Security Model

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

Code Provenance

Function 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.

We offer access to the generated native code to enterprise and hardware partners. Reach out to us at sales@fxn.ai.

Code Signing

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

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, Function client SDKs for Android, Swift, React Native, and Unity allow you to embed predictors into the app bundle at build time:

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

build.gradle.kts
import ai.fxn.fxn.gradle.FunctionEmbed

plugins {
    id("com.android.application")
    id("org.jetbrains.kotlin.android")
    id("ai.fxn.fxn-gradle") version "0.0.2"
}

android {
    ...
}

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

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

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

AppBehaviour.cs
using UnityEngine;
using Function;

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

    ...
}

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

The Function.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 Function client:

AppBehaviour.cs
using UnityEngine;
using Function;

public class AppBehaviour : MonoBehaviour {

    [Function.Embed("@apple/openelm")]
    private static Function fxn => new Function(
        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 Function API to retrieve a predictor; and to report telemetry data. Below is the data that the Function SDK transmits from user devices to the Function API:

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