracing runtime behavior and call chains, allowing the Agent to base its functional implementations on execution facts rather than fabricating them out of thin air. Precise replication across tech stacks: After analyzing the logic loop, it guides the Agent to directly transplant the extracted core logic into your current tech stack. Community discussions have also quickly pointed out the real-world challenges and deep reflections of this paradigm: Symbol stripping and obfuscation countermeasures: Simple decompilation is far from enough; encountering low-level native code with removed symbol tables (stripped binaries) or control flow obfuscation is the true test of the Agent's semantic inference capabilities. Clean-room design and copyright boundaries: From decompilation analysis to code reproduction, avoiding code plagiarism controversies and establishing compliant isolation and reproduction mechanisms is crucial. Black-box comparative verification: Placing some test inputs outside the Agent's field of view and strictly comparing the input-output consistency between the original app and the reproduced function using a test set is the best approach to measure the reliability of generated code. Reverse engineering is gradually evolving from the "black magic" of a few security researchers into an engineering infrastructure that can be automated by Agents. How do you view the impact of AI on low-level software engineering and reverse analysis? Project address: