The January 2015 release of CloudHub features several key upgrades to our infrastructure. With this release, customers will be able to run more applications per vCore (10 per vCore – up from the current limit of 4), and will also be able to run more compute and memory intensive applications – which consume up to 4 vCores in one instance. We are also releasing a new security feature – data encryption for persistent queues, which will help customers with their security and compliance needs.
More Applications per vCore, and Support for Bigger Applications
Starting with the January 2015 release, customer applications running on CloudHub will have a bigger palette of worker sizes to choose from. Currently, a customer application has a choice of 3 worker sizes – Shared, Regular, or Double, representing 1/4th of a vCore, 1 vCore, and 2 vCore capacities respectively. Starting February 7, applications running on CloudHub will have 5 different worker sizes to choose from, with the compute and memory capacities described in the following table:
|New Worker Sizes
||500 MB Mem
||1 GB Mem
||1.5 GB Mem
||3.5 GB Mem
||7.5 GB Mem
Securing an API in Anypoint Platform is easy. In a previous post we showed how Anypoint Platform for APIs allows you to fully protect your API. We concluded then that the combination of HTTPS and OAuth 2.0 are a rule-of-thumb best practice for Web API security. In this post, we’ll take a deeper dive into the makeup of a security configuration in Anypoint Platform and explore in more detail the areas of Basic Authentication and OAuth2 Authorization in the context of Identity Management. We’ll also give you some pointers about when and how to use these two standards.
Central to authentication in Mule is the Security Manager. This is the bridge between standard mule configuration and Spring Security beans. In the example we build in this blog, we will use Spring Security to authenticate credentials against an LDAP server. We suggest you read the Spring Documentation on this topic if you want to delve further.
Trust no one! Most security issues comes from assuming that no bad person is going to tamper with your input data. We usually pay more attention to it when processing the most common inputs, such as an HTTP request or some argument that’s going into an SQL query. But we usually don’t pay much attention to other types of resources that are also vulnerable to malicious thinking – such as an XML file.
External Entities are an XML feature which allow you to embedded an external source into your document. For example, let’s suppose that your application responds to queries using an XML schema, which contains a disclaimer footer. Your legal department is prone to changing the wording on it so it probably makes sense to take it from an external file, so that your templates (which are part of your deployed source code) are not modified. Such templates could look like this:
Last month the massive Heartbleed security vulnerability was exposed. Three weeks later a security flaw in Microsoft Internet Explorer was revealed. It seems as though every few months there is news of a security breach or vulnerability. As more and more business is done online, in the cloud and through SaaS providers, how can you be sure the applications you and your business use are safe? Using the Heartbleed vulnerability as a case study, this article will examine what went wrong, as well as what you should expect from a SaaS provider, before, during and after a security event.
What is Heartbleed?
Heartbleed is the commonly recognized name of an exposure identified in a critical Internet security software package called OpenSSL, the most common transmission encryption software package used by Internet servers worldwide. This vulnerability allows an attacker to craft keepalive messages in such a way as to force a server disclose its short-term memory space. Since a server’s memory often contains personal, confidential information, such as user passwords or credit card numbers, the attacker could obtain that information. More severely, the server may also inadvertently disclose to the attacker its own private encryption key, which then allows the attacker to subsequently listen to all communications with that server, even without using the Heartbleed vulnerability. The nature of this attack is such that it leaves no traces, and is practically invisible to common detection mechanisms (although now that it has been exposed, signatures for it are becoming available for popular intrusion detection software).
The “Man-in-the-Middle” attack is such a well-recognized security risk, with established solutions and preventative measures in place that when I first heard about the recent ruckus around the Apple security flaw, I thought Apple’s trouble was more legal in natural, maybe some sort of royalties dispute between iTunes and the Michael Jackson estate. Only later did I found out what all the fuss was about “in the middle”, not “in the mirror”, and why I had to upgrade the iOS on my iPhone on a beautiful Saturday afternoon.
Regarding the specifics to Apple’s security flaw, there is already plenty of press coverage out there. For example, David Auerbach wrote a great analysis over at Slate.com.
In this post, I’d like to illustrate how automated unit testing with appropriate code coverage could have detected that particular kind of error, the one caused by grammatically correct code that inadvertently invalidated the whole logic of the program. We will build the unit tests using the MUnit module, an open source Mule testing framework that significantly streamline and simplify the process of writing unit tests.
It sounds like the title for a fantasy movie, but Google, OAuth and the “confused deputy” is a very common issue. Wikipedia defines a confused deputy as “a computer program that is innocently fooled by some other party into misusing its authority. It is a specific type of privilege escalation” (complete article here).
The Wikipedia article shares an example of a compiler exposed as a paid service. This compiler receives an input source code file and the path where the compiled binary is to be stored. This compiler also keeps a file called BILLING where billing information is updated each time a compilation is requested. If a user were to request a compilation setting the output path to “BILLING”, then the file would be overwritten and the billing information lost. In this case, the compiler is a “confused deputy” because although the client doesn’t have access to the file, it’s tricked the compiler (who does have access) into altering the file.
You may have already heard that on December 31st, 2013, Snapchat was hacked and 4.6MM records were subsequently compromised. According to the official blog, “an attacker released a database of partially redacted phone numbers and usernames.” It turns out the hacker(s) had exploited the “Find Friends” API to try to return the username of automatically generated phone number combinations.
The dreaded user table. Think about it: whenever you start working on a new end-user application, you’ll have to create a table to store emails, user information and passwords. And then you’ll need to add support for the password reset workflow. And so on and so forth. The wheel gets re-invented time and again. Of course, you may go sophisticated and decide to manage users in LDAP or even – gasp – ActiveDirectory. Now you would have a whole different range of problems to deal with, starting with interacting with this external directory in a graceful manner.
Enter Stormpath, the SaaS API whose sole mission is to make authentication and user management awesome and developer friendly! And thanks a new connector for Mule (available here), you can now benefit from Stormpath’s extensive features, which include all of the aforementioned ones, and many more.
In this post, we will look at a Mule application that integrates with the Stormpath API via this new connector. This application exposes a web user interface that uses AJAX to interact with the Mule application. This application allows a user to create an account, log-in and trigger the password forgotten procedure. Enough ado, let’s start digging!
On my previous 3-part blog, I showed how Mule ESB can be used to service-enable and orchestrate traditional on-premise technologies like an Oracle database and IBM Websphere MQ. Using Mule ESB, we created a service that accessed employee information from an Oracle database table and transmitted this to IBM WebSphere MQ. An observant customer I was showing this to noticed a security flaw with how sensitive employee information was being transmitted in plain text and also asked how the employee record can be sent to SalesForce.com. This blog will show how these can be easily addressed using MuleSoft’s AnyPoint Platform. We’ll make use of the PGP encryption features from AnyPoint Enterprise Security to encrypt the data before sending it to WebSphere MQ. Then, we’ll create another message flow to retrieve this message, decrypt it and send it to SalesForce.com using the AnyPoint Connector for SalesForce.com.
Security is an ever-present concern for IT. It can be a rather daunting area when one considers all of the different possible dangers and the large variety of solutions to address them. But, the aim of Enterprise Security really just boils down to establishing and maintaining various levels of access control. Mule itself has always facilitated secure message processing both at the level of the transport, the service layer and of the message . Mule configurations can include all that Spring Security has to offer giving, for example, easy access to an LDAP server for authentication and authorisation. On top of that Mule Applications can apply WS-Security thus facilitating, for example, the validation of incoming SAML messages. But in this post, rather than delve into all the details of the very extensive security feature set , I would rather approach the subject by considering the primary concerns that drive the need for security in a Service Oriented Architecture, how the industry as a whole has addressed those concerns, the consequent emergence of popular technologies based on this Industrial best practice and finally, the implementation of these technologies in Mule.