Homing Pigeon

Works with Muse, Grok, ChatGPT, Claude, Gemini, Copilot, etc.

⚠️ USE AT YOUR OWN RISK. This tool hands full administrative control of the machine to whoever holds the shared secret. It reads email and executes what it finds — blindly, as a privileged user. Anyone who can produce a correctly-signed email owns the box: install software, read files, add users, pivot to your network. Only run this with a dedicated throwaway account on a machine you can afford to lose, and read PROTOCOL.md before you deploy it.

Your cloud SI is smart, but it can't touch your computer. Homing Pigeon fixes that: tell your SI to complete a task and it will do it on your machine. The results return so that your SI can iterate — run the tests, read the failure, fix, repeat. From your phone or anywhere.

Install the Homing Pigeon agent/runner on your system. It will prompt for login credentials to an email address, the default. The agent bares similarity to GitHub self-hosted runners.

Then try this prompt in your cloud SI, it should return in less than 30 seconds.

Send an email to YOUR_RUNNER_EMAIL
Use this protocol: https://github.com/Pigeon-Home/homing-pigeon/blob/main/PROTOCOL.md
Shared Secret: "YOUR_SECRET_STRING"
I have a Windows computer. Retrieve the system clock from it and display it here.

IETF Homing Pigeon Draft: https://github.com/Pigeon-Home/homing-pigeon/blob/main/docs/ietf/draft-homing-pigeon-00.md

What this is for

Homing Pigeon is a personal remote-development agent. The point is not that the SI becomes smarter. It is that the SI gets a way to delegate work to a computer it could not otherwise reach. The most powerful computer you have access to should not stop being useful the moment you walk away from your desk, and neither should your SI assistant.

Concrete uses:

• Remote test runs. Ask your SI to run the suite, read the failures, patch the code, rerun, and report back, all while you are away from the machine.
• Your hardware as a worker. Long builds, data crunching, batch jobs, on the workstation you own, without a remote-desktop session open.
• Scheduled-style automation. Maintenance scripts, reports, and repetitive technical tasks, dispatched from your phone.
• Cloud SI, local resources. Your files, your dev environment, your GPU, reachable by an assistant that lives in the cloud.
• Physical peripherals. Plug a sensor or servo board into a Raspberry Pi, point your SI at it, and let it write the control code and debug the hardware into motion. You do the soldering, it does the head-scratching. Let SI build a robot.

Send a job from your phone, let the big machine run the tests, crunch the data, or grind through the refactor, and read the results when they land in your inbox.

The SI systems are brilliant, but they are kept fenced off from our real machines. So instead we will point one at a disposable box, give it a signed channel, and let it do tasks from start to finish. The upside is an assistant that operates, not just advises. If the capability is real, there is very little downside to exercising it.

A new model claims to be good at something — robotics, automation, data work? Point it at a box and find out.

It belongs to the same family as GitHub self-hosted runners, VS Code Remote tunnels, and Tailscale — legitimate tools for administering your own hardware remotely. And like all of those, its power is the point, not an accident: whoever holds the shared secret has full control of the machine. That's exactly why this project states its threat model plainly instead of burying it. Keep the secret like a password, use a throwaway account, and only ever install the agent on machines you own and operate.

  SI (cloud)                        Your machine
  ------------                      ------------------
  send_job.py --sign--> job message -->  runner.py
       |                  |              |  poll IMAP
       |                  |              |  verify HMAC
       |                  |              v
       |                  |           run script
       |                  |              |
       |             result message <--+ sign
       v                  |
  verify result <----------+

Large inputs don't go through email: your SI uploads them to file hosting and the job message carries download URLs (with optional SHA-384).

Why email? Because it's the only transport with all five properties on day one: reachable from anywhere, outbound-only polling (no inbound ports — NAT-proof), at-least-once delivery, and zero infrastructure to operate. The protocol does not care about the pipe, but email is the only transport wired in today. Anything else is in FUTURE.md.

Runbooks, not prompts. The SI doesn't chat about the task — it ships a runbook: deterministic steps, each with a check that proves the step worked. The runner executes it and reports back what actually happened, and that feedback goes into the next runbook. Each cycle is a little smarter than the last, because the failures are data. If you've ever debugged something by running it, reading the output, and trying again with what you learned — that's the whole product. The SI is just the part that writes the next attempt.

The Oracle: answers, not output

The Oracle is a black box the SI can question but never inspect.

The SI asks "did the tests pass?" or "does the output contain secrets?" and gets back HMAC-signed attestations: proofs about the output that reveal nothing else. It's the zero-knowledge shape applied at the trust boundary — prove things about your data without showing your data. The raw output never leaves your disk.

  Cloud SI                                Your machine
  --------                                --------------------
  "Did the tests pass?" --signed------> +-------------+
                                        |  The Oracle |
  "Any secrets in there?" ------------> |             |
                                        | output stays|
   +---------------------+             | on your disk|
   | signed attestation   | <--signed-- |             |
   | { exit_zero: true,   |             | answers only|
   |   no_secrets: true } |             | what policy |
   +---------------------+             | allows      |
                                        +------------+
   The SI learns answers. Never the raw output.

Queries are declared upfront (no adaptive probing), each job gets a privacy budget bounding total leakage, and the disclosure ceiling is set by you in config — never by the SI.

Your machine runs two roles in one box:

+-----------------------------+-----------------------------+
| RUNNER (faces the SI)        | ORACLE (faces you)           |
+-----------------------------+-----------------------------+
| polls the mailbox            | reviews every job            |
| verifies HMAC + key policy   | before it runs               |
| judge: the policy gate      | fuses sensor alerts          |
| executes jobs, confined     | watches resources            |
| answers queries with        | pages you when a             |
| attestations -- never       | threshold trips              |
| raw output                  | never remediates             |
+-----------------------------+-----------------------------+

The runner does what the SI asks. The Oracle watches what happens and tells you about it.

Operating modes

Five knobs set the posture. They are independent — these are three coherent postures, not the only combinations.

  knob                  just run it  eyes open    lockdown
  ------------------    -----------  ---------    --------
  judge                 off          advisory     enforce
  sensors               off          on           on
  resources             on           on           on
  dri                   off          on           on
  max_result_view       full         attested     attested

just run it (the defaults). Runner: executes any signed job, answers queries with full output. Oracle: reviews every job anyway (notes ride in the result), logs resource pressure. Quiet — nothing pages, nothing blocks.

eyes open (recommended). Runner: executes, attaches the policy verdict to each result, answers queries with attestations only. Oracle: reviews, fuses sensor alerts, pages you when a trigger trips. Still takes no action itself.

lockdown. Runner: refuses anything the policy rejects; the SI gets attestations. Oracle: same as eyes-open, and every refusal is page-worthy. Nothing moves unless the policy allows it.

The Oracle ships two confinement capabilities:

• Local inference. Point it at a model running on your own box and the SI can ask questions in plain English — "why did the tests fail?" — answered locally, with only the answer crossing the boundary. The endpoint must be loopback (fail closed), and every answer is truncated, redacted, and secret-scanned before it's sent. The SI gets useful answers, and your raw data never leaves the machine.
• Execution confinement. Two different checks, both shipped. Set workspace_root and the Oracle snapshots file metadata outside that folder before and after the job, then attests confined_to_workspace. That one is a snapshot, not a sandbox. The walk stops at 50000 files. Separately, on Linux, set security.modes.confinement to enforce and the job runs inside bubblewrap (bwrap): /usr read-only, no network, and only the job workdir writable. It is off by default. If bwrap is missing, or the machine is not Linux, the job runs normally and the audit log records the fallback. See SECURITY.md.

Full spec in PROTOCOL.md (sections 9–12). Oracle fields are optional members of a "v": 1 message. They are not a separate message version.

Layout

• PROTOCOL.md — the full wire protocol (message formats, signatures, verification rules, security model).
• FUTURE.md — work that is not built. The only list.
• TRANSPORTS.md — email is the only transport in the runner. The rest of that file is design notes, not code.
• SAFEGUARDS.md — how to run this without getting burned: the non-negotiables, containment, the Oracle as a safeguard, and what to do when something goes wrong.
• runner/runner.py — the agent. Polls IMAP, verifies, executes, replies via SMTP. No dependencies beyond Python 3.9 stdlib.
• runner/install.py — installs it as a boot-start service: systemd (Linux), launchd (macOS), cron @reboot (FreeBSD), Scheduled Task (Windows).
• runner/config.example.json — copy to config.json and fill in.
• cloud/send_job.py — cloud-side helper: builds signed job messages, verifies result messages. (Your SI uses this logic when dispatching jobs.)
• tests/test_runner.py — end-to-end tests with fake IMAP/SMTP servers.
• RUNBOOK.md — step-by-step instructions (written for a 5-year-old): cloning, pushing, cutting a release, installing, sending a job.
• examples/hello-world.json — unsigned example job payload.

Setup

Setup lives in INSTALL.md: the three install paths (Docker, pip, release binary) and the full first-run walkthrough — dedicated mail account, app password, shared secret, first job.

How scripts run

Jobs carry a script (or script_url) plus an optional interpreter list, e.g. ["bash"] or ["pwsh", "-File"]. The runner writes the script to the job's work dir and runs <interpreter...> script <args...>. Defaults: PowerShell on Windows, sh elsewhere; override per-install with default_interpreter in config.json, per-job with interpreter in the job (send_job.py new --interpreter '["bash"]').

Sending a job (any SI)

The runner is SI-agnostic: anything that can send email to the runner's mailbox and sign the body can drive it — your SI of choice, a cron script, whatever. The SI needs the shared_secret string from your config.json — the random value the installer generated, not the mailbox password. Then:

python3 cloud/send_job.py new --kind ping --secret <the-string>
# prints: subject line, blank line, signed body -> send as email to the runner mailbox

python3 cloud/send_job.py new --kind exec --script-file job.sh \
  --interpreter '["bash"]' --file data.csv=https://... --timeout 300 \
  --secret <the-string>

Or paste the string to your SI in chat — it composes, signs, and sends the job message itself, and reads the result message when it arrives. Large files are uploaded to cloud storage first; the job message carries the URLs.

Verify a result body:

python3 cloud/send_job.py verify --secret <the-string> result.txt

Testing

Watch before you trust. The runner is fully inspectable — these flags exist so you can audit everything it would do before it does it:

python3 runner/runner.py --config config.json --once --dry-run --verbose
# verifies signatures, prints what each job WOULD do, executes nothing,
# sends nothing, changes nothing (mail stays unseen)

install.py accepts the same flags and bakes them into the installed service, so the agent on boot runs exactly what you tested: python3 runner/install.py --verbose --poll-interval 30.

python3 -m unittest discover -s tests -t .

The suite drives the real runner code against fake IMAP/SMTP servers: ping roundtrip, script execution, timeout kill, poison-job rejection, signature/tamper rejection, any-sender acceptance.

End-to-end without your SI:

1. python3 cloud/send_job.py new --kind ping --secret <the-string> → copy the subject + body.
2. From any email account, send it to the runner mailbox.
3. On the target machine: python3 runner.py --config config.json --once.
4. A result message with stdout: "pong" should arrive in the mailbox (or at the job's reply_to address, if you set one).

Security notes

• Honest threat model: docs/threat-model.md — what this is, what it isn't, and what "anyone holding the shared secret effectively owns the box" means for how you deploy it. Its companion docs/security-gaps.md covers what the defaults don't protect you from, and the order to turn things on.
• Industry map: docs/industry-map.md — five years of commercial detection (2021–2026) read honestly against this project, sorted by local footprint cost.
• The runner is a full-trust execution environment: a valid signed job message means arbitrary code as a privileged user. The app password sits in plaintext in config.json (your explicit choice: throwaway account, fresh box). Anyone holding it gets mailbox access. The HMAC shared secret is a separate value — anyone holding it can sign jobs. Don't use this setup with an account you care about.
• Every job is HMAC-SHA-384 signed; the signature is the sole authentication gate. Timestamp window and replay protection (seen-set) are enforced before anything runs; the sender address is logged but never trusted.
• Delivery is at-least-once: if a result email fails to send, the job stays unseen and will be re-executed next cycle. Make jobs idempotent or check the seen behavior accordingly.
• Size caps: max_job_bytes (default 4 MiB) bounds the raw mail before the signature is even checked; max_script_bytes (default 1 MiB) bounds the script after verification. Either rejects through the normal reject path; 0 means unlimited. A negative or non-integer value is a startup config error.
• Optional hardening controls (all off by default): see SECURITY.md. The Oracle is implemented: result policies, signed attestations, and a query budget, so a cloud SI learns answers about your output without ever seeing it.

The download version and the message field "v" are different numbers. The runner is 3.7.x and speaks "v": 1.

Not built

The list is FUTURE.md.

Status

The current release is in CHANGELOG.md. The download version is the number in pyproject.toml.

Still true from earlier releases. Any sender may submit a job, and the HMAC is the only gate. config.json hot-reloads poll interval, workers, timeouts, output limits, and max job age. A security mode change still needs a restart. Thunderbird-style Google sign-in is shipped. PowerShell jobs stage as script.ps1. Release binaries carry Sigstore build provenance. The suite is python3 -m unittest discover -s tests -t ..

Mirrors

• GitLab: https://gitlab.com/pigeon-home/homing-pigeon
• SourceForge: https://sourceforge.net/projects/homing-pigeon/
• Randall Munroe: xkcd 568 "You'll never find a programming language that frees you from the burden of clarifying your ideas." (Except with English + SI + a long context paragraph clarifying your ideas => the cycle begins again)