David S. Read

SQL Injection is commonly given as a  root cause when news sites report about stolen data. Here are a few recent headlines for articles describing data loss related to SQL injection: Hackers steal customer data by accessing supermarket database¹, Hacker swipes details of 4m Pirate Bay users², and Mass Web Attack Hits Wall Street Journal, Jerusalem Post³. I understand that SQL injection is prevalent; I just don’t understand why developers continue to write code that offers this avenue to attackers.

From my point of view SQL injection is very well understood and has been for many years. There is no excuse for a programmer to create code that allows for such an attack to succeed. For me this issue falls squarely on the shoulders of people writing applications. If you do not understand the mechanics of SQL injection and don’t know how to effectively prevent it then you shouldn’t be writing software.

The mechanics of SQL injection are very simple. If input from outside an application is incorporated into a SQL statement as literal text, a potential SQL injection vulnerability is created. Specifically, if a parameter value is retrieved from user input and appended into a SQL statement which is then passed on to the RDBMS, the parameter’s value can be set by an attacker to alter the meaning of the original SQL statement.

Note that this attack is not difficult to engineer, complicated to execute or a risk only with web-based applications. There are tools to quickly locate and attack vulnerable applications. Also note that using encrypted channels (e.g. HTTPS) does nothing to prevent this attack. The issue is not related to encrypting the data in transit, rather, it is about keeping the untrusted data away from the backend RDMBS’ interpretation environment.

Here is a simple example of how SQL injection works. Assume we have an application that accepts a last name which will be used to search a database for contact information. The program takes the input, stores it in a variable called lastName, and creates a query:

String sql = "select * from contact_info where lname = '" + lastName + "'";

Now, if an attacker tries the input of: ‘ or 1=1 or ’2′=’

It will create a SQL statement of:

select * from contact_info where lname = '' or 1=1 or '2'=''

This is a legal SQL statement and will retrieve all the rows from the contact_info table. This might expose a lot of data or possibly crash the environment (a denial of service attack). In any case, using other SQL keywords, particularly UNION, the attacker can now explore the database, including other tables and schemas.

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In an earlier blog entry I discussed the potential reduction in refactoring effort if our data is represented as RDF triples rather than relational structures.  As a way to give myself easy access to RDF data and to work more with semantic web tool features I have created a program to export relational data to RDF.

The program is really a proof-of-concept.  It takes a SQL query and converts the resulting rows into assertions of triples.  The approach is simple: given a SQL statement and a chosen primary key column (PK) to represent the instance for the exported data, assert triples with the primary key column value as the subject, the column names as the predicates and the non-PK column values as the objects.

Here is a brief sample taken from the documentation accompanying the code.

• Given a table named people with the following columns and rows:

       id    name    age
       --    ----    ---
       1     Fred    20
       2     Martha  25

• And a query of:  select id, name, age from people
• And the primary key column set to: id
• Then the asserted triples (shown using Turtle and skipping prefixes) will be:

       dsr:PK_1
          a       owl:Thing , dsr:RdbData ;
          rdfs:label "1" ;
          dsr:name "Fred" ;
          dsr:age "20" .

       dsr:PK_2
          a       owl:Thing , dsr:RdbData ;
          rdfs:label "2" ;
          dsr:name "Martha" ;
          dsr:age "25" .

You can see that the approach represents a quick way to convert the data.

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InformationWeek Analytics (http://analytics.informationweek.com/index) invited me to write about the subject of process automation.  The article, part of their series covering application architectures, was released in July of this year.  It provided an opportunity for me to articulate the key components that are required to succeed in the automation of business processes.

Both the business and IT are positioned to make-or-break the use of process automation tools and techniques. The business must redefine its processes and operational rules so that work may be automated.  IT must provide the infrastructure and expertise to leverage the tools of the process automation trade.

Starting with the business there must be clearly defined processes by which work gets done.  Each process must be documented, including the points where decisions are made.  The rules for those decisions must then be documented.  Repetitive, low-value and low-risk decisions are immediate candidates for automation.

A key value point that must be reached in order to extract sustainable and meaningful value from process automation is measured in Straight Through Processing (STP).  STP requires that work arrive from a third-party and be automatically processed; returning a final decision and necessary output (letter, claim payment, etc.) without a person being involved in handling the work.

Most businesses begin using process automation tools without achieving any significant STP rate.  This is fine as a starting point so long as the business reviews the manual work, identifies groupings of work, focuses on the largest groupings (large may be based on manual effort, cost or simple volume) and looks to automate the decisions surrounding that group of work.  As STP is achieved for some work, the review process continues as more and more types of work are targeted for automation.

The end goal of process automation is to have people involved in truly exceptional, high-value, high-risk, business decisions.  The business benefits by having people attend to items that truly matter rather than dealing with a large amount background noise that lowers productivity, morale and client satisfaction.

All of this is great in theory but requires an information technology infrastructure that can meet these business objectives.

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I am happy to report that I have been awarded the Certified Information Systems Security Professional (CISSP) by the International Information Systems Security Certification Consortium [(ISC)²]^a.

I started pursuing the certification in mid-2009, got serious about studying early this year (2010), took the exam in late April, was notified that I passed and had my background endorsed in May, had to update my resume for an auditor in early June and was awarded the CISSP designation at the end of June.

I felt that this certification was important both professionally and personally.

Professionally, the certification serves as a validation that I have a solid and broad understanding of information systems’ security.  People who have worked with me know that I have been focused on IS security for many years.

Whether performing security-centered code reviews, fixing flawed implementations or teaching designers and developers how to improve the security of their systems, I have been on a mission to mentor and train people to observe effective security practices and principles.  I’ve also had operational responsibility for system infrastructures.  With that experience I was able to pass GIAC’s GSEC and Red Hat’s RHCE exams several years ago.

Personally, the process of studying and passing the exam allowed me to pursue and attain a non-trivial goal.  I am enrolled and taking classes toward my master’s degree, but completing that work will require several more years of part time attendance.  Setting and achieving intermediate goals helps to keep me focused and learning.

If you are wondering what the CISSP is all about, please read on.

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I have create my first slightly interesting, to me anyway, program that uses some semantic web technology.  Of course I’ll look back on this in a year and cringe, but for now it represents my understanding of a small set of features from Jena and Pellet.

The basis for the program is an example program that is described in Hebler, Fischer et al’s book “Semantic Web Programming” (ISBN: 047041801X).  The intent of the program is to load an ontology into three models, each running a different level of reasoner (RDF, RDFS and OWL) and output the resulting assertions (triples).

I made a couple of changes to the book’s sample’s approach.  First I allow any supported input file format to be automatically loaded (you don’t have to tell the program what format is being used).  Second, I report the actual differences between the models rather than just showing all the resulting triples.

As I worked on the code, which is currently housed in one uber-class (that’ll have to be refactored!), I realized that there will be lots of reusable “plumbing” code that comes with this type of work.  Setting up models with various reasoners, loading ontologies, reporting triples, interfacing to triple stores, and so on will become nuisance code to write.

Libraries like Jena help, but they abstract at a low level.  I want a semantic workbench that makes playing with the various libraries and frameworks easy.  To that end I’ve created a Sourceforge project called “Semantic Workbench“.

I intend for the Semantic Workbench to provide a GUI environment for manipulating semantic web technologies. Developers and power users would be able to use such a tool to test ontologies, try various reasoners and validate queries.  Developers could use the workbench’s source code to understand how to utilize frameworks like Jena or reasoner APIs like that of Pellet.

I invite other interested people to join the Sourceforge project. The project’s URL is: http://semanticwb.sourceforge.net/

On the data side, in order to have a rich semantic test data set to utilize, I’ve started an ontology that I hope to grow into an interesting example.  I’m using the insurance industry as its basis.  The rules around insurance and the variety of concepts should provide a rich set of classes, attributes and relationships for modeling.  My first version of this example ontology is included with the sample program.

Finally, I’ve added a semantic web section to my website where I’ll maintain links to useful information I find as well as sample code or files that I think might be of interest to other developers.  I’ve placed the sample program and ontology described earlier in this post on that page along with links to a variety of resources.

My site’s semantic web page’s URL is: http://monead.com/semantic/
The URL for the page describing the sample program is: http://monead.com/semantic/proj_diffinferencing.html

One of the aspects of agile software development that may lead to significant angst is the database.  Unlike refactoring code, the refactoring of the database schema involves a key constraint – state!  A developer may rearrange code to his or her heart’s content with little worry since the program will start with a blank slate when execution begins.  However, the database “remembers.”  If one accepts that each iteration of an agile process produces a production release then the stored data can’t be deleted as part of the next iteration.

The refactoring of a database becomes less and less trivial as project development continues.  While developers have IDE’s to refactor code, change packages, and alter build targets, there are few tools for refactoring databases.

My definition of a database refactoring tool is one that assists the database developer by remembering the database transformation steps and storing them as part of the project – e.g. part of the build process.  This includes both the schema changes and data transformations.  Remember that the entire team will need to reproduce these steps on local copies of the database.  It must be as easy to incorporate a peer’s database schema changes, without losing data, as it is to incorporate the code changes.

These same data-centric complexities exist in waterfall approaches when going from one version to the next.  Whenever the database structure needs to change, a path to migrate the data has to be defined.  That transformation definition must become part of the project’s artifacts so that the data migration for the new version is supported as the program moves between environments (test, QA, load test, integrated test, and production).  Also, the database transformation steps must be automated and reversible!

That last point, the ability to rollback, is a key part of any rollout plan.  We must be able to back out changes.  It may be that the approach to a rollback is to create a full database backup before implementing the update, but that assumption must be documented and vetted (e.g. the approach of a full backup to support the rollback strategy may not be reasonable in all cases).

This database refactoring issue becomes very tricky when dealing with multiple versions of an application.  The transformation of the database schema and data must be done in a defined order.  As more and more data is stored, the process consumes more storage and processing resources.  This is the ETL side-effect of any system upgrade.  Its impact is simply felt more often (e.g. potentially during each iteration) in an agile project.

As part of exploring semantic technology, I am interested in contrasting this to a database that consists of RDF triples.  The semantic relationships of data do not change as often (if at all) as the relational constructs.  Many times we refactor a relational database as we discover concepts that require one-to-many or many-to-many relationships.

Is an RDF triple-based database easier to refactor than a relational database?  Is there something about the use of RDF triples that reduces the likelihood of a multiplicity change leading to a structural change in the data?  If so, using RDF as the data format could be a technique that simplifies the development of applications.  For now, let’s take a high-level look at a refactoring use case.

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Last week I had the pleasure of attending Semantic Arts’ training class entitled, “Designing and Building Business Ontologies.”  The course, led by Dave McComb and Simon Robe, provided an excellent introduction to semantic technologies and tools as well as coverage of ontological best practices.  I thoroughly enjoyed the 4-day class and achieved my principle goals in attending; namely to understand the semantic web landscape, including technologies such as RDF, RDFS, OWL, SPARQL, as well as the current state of tools and products in this space.

Both Dave and Simon have a deep understanding of this subject area.  They also work with clients using this technology so they bring real-world examples of where the technology shines and where it has limitations.  I recommend this class to anyone who is seeking to reach a baseline understanding of semantic technologies and ontology strategies.

Why am I so interested in semantic web technology?  I am convinced that structuring information such that it can be consumed by systems, in ways more automated than current data storage and association techniques allow, is required in order to achieve any meaningful advancement in the field of information technology (IT). Whether wiring together web services or setting up ETL jobs to create data marts, too much IT energy is wasted on repeatedly integrating data sources; essentially manually wiring together related information in the absence of the computer being able to wire it together autonomously!

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Encapsulation, one of Object Orientation’s (OO) “Big Three” (or four if you include composition), is the concept most often forgotten when I ask an interview candidate to define the key tenants of OO.  Giving the benefit of the doubt, perhaps it is considered “obvious” and hence not necessarily related to OO design in the person’s mind.  Once I bring it up though, there is usually agreement that it is an important aspect to achieving significant value from OO design.

Classically, encapsulation, also called information hiding, “serves to separate the contractual interface of an abstraction and its implementation.”¹ The idea is that the user of the functionality only knows about the public interface (contractual interface) and has no knowledge, nor any ability to tie itself, to the implementation.  The implementation includes both data representation and, effectively, algorithm.

Many times I’ll get an alternate definition; essentially the respondent will define encapsulation as, “as an approach which allows an object’s behavior to be called without the caller having knowledge of how the behavior is implemented.”  This seems very close to the classical definition but misses the key point of “contractual interface.”

I could argue that when any method is called the caller doesn’t have knowledge of how the method is implemented; it just gets a value back.  The missing aspect, the key aspect, is the constraint regarding which methods the caller may call, e.g. the public interface.

What started me on this topic was a recent conversation with a peer regarding read-only objects.  Before I get into specifics, let me baseline the traditional encapsulation approach in a typical object.

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As I read more and more about the Toyota debacle I’m struck by an apparently myopic management drive to cut costs.  In the case of Toyota it appears that cost cutting extended into quality cutting.  A company once known for superb quality had methodically reduced that aspect of their output.  This isn’t just conjecture; it seems that people inside the company had been aware of a decline in quality due to a focus on reducing costs.¹ Is there a general lesson to consider?

I believe the failure is one of misplaced focus. The focus when Toyota began cutting costs was to remove waste.  That waste could be found throughout their manufacturing processes.  Wasted materials, productivity, tooling, and equipment were all identified as Toyota’s management and workers struck out on a journey to reduce waste and improve productivity.  They ushered in a set of practices that others would soon adopt.

Head back to the 1950′s and you’ll find Taiichi Ohno² hard at work addressing myriad manufacturing shortcomings at Toyota.  Mr. Ohno is really the father of lean manufacturing and just-in-time inventory management.  He didn’t name them as such.  He was just trying to remove waste from the entire manufacturing process.  By the late 1990′s these concepts had become standard operating procedure at many firms.

It makes sense that a business would focus on reducing waste.  Although it may require effort to remove waste without reducing productivity, overall one would expect a leaner process to have an overall efficiency gain.  It would also seem that quality does not benefit from waste.  After many years of experience with these principles, companies have found that an approach of using only the resources that are needed when they are needed provides a sound basis for their operations.  So what happened at Toyota?  They apparently went beyond cutting waste.

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During the past several months I’ve had an interesting experience working with Brainbench.  As you may know, Brainbench (a part of Previsor) offers assessment tests and certifications across a wide range of subjects.  They cover many technical and non-technical areas.  I have taken Brainbench exams myself and I have seen them used as a component within a hiring process.  However, I did not understand how these exams were created.

That mystery ended for me late last year when I received an email looking for technologists to assist in validating a new exam that Brainbench was creating to cover Spring version 2.5.  Being curious about the test creation process I applied for the advertised validator role.  I was pleasantly surprised when they contacted me with an offer for the role of test author instead.

I will not delve into Brainbench’s specific exam creation approach since I assume it is proprietary and want to be sure I respect their intellectual property.  What I found was a very well-planned and thorough process.  Having a background in education and a strong interest in teaching and mentoring, I know the challenge of creating a meaningful assessment.  In the case of their approach, they focus on an accurate and well-considered exam.

I believe that I am quite knowledgeable regarding Spring.  I have used many of its features for work and personal projects.  The philosophies supported by the product (encouraged, not prescribed) address many areas of coding that help reduce clutter, decouple implementations, and simplify testing.  As a true fan of Spring’s feature set, I found it challenging to decide which aspects were most important when assessing an individual’s knowledge of the overall framework. (more…)