When AI Meets Reverse Engineering: What I Found Walking Through the REA Hands-On

There's an open-source project that's been making the rounds in developer communities lately. It lets an AI agent look directly inside a program that has no source code, explain how its features actually work with evidence, and then rebuild those features in a fresh project. The tool is called REA — Reverse Engineer Anything.

According to the public coverage, this project exploded in attention right out of the gate. One video reported that GitHub stars went from around 40 to over 57,000 in eight days, and various outlets later cited even bigger numbers. The figures shift depending on when you look, so for now it's enough to remember it as a project that grew very fast in a very short time.

What matters isn't the star count. It's that reverse engineering — a discipline that has long depended on a specialist's fingertips — has now moved inside the AI agent's own workflow.

1. What exactly is REA?

The idea and how it works. REA is an MCP (Model Context Protocol) server and a CLI tool. MCP is the standard way coding agents connect to external tools. Once you install REA, agents like Claude Code, Codex, Cursor, and Gemini CLI can call reverse-engineering tools directly.

The key word is "connection," not "judgment." REA didn't invent any new decompilation technique. It's a translation layer that sits between existing analysis engines — Ghidra (released by the NSA), the commercial tools Hopper and IDA Pro, and Chrome DevTools for web and Electron apps — and the agent. The agent doesn't need to know how to use any of those tools; it just asks REA to analyze something, and REA routes the request to the right engine.

Local analysis and security. Decompilation and analysis happen on the user's local machine. The results — pseudocode, evidence, and open questions — are then handed to the agent, and whatever happens next follows the policies of the model provider the agent is using. In short: analysis runs locally, interpretation happens in the cloud model.

What it supports. Native binaries (compiled C/C++), Electron apps, JavaScript, .NET assemblies, websites, captured network traffic, Android APKs, and firmware. Native analysis requires Ghidra, Hopper, or IDA to be installed. JavaScript static analysis works without a separate engine.

What it returns. For native code, you get pseudocode and assembly. For JavaScript and Electron, you get modules and their relationships. The important thing is that the output isn't finished source code — it's an interpretation backed by evidence. The agent writes its own code from that interpretation, tests it against the original, then feeds the result back into another round of analysis.

Installation is simple. With Node.js and npm available, one command — npx rea-agents setup — registers the agent and installs the workflow guidance. Existing settings are backed up, and you get a chance to review and approve what changes.

2. The hands-on sessions, step by step

What follows is a walkthrough of the public demos.

① Analyzing an Electron notes app. REA maps out the IPC (inter-process communication) structure of a notes app with no source code. It traces the preload API that bridges the renderer and main processes (calls like notes.copy and notes.search) and pinpoints which line of source defines each handler. It goes further than that, flagging things like "the sender-validation code appears to be missing" — showing the limits of static analysis with evidence to back it up. This is where reverse engineering can serve as a starting point for security review.

② Decompiling a C program and tracing compiler optimization. The target is a simple C executable that clips input to a range (0 to 639), does some math, and returns a result. The interesting part is how REA uses pseudocode analysis to uncover why a division operation in the source became a multiply-and-bit-shift during compilation. Division is relatively slow on CPUs, so compilers sometimes rewrite it as a constant multiply combined with shifts. Compiler optimization is the kind of thing people struggle to grasp intuitively — and REA surfaces those traces.

③ Verifying the rebuild against the original — in 42 seconds. The most striking scene is the verification. Using nothing but the pseudocode, the agent rewrote the code and then fed inputs from -50 to 700 into both the new build and the original executable. All 751 test cases matched, with zero mismatches. This moves beyond "the code looks plausible" to "the behavior is demonstrably identical," confirmed by the numbers. The demo then hands the entire pipeline — analysis, rebuild, test-script authoring, verification — to a Claude Code agent and shows it finishing in 42 seconds.

What's worth noting isn't the 42 seconds but the procedure. REA doesn't stop at a "plausible answer." It ships verifiable evidence and tests alongside it. In the end, this tool's credibility rests not on how fast it builds, but on how honestly it verifies.

3. Real-world examples and limitations

Examples. One outlet's hands-on review reported faithfully recreating a surprising amount of macOS Preview: zoom, annotations, drawing, rotation, and save. Another demo built a program with hotkeys, notes, and local transcription by studying a voice-to-text app.

Limitations. REA is not a magic wand.

• In the published comparisons, a general coding agent produced similar results. In some tests, REA actually used about 2.5 times the tokens. The smaller the program, the less of an edge REA provides.
• There's a difficulty gap. Recreating a specific feature of a small program and cloning an entire large, obfuscated commercial product are entirely different challenges. For big binaries and heavily obfuscated targets, verification evidence is still thin.
• The model's safety rails matter. One user reported that for aggressive targets (router firmware, for example), some leading models refused to work on safety-policy grounds, while models with looser policies proceeded. The results depend heavily on which model you pair with REA — not on REA alone.

4. Takeaways and practical implications

Productivity. When you need to understand how a legacy program works or reconstruct code whose source has been lost, a tool like REA can cut the analysis effort dramatically. But remember: the benefit grows with the size of the target and with low-level analysis that humans are poorly equipped to do.

This isn't fundamentally new technology. REA is an interface that connects agents to analysis engines that are decades old (Ghidra was released in 2019, Hopper dates to 2011, IDA to the 1990s). What's new isn't the decompilation — it's that the agent can autonomously handle the loop of interpreting the result, verifying it, and implementing it.

Ethics and legal responsibility. The demos repeat this point: restrict your targets to software you built yourself, or to programs where your license and terms of service clearly permit analysis. Cloning a competitor's UI or features carries intellectual-property and legal risk, whether or not an AI wrote the code. A tool that lowers the bar for entry doesn't lower the bar for responsibility.

Check the data policy. Analysis runs locally, but the results may be transmitted to whatever model provider the agent uses. If you're working with sensitive corporate code, verify that provider's data policy first.

Verification is the human's job. When you hear "an AI rebuilt this program," look at what was actually reproduced and how it was verified. The presence of evidence — something like 751 out of 751 matching test cases — is what matters.

Wrapping up

REA has moved reverse engineering from "a specialist-only craft" to "a workflow where agents participate." The claim that software development is over remains an overstatement. Understanding what a user actually needs, handling edge cases, and securing the result — AI doesn't take those off your plate.

But one thing has genuinely changed. Now, the moment you ask "how does this feature actually work?", you can have a helper that brings you the answer with evidence and then rewrites it in your own code. Tools lower the bar. Responsibility still lands on the person holding the tool.