Reverse Engineering with AI Agents: Introducing REI Skills and the REA Workbench

说明与用途

文章描述了使用 REI Skills 和 REA Workbench 进行可复现逆向工程的工作流程，重点关注证据和可验证性。

问题

AI agents generate plausible explanations but lack evidence tracking, repeatability, and explicit uncertainty needed for reverse engineering tasks.

结果

The workflow enables reproducible, evidence‑linked reverse engineering, exemplified by comparing two WebAssembly binaries and distinguishing observed changes from inferred behavior.

工作流

• Install REI Skills via npx rei-skills
• Add REA skills with npx rei-skills add rei-rea --<target>
• (Optional) Clone REA workbench repo and install Python dependencies
• Run rei-rea inspect <artifact> to generate evidence
• Run rei-rea diff <artifact1> <artifact2> to compare structures
• Use rei-rea doctor or MCP tools to verify evidence and plan further analysis

证据

AI coding agents can generate software surprisingly well. But what happens when the source code is missing?

来源

REI Skills is an open‑source registry and management layer for engineering workflows used by AI coding agents.

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REI REA (Reverse Engineering Workbench) integrates 12 specialized agent skills.

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The optional Python workbench provides the actual local analysis capabilities.

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The repository's recorded demonstration identifies two structural additions: New imported function: env.audit_log_frame and New exported function: validate_packet_header.

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