🔍 Read the full analysis: Getting Started With AI-Assisted Software Reverse Engineering on ThorstenMeyerAI.com
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TL;DR
A ThorstenMeyerAI.com article published Oct. 19 describes how developers can use the open-source Reverse Engineer Anything (REA) project with AI coding agents to inspect existing software. It outlines focused investigations, setup and verification steps, and safeguards for checking agent conclusions; REA’s analyses are not proof on their own.
ThorstenMeyerAI.com published a guide on October 19 to using Reverse Engineer Anything (REA) with AI coding agents to investigate how existing software behaves. The report describes a workflow for asking narrow questions, checking that the agent actually uses REA, and testing its conclusions against concrete examples—a process that matters when developers use familiar programs as references for new software.
REA is an MIT-licensed open-source project hosted on GitHub, according to the article. It combines a local Model Context Protocol (MCP) server, a command-line tool and an agent skill that guides investigations. The project supports different analysis paths for targets including native programs, Electron and JavaScript applications, .NET and Android software, firmware, network captures, WebAssembly and SQLite databases. For websites, the guide says it can observe activity in a Chrome-family browser controlled by the user.
The article’s central recommendation is to investigate one specific behavior tied to a decision, rather than asking an agent to explain an entire program. For example, a developer might ask how a partial invoice payment changes the remaining balance and which account receives the posting. The guide recommends asking the agent to identify stored data, calculations and conditions that affect the result, while labeling findings as observed, inferred or unknown.
Setup uses Node.js 22.19 or newer, according to the guide. Users can run the project’s guided installer, connect an MCP server to a supported agent, and check prerequisites with REA’s doctor command. The article cautions that this check does not verify a live agent connection: users should also confirm that the agent can call an REA tool. A useful test is whether it can explain a complete example using specific inputs and outputs, rather than merely describing a program’s screens.
Infographic 01 · Reverse Engineer Anything
Five places to point REA first, and how to tell when the answer is wrong.
REA gives coding agents such as Claude Code and Codex tools to inspect programs, apps and browser sessions. Each investigation needs a finishable question and a check.
npx rea-agents@latest setupThe five investigations
What to ask, and the check that catches a wrong answer.
- 1
Ask a question you can finish
One behaviour, one decision it informs. Example: how a partial payment changes an invoice balance.
It’s wrong ifThe agent can't walk through one concrete example with real inputs and outputs. - 2
Get REA connected and confirmed
Run the guided setup, then doctor, then call binary_session inside the agent before any real target.
It’s wrong ifAnswers arrive without a single REA tool call: you're reading the model's guesses. - 3
Turn a business rule into a spec
Inputs, stored fields, formula, edge cases, plus test cases checked against the original's own output.
It’s wrong ifYour version and the original disagree on a test case, even by one cent. - 4
See what has to survive a copy
Find what an app writes to the clipboard and what it keeps locally, then design your own copy and paste.
It’s wrong ifThe explanation doesn't predict what lands in a plain text editor, an email and your app. - 5
Your own app, your equipment, an old program
Start with software you own or rely on. Ask what it sends, stores or calculates when you do one thing.
It’s wrong ifIt only works for the example you watched. Test with a new file or a read-only command first.
Sources: REA on GitHub (stars via GitHub API, 19 Oct 2026); REA installation guide; REA security policy.
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From Software Behavior to Working Specs
The approach is relevant to developers trying to recreate or replace a feature in software they already use. Screens and menus show what users can do, but not necessarily which records the program stores, how a calculation works, or what changes when an edge case occurs. Those details can determine whether a replacement behaves consistently in real situations.
The guide points to Ledger Reborn, a locally built accounting application, as an example. Its author said the app’s profit-and-loss statement, balance sheet and trial balance matched those of Intuit’s official QuickBooks sample company account by account, to the cent. The source describes an investigation of QuickBooks Desktop for Mac 2024 using REA 6.4.0 and Ghidra, focused on areas such as inventory calculations, reconciliation and cash-basis reporting. That is a reported case study, not independent verification that REA will produce the same results for other programs.
For readers, the practical takeaway is that an AI agent’s fluent explanation should not be treated as evidence by itself. A disciplined investigation can help turn observed behavior into a specification, while concrete test cases provide a way to check whether the explanation holds up.
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How REA Connects to Coding Agents
REA is designed to give coding agents access to analysis tools through MCP, a standard for connecting agents to external tools. The agent can request an analysis, while REA and its supported engines inspect the target. The project also offers a command-line interface for setup and direct analysis. The article identifies Claude Code, Codex, Cursor and Gemini CLI among the agents it can work with.
The source reports that REA version 6.7.0 was released October 18, one day before the guide appeared. It also says the project had about 325,000 GitHub stars and 82,000 forks on October 19, following a period on GitHub’s trending page in early October. Those are dated figures from the source, not a measure of accuracy or effectiveness.
The guide stresses two operational limits. Analysis runs locally, but findings are passed to the coding agent and may also be handled by the model provider under that provider’s data policy. REA is also not a sandbox: opening an unknown program exposes it to analysis tools running with the user’s permissions. The article advises treating unfamiliar binaries as untrusted files.
“REA supplied evidence and imperf”
— Thorsten Meyer’s article, describing the Ledger Reborn case study
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Limits of the Reported Results
The source provides a detailed account of one QuickBooks investigation, but the supplied material does not include the full Ledger Reborn case study or enough information to independently check its reported account-by-account results. The quotation about the project’s method is truncated, and no independent assessment of the comparison is included.
It is also unclear from the supplied article how consistently REA works across its supported target types, what accuracy users should expect, or how much investigation a particular program requires. Agent output can mix direct observations with inference, so readers should not treat an explanation as confirmed unless it is tied to repeatable evidence and tested examples.
The article says analysis runs locally, but it does not detail the data-handling terms of individual model providers. Users should check those policies before sending analysis results to an agent, especially when examining proprietary or sensitive software.
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Verify the Agent Before Investigating
The guide’s immediate next step for anyone trying REA is to complete setup, restart the coding agent and verify that the connection is live. Users can inspect a harmless program they own before opening a more complex target, then ask one bounded question and require a walk-through of a specific input and output.
For a deeper investigation, the article recommends comparing the agent’s proposed explanation against additional cases, including conditions that could change the result. The supplied material does not identify a forthcoming REA release or a scheduled follow-up to the Ledger Reborn case study, so the next developments are not specified.
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Key Questions
What is REA?
Reverse Engineer Anything is an open-source project that connects AI coding agents to software-analysis tools through MCP. It also includes a command-line tool and an agent skill for guiding investigations.
Does REA prove that an agent’s explanation is correct?
No. The guide treats REA as a way to gather evidence, not a guarantee of accuracy. It recommends checking that the agent used REA and can explain a complete example with concrete inputs and outputs.
What is needed to set it up?
The article says REA requires Node.js 22.19 or newer. Some kinds of native analysis also need a disassembler such as Ghidra, Hopper or IDA. Users should check the project’s current documentation for supported setup options.
Is it safe to analyze an unknown program?
The article warns that REA is not a sandbox and that analysis tools run with the user’s permissions. It advises treating unknown binaries as untrusted files and considering what analysis results may be shared with the agent and its model provider.
Source: ThorstenMeyerAI.com
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