How To Control Access To REST APIs

hackerExposing your data or application through a REST API is a wonderful way to reach a wide audience.

The downside of a wide audience, however, is that it’s not just the good guys who come looking.

Securing REST APIs

Security consists of three factors:

  1. Confidentiality
  2. Integrity
  3. Availability

In terms of Microsoft’s STRIDE approach, the security compromises we want to avoid with each of these are Information Disclosure, Tampering, and Denial of Service. The remainder of this post will only focus on Confidentiality and Integrity.

In the context of an HTTP-based API, Information Disclosure is applicable for GET methods and any other methods that return information. Tampering is applicable for PUT, POST, and DELETE.

Threat Modeling REST APIs

A good way to think about security is by looking at all the data flows. That’s why threat modeling usually starts with a Data Flow Diagram (DFD). In the context of a REST API, a close approximation to the DFD is the state diagram. For proper access control, we need to secure all the transitions.

The traditional way to do that, is to specify restrictions at the level of URI and HTTP method. For instance, this is the approach that Spring Security takes. The problem with this approach, however, is that both the method and the URI are implementation choices.

link-relationURIs shouldn’t be known to anybody but the API designer/developer; the client will discover them through link relations.

Even the HTTP methods can be hidden until runtime with mature media types like Mason or Siren. This is great for decoupling the client and server, but now we have to specify our security constraints in terms of implementation details! This means only the developers can specify the access control policy.

That, of course, flies in the face of best security practices, where the access control policy is externalized from the code (so it can be reused across applications) and specified by a security officer rather than a developer. So how do we satisfy both requirements?

Authorizing REST APIs

I think the answer lies in the state diagram underlying the REST API. Remember, we want to authorize all transitions. Yes, a transition in an HTTP-based API is implemented using an HTTP method on a URI. But in REST, we shield the URI using a link relation. The link relation is very closely related to the type of action you want to perform.

The same link relation can be used from different states, so the link relation can’t be the whole answer. We also need the state, which is based on the representation returned by the REST server. This representation usually contains a set of properties and a set of links. We’ve got the links covered with the link relations, but we also need the properties.

PolicyIn XACML terms, the link relation indicates the action to be performed, while the properties correspond to resource attributes.

Add to that the subject attributes obtained through the authentication process, and you have all the ingredients for making an XACML request!

There are two places where such access control checks comes into play. The first is obviously when receiving a request.

You should also check permissions on any links you want to put in the response. The links that the requester is not allowed to follow, should be omitted from the response, so that the client can faithfully present the next choices to the user.

Using XACML For Authorizing REST APIs

I think the above shows that REST and XACML are a natural fit.

All the more reason to check out XACML if you haven’t already, especially XACML’s REST Profile and the forthcoming JSON Profile.

The Decorator Pattern

decoratingOne design pattern that I don’t see being used very often is Decorator.

I’m not sure why this pattern isn’t more popular, as it’s quite handy.

The Decorator pattern allows one to add functionality to an object in a controlled manner. This works at runtime, even with statically typed languages!

The decorator pattern is an alternative to subclassing. Subclassing adds behavior at compile time, and the change affects all instances of the original class; decorating can provide new behavior at run-time for individual objects.

The Decorator pattern is a good tool for adhering to the open/closed principle.

Some examples may show the value of this pattern.

Example 1: HTTP Authentication

Imagine an HTTP client, for example one that talks to a RESTful service.

Some parts of the service are publicly accessible, but some require the user to log in. The RESTful service responds with a 401 Unauthorized status code when the client tries to access a protected resource.

Changing the client to handle the 401 leads to duplication, since every call could potentially require authentication. So we should extract the authentication code into one place. Where would that place be, though?

Here’s where the Decorator pattern comes in:

public class AuthenticatingHttpClient
    implements HttpClient {

  private final HttpClient wrapped;

  public AuthenticatingHttpClient(HttpClient wrapped) {
    this.wrapped = wrapped;
  }

  @Override
  public Response execute(Request request) {
    Response response = wrapped.execute(request);
    if (response.getStatusCode() == 401) {
      authenticate();
      response = wrapped.execute(request);
    }
    return response;
  }

  protected void authenticate() {
    // ...
  }

}

A REST client now never has to worry about authentication, since the AuthenticatingHttpClient handles that.

Example 2: Caching Authorization Decisions

OK, so the user has logged in, and the REST server knows her identity. It may decide to allow access to a certain resource to one person, but not to another.

IOW, it may implement authorization, perhaps using XACML. In that case, a Policy Decision Point (PDP) is responsible for deciding on access requests.

Checking permissions it often expensive, especially when the permissions become more fine-grained and the access policies more complex. Since access policies usually don’t change very often, this is a perfect candidate for caching.

This is another instance where the Decorator pattern may come in handy:

public class CachingPdp implements Pdp {

  private final Pdp wrapped;

  public CachingPdp(Pdp wrapped) {
    this.wrapped = wrapped;
  }

  @Override
  public ResponseContext decide(
      RequestContext request) {
    ResponseContext response = getCached(request);
    if (response == null) {
      response = wrapped.decide(request);
      cache(request, response);
    }
    return response;
  }

  protected ResponseContext getCached(
      RequestContext request) {
    // ...
  }

  protected void cache(RequestContext request, 
      ResponseContext response) {
    // ...
  }

}

As you can see, the code is very similar to the first example, which is why we call this a pattern.

As you may have guessed from these two examples, the Decorator pattern is really useful for implementing cross-cutting concerns, like the security features of authentication, authorization, and auditing, but that’s certainly not the only place where it shines.

If you look carefully, I’m sure you’ll be able to spot many more opportunities for putting this pattern to work.

Data Classification In the Cloud

Whenever a bug report comes in, I subconsciously classify it according to how it impacts the customer’s ability to derive value from the product.

Many software development companies have policies that formalize such classifications, e.g. into critical, high, medium, and low priority.

One can take that very far, like the Common Weakness Scoring System (CWSS) for classifying security vulnerabilities.

Data classification

Classifications are useful, because they compress a vast set of possibilities into a small set of categories. This makes it easier to decide what to do.

Classification applied to data stored in computer systems is called data classification. There are different reasons for classifying data.

One is to determine appropriate access control policies. It is wasteful to protect all your information at the highest level, so you want to divide up your data into a small number of buckets and take measures that are appropriate for each bucket.

Another important use case of data classification is to drive compliance efforts. If you process health care data, for instance, you may have to comply with the Health Insurance Portability and Accountability Act (HIPAA). This data requires different controls to be put in place than credit card data that is covered by PCI DSS.

Data in the Cloud

Things get more interesting in the cloud.

As a cloud user, you are still subject to the same laws and regulations as before, but now you’ve given away part of the control to your cloud provider. This means you have to make sure that they implement the required controls.

If the regulations you must comply with come with assessments, then those must extend to the cloud provider. Many cloud providers will not allow you to come in and do such assessments yourself, but they may allow assessments from third parties, like TRUSTe for a Safe Harbor assessment.

As a cloud provider, you will want to implement as many controls as possible, to support the maximum number of laws and regulations that your customers must comply with.

Both parties benefit from clear contracts. Part of such a contract may be a Data Protection Agreement that lists the duties of both parties in classifying and properly protecting data to meet security requirements and regulations.

If you’re unsure how to do all of this right, then you may want to look for guidance from the Cloud Security Alliance (CSA).