Skip to main content

Simplest Authentication in Lift

Lift has been an interesting experience so far, particularly since I'm learning Scala at the same time. Lift comes with quite a few built-in mechanisms to handle various features, such as authentication, authorization and role-based access control.

A lot of the documentation utilizes these built-ins to good effect, but because the core mechanisms are complete skipped, you have no idea where to start if you have to roll your own authentication. A suggestion for Lift documentation: cover the basic introduction first, then show how Lift builds on that foundation.

I present here the simplest possible authentication scheme for Lift, inspired by this page on the liftweb wiki:

object isLoggedIn extends SessionVar[Boolean](false)
...

// in Boot.scala
LiftRules.loggedInTest = Full(() => isLoggedIn.get)

That last line only needs to return a boolean. If you wish to include this with Lift's white-listed menu system, you merely need to add this sort of test:

val auth = If(() => !Authentication.user.isEmpty,
              () => RedirectResponse("/index"))
val entries = 
    Menu(Loc("Login", "index" :: Nil, "Login", Hidden)) :: 
    Menu(Loc("Some Page", "some-page" :: Nil, "Some-Page", auth)) ::
    Nil
SiteMap(entries:_*)

Any request other than /index that is not authenticated, ie. isLoggedIn.get returns false, will redirect to /index for login.

One caveat: since the authenticated flag session-level data, you are vulnerable to CSRF attacks unless you utilize Lift's built-in CSRF protection, where input names are assigned GUIDs. This is the default, but since it is easy to circumvent this to support simple query forms and the like, it's worth mentioning.

Comments

Popular posts from this blog

async.h - asynchronous, stackless subroutines in C

The async/await idiom is becoming increasingly popular. The first widely used language to include it was C#, and it has now spread into JavaScript and Rust. Now C/C++ programmers don't have to feel left out, because async.h is a header-only library that brings async/await to C! Features: It's 100% portable C. It requires very little state (2 bytes). It's not dependent on an OS. It's a bit simpler to understand than protothreads because the async state is caller-saved rather than callee-saved. #include "async.h" struct async pt; struct timer timer; async example(struct async *pt) { async_begin(pt); while(1) { if(initiate_io()) { timer_start(&timer); await(io_completed() || timer_expired(&timer)); read_data(); } } async_end; } This library is basically a modified version of the idioms found in the Protothreads library by Adam Dunkels, so it's not truly ground bre...

Easy Automatic Differentiation in C#

I've recently been researching optimization and automatic differentiation (AD) , and decided to take a crack at distilling its essence in C#. Note that automatic differentiation (AD) is different than numerical differentiation . Math.NET already provides excellent support for numerical differentiation . C# doesn't seem to have many options for automatic differentiation, consisting mainly of an F# library with an interop layer, or paid libraries . Neither of these are suitable for learning how AD works. So here's a simple C# implementation of AD that relies on only two things: C#'s operator overloading, and arrays to represent the derivatives, which I think makes it pretty easy to understand. It's not particularly efficient, but it's simple! See the "Optimizations" section at the end if you want a very efficient specialization of this technique. What is Automatic Differentiation? Simply put, automatic differentiation is a technique for calcu...

Easy Reverse Mode Automatic Differentiation in C#

Continuing from my last post on implementing forward-mode automatic differentiation (AD) using C# operator overloading , this is just a quick follow-up showing how easy reverse mode is to achieve, and why it's important. Why Reverse Mode Automatic Differentiation? As explained in the last post, the vector representation of forward-mode AD can compute the derivatives of all parameter simultaneously, but it does so with considerable space cost: each operation creates a vector computing the derivative of each parameter. So N parameters with M operations would allocation O(N*M) space. It turns out, this is unnecessary! Reverse mode AD allocates only O(N+M) space to compute the derivatives of N parameters across M operations. In general, forward mode AD is best suited to differentiating functions of type: R → R N That is, functions of 1 parameter that compute multiple outputs. Reverse mode AD is suited to the dual scenario: R N → R That is, functions of many parameters t...