Security Model

Muna works by sending compiled binaries to end users’ devices. As such, Muna is carefully designed to minimize any attack surface that exists in downloading and executing software binaries.

Code Provenance

Muna works by lowering Python code to native code that is then compiled. This process involves reimplementing Python operations natively. These native implementations are written and maintained by us, and are rigorously tested to ensure correctness and memory safety. This means that regardless of what the original Python code does, the resulting compiled binary will only ever contain code, written, reviewed, and tested by us.

Prohibited Python APIs

Code Signing

When we compile native binaries, we perform code signing for platforms that support it:

Android

iOS

Linux

macOS

visionOS

Web

Windows

Code Sandboxing

On Android, iOS, macOS (App Store), and visionOS, there are strict sandboxing restrictions that prohibit downloading and executing code at runtime. As a result, Muna client SDKs for Android, Swift, React Native, and Unity Engine allow you to embed predictors into the app bundle at build time:

Muna for Android

Use the ai.muna.muna-gradle Gradle plugin in your build.gradle or build.gradle.kts file like so:

build.gradle.kts

import ai.muna.muna.gradle.MunaEmbed

plugins {
    id("com.android.application")
    id("org.jetbrains.kotlin.android")
    id("ai.muna.muna-gradle") version "0.0.5"
}

android {
    ...
}

muna {
    embeds.addAll(
        MunaEmbed(tag = "@fxn/greeting")
    )
}

Make sure to add a MunaEmbed entry for every predictor your app uses.

Muna for React Native

Muna for Swift

Muna for Unity

Embed predictors by adding the Muna.EmbedAttribute attribute to any class or struct in your project code:

AppBehaviour.cs

using UnityEngine;
using Muna;

[Muna.Embed("@fxn/greeting")]
public class AppBehaviour : MonoBehaviour {

    ...
}

When building your Unity app, the Muna SDK will fetch and embed predictors using the Muna access key in your project settings.

The Muna.EmbedAttribute attribute can accept multiple predictor tags.

If you use a custom proxy URL with custom authentication, you can instead apply the attribute to a static property that returns your authenticated Muna client:

AppBehaviour.cs

using UnityEngine;
using Muna;

public class AppBehaviour : MonoBehaviour {

    [Muna.Embed("@apple/openelm")]
    private static Muna muna => new Muna(
        url: "https://apple.com/api",
        accessKey: "tim apple"
    );
}

With predictor embedding, all prediction code will be present for code review and signing when the application is archived for distribution (e.g. on the App Store or Play Store).

Data Collection

At runtime, end user devices will make web requests to the Muna API to retrieve a predictor; and to report telemetry data. Below is the data that the Muna SDK transmits from user devices to the Muna API:

Processor Identifier

Configuration Identifier

Prediction Telemetry

Get Started

Making Predictions

Creating Predictors

Insiders

Code Provenance

Code Signing

Code Sandboxing

Data Collection

Get Started

Making Predictions

Creating Predictors

Insiders

​Code Provenance

​Code Signing

​Code Sandboxing

​Data Collection

Code Provenance

Code Signing

Code Sandboxing

Data Collection