openbin-ai/binny

An open source agent harness for reverse engineering. Plug in any model, point it at a binary, and it works through the code with real tools and hands you a report to review.

BINNY is the harness, not the model. It brings the tools (decompiled functions, strings, imports, data, a sealed sandbox, local Ghidra), the playbooks, the triage, investigate, draft loop, the budgets, the checkpoints and the report format. The model is whatever you plug in: Claude, GPT, DeepSeek, Qwen, Kimi, Gemini, Bedrock, or a local model through Ollama or LM Studio. BINNY opens one of your OpenBin or OpenAPK projects, or decompiles a file on your disk, and runs that loop against the decompiled code. Every step is a tool call you can watch: it lists functions, reads decompilations, pulls strings, imports and data, runs scripts in a sealed sandbox, and records findings as it goes. The draft lands on the project report for you to check before anything is published.

It runs on your own model key, or on a local model. Nothing about your sample goes to us unless you put it in a project.

macOS and Linux:

curl -fsSL https://openbin.ai/install-binny.sh | sh

Windows (PowerShell):

irm https://openbin.ai/install-binny.ps1 | iex

Then:

openbin login   # once; BINNY reuses the openbin CLI's sign-in
binny           # pick a project, set a key, go

Prebuilt binaries for every platform are on the releases page. Docker is needed only for /decompile (local Ghidra) and the sandbox tools.

Investigate. /investigate starts the full loop. Triage classifies the sample against the STIX 2.1 malware vocabulary, picks the matching playbooks, then investigates function by function until the budget or the evidence runs out, and writes the draft. /mode malware and /mode vuln switch the lens between a malware analysis report and a vulnerability research report.

Three ways to run it. /autonomy autonomous runs to a finished draft with no stops. supervised pauses at phase boundaries and whenever BINNY is unsure, and asks you. collaborative adds a short interview before the write-up: audience, depth, framing.

Ask. /ask skips the pipeline. Pick up a project at its current state and question it: verify a claim in an existing report, inspect one function, or build a defensive proof of concept such as a config decryptor. An existing report is treated as claims to check, not facts.

Tools, not vibes. Read tools work on the project's analysis: metadata, function list, decompiled function, strings, imports, exports, data symbols, raw data reads, and for Android projects file listing and file reads. /sample <path> attaches the raw binary and unlocks extraction. The sandbox run tool executes scripts in a Docker container with no network, a read-only root, a read-only sample mount, dropped capabilities and CPU, memory and pid limits. /work is a persistent workspace that survives between sessions.

Local decompile. /decompile <path> [processor] runs Ghidra on your machine and keeps the result on disk. Nothing is uploaded. Raw firmware takes a Ghidra processor ID such as ARM:LE:32:v7. Multi-sample projects (dropper plus payloads, firmware revisions) are handled with /samples and /switch.

Resume. Sessions auto-save after every step. Reopen a project marked resumable and BINNY continues where it stopped.

Report. /draft shows the draft, /findings the recorded findings, IOCs, classification and coverage. /save pushes the draft to the project report as a draft for you to review.

Bring your own key. Anthropic, OpenAI, DeepSeek, Qwen, Kimi, Gemini (OpenAI-compatible) and Bedrock are preconfigured. /key stores a key locally, /model switches, /models lists what is configured. /endpoint <url> points a provider at another host, for example a gateway that fronts the Claude Messages API on your own domain. Config lives in ~/.config/binny/config.json.

Ollama and LM Studio are preconfigured and need no key. Start the server, pick ollama or lmstudio in setup (or /model ollama), and BINNY lists the models you have pulled. llama.cpp (llama-server) and vLLM work the same way: /endpoint ollama http://localhost:8080/v1.

Two things matter for a local model:

• It must support tool calling. qwen3, gpt-oss, llama3.1 and newer, mistral-nemo and devstral do; most small instruct models do not.
• The context window must be big enough. Ollama defaults to 4k, which is too small for decompiled code; run it with OLLAMA_CONTEXT_LENGTH=32768 or set num_ctx in a Modelfile. BINNY compacts history earlier for local providers (contextTokens in config.json) so the tool list stays in the window.

Type / in the prompt for the palette, or /help for the full list.

Go 1.22 or newer for a release build; the module itself compiles on Go 1.18.

go build -o binny .
go test ./...

Releases are cut by pushing a v* tag. The workflow cross-compiles for Linux, macOS and Windows, publishes the release here, and mirrors it to openbin-ai/binny-releases for the installers.

Pull requests are welcome. Every contributor signs the CLA once; it lets the project stay AGPL while OpenBin can offer the same code under a commercial licence to teams that cannot use AGPL.

AGPL-3.0. The platform BINNY talks to is openbin-ai/platform, under the same licence.