Agents already have sandboxes to run code and browsers to read the web. Dr.Binary gives them a hosted workspace for compiled binaries, with nothing to install.
Copy this prompt into Claude Code, Codex, Cursor, or any agent that can add an MCP server. It connects, walks you through sign-in, and checks that everything works.
# Set up Dr.Binary You are an AI agent. Your user wants you to analyze compiled binaries (executables, libraries, firmware, APKs) with Dr.Binary, a hosted binary analysis service. Follow these steps in order, then tell them you are ready. ## 1. Connect the Dr.Binary MCP server Add it to your own MCP configuration, the way your tool adds MCP servers: - name: `drbinary` - transport: streamable HTTP - URL: `https://mcp.drbinary.ai/mcp` The server signs in with OAuth, so there is no API key. When you connect, a browser sign-in opens; ask the user to complete it (a free account works). If your tool needs a restart or a separate step to sign in, for example `/mcp` in Claude Code, tell the user exactly what to do. ## 2. Install the skill Install the `binary-analysis` skill from https://github.com/DeepBitsTechnology/codex-plugins/tree/main/plugins/drbinary-chat-plugin/skills/binary-analysis the way you normally load skills. Copy the whole folder. If you have no skills support, read its `SKILL.md` before each binary task instead. ## 3. Check it works Confirm these tools are available: `prepare_upload`, `inspect_binary`, `run_sandbox` and `dump_data`. Report what passed and what did not. ## How to use it - The server cannot see the user's files. To analyze a local file, call `prepare_upload`, then run the `curl` command it returns yourself, with the file's absolute path. - Start with `inspect_binary`, then go deeper with `run_sandbox` and `dump_data`. - Only upload files the user has asked you to analyze.
Shift the low-level work from the model to the engines and the model gets further on less. Two measurements, two angles.
Same model, with and without Dr.Binary
Plain shell
6/13
With Dr.Binary
11/13
Against frontier models
Dr.Binary + open-weight model
5 of 11
45%
Best published model
same 11 tasks
39%
Average of 26 models
same 11 tasks
13%
Less of the reasoning is delegated to the model, so the same problem costs less and often runs on a smaller model.
A model small enough to run on your own hardware becomes worth running, so sensitive binaries never leave your network.
Drive it yourself in the browser, or let the agents you already run drive it for you.
Platform teams · AI engineers
“Your agent can call every tool and still cannot finish an investigation.”
Reverse engineers · CTF · Bug hunters
“You know where to look. You do not want to rebuild the plumbing every time.”
SOC · MSSP · Incident response
“A suspicious sample on the desk and no reverse engineer free this week.”
The complete platform in a box, built on NVIDIA DGX, for defense, government, and critical-infrastructure teams whose binaries can never touch a third-party cloud. The same shift that makes a 27B model competitive in our benchmarks is what makes it worth running on your own hardware.
From $20,000
Desk-side and rack configurations
Samples, prompts, and findings stay inside your security boundary. No external model provider, no internet required.
The engines carry the low-level analysis, so a model sized to run on the box reaches conclusions it could not reach on its own.
Every analysis, sandboxes, custom agents, and MCP into your internal tools. Unlimited analyses and seats, no credits, no rate limits.
Free to start. Pay as your team grows.
Cloud plans from $0 to $599 a month, private-cloud and enterprise deployments, and AirGap from $20,000. Expert services if you would rather hand it off.
From the blog
July 9, 2026
We compare DeepDiff and Google BinDiff across 21 binary pairs to measure function matching under compiler changes, stripped static code, and code movement between symbols.
May 12, 2026
The most fundamental failure mode of LLM-powered binary analysis: models evaluate binaries by their most obvious features and stop there. This post walks through how Dr. Binary addresses it with an exhaustive analysis tool that processes every function before the LLM draws any conclusions.
October 8, 2025
Exploring how autonomous AI agents are revolutionizing binary code analysis through self-directed reasoning, collaborative multi-agent systems, and adaptive threat detection strategies that operate without human intervention.