.NET Tips and Tricks

Blog archive

Mocking an Authenticated User in Blazor/ASP.NET Core

I've done a couple of recent columns about securing Blazor Components and using claims-based policies declaratively in ASP.NET Core generally. While working with security, I'm always interested in doing end-to-end testing: Starting up the application and seeing what happens when I try to navigate to a page.

However, while that matters to me, I'm less interested in setting up users with a variety of different security configurations (so many names! so many passwords!). Inevitably while thinking I'm testing one authorization scenario, I pick a user that actually represents a different scenario.

So I created a MockAuthenticatedUser class that, once added to my application's middleware, creates an authenticated user for my application. I find it easier to configure my mock user's authorization claims in code before running a test than it is to maintain (and remember) a variety of users.

If you think you might find it useful, you can add it to your processing pipeline with code like this in your Startup class' ConfigureServices method:

services.AddAuthentication("BasicAuthentication")
                .AddScheme<AuthenticationSchemeOptions, 
                              MockAuthenticatedUser>("BasicAuthentication", null);

To use this class, you'll also need this line in your Startup class' Configure method:

app.UseAuthentication();

I should be clear that I've only used this to test Controllers so it might behave differently with Razor Pages.

Here's the code for my MockAuthenticatedUser class that configures a user with a name, an Id, a role, and some random claims:

using System.Security.Claims;
using System.Text.Encodings.Web;
using System.Threading.Tasks;
using Microsoft.AspNetCore.Authentication;
using Microsoft.Extensions.Logging;
using Microsoft.Extensions.Options;

namespace SampleBlazor.Models
{
  public class MockAuthenticatedUser : AuthenticationHandler<AuthenticationSchemeOptions>
  {
    const string userId = "phv";
    const string userName = "Jean Irvine";
    const string userRole = "ProductManager";

    public MockAuthenticatedUser(
      IOptionsMonitor<AuthenticationSchemeOptions> options,
      ILoggerFactory logger,
      UrlEncoder encoder,
      ISystemClock clock)
      : base(options, logger, encoder, clock){ }

    protected override async Task<AuthenticateResult> HandleAuthenticateAsync()
    {
      var claims = new[] 
        {
          new Claim(ClaimTypes.NameIdentifier, userId),
          new Claim(ClaimTypes.Name, userName),
          new Claim(ClaimTypes.Role, userRole),
          new Claim(ClaimTypes.Email, "[email protected]"),
        };
        var identity = new ClaimsIdentity(claims, Scheme.Name);
        var principal = new ClaimsPrincipal(identity);
        var ticket = new AuthenticationTicket(principal, Scheme.Name);

        return await Task.FromResult(AuthenticateResult.Success(ticket));
    }
  }
}

Posted by Peter Vogel on 11/14/2019


comments powered by Disqus

Featured

  • Microsoft Ships Stable Versions of OpenAI Libraries for .NET and Azure

    Further leveraging the relationship that vaulted Microsoft and OpenAI into leadership positions in the AI era, Microsoft this week announced stable versions of two new OpenAI libraries.

  • Microsoft Further Embraces OpenAPI Spec (formerly Swagger)

    Microsoft has long embraced the OpenAPI Specification (formerly known as Swagger) for describing APIs, and it's now taking that support to the next level with a new online resource.

  • Get Good at DevOps: Feature Flag Deployments with ASP.NET WebAPI

    They provide developers with the ability to toggle features on and off without having to redeploy code, making it easier to manage risk, test features in production, and facilitate smoother releases.

  • Implementing k-NN Classification Using C#

    Dr. James McCaffrey of Microsoft Research presents a full demo of k-nearest neighbors classification on mixed numeric and categorical data. Compared to other classification techniques, k-NN is easy to implement, supports numeric and categorical predictor variables, and is highly interpretable.

Subscribe on YouTube