The author updates his analysis of Open Source Software applications, their development methodology, and the frequency of the discovery of security vulnerabilities.

This article is a followup to an article entitled The Myth of Open Source Security Revisited. The original article tackled the common misconception amongst users of Open Source Software(OSS) that OSS is a panacea when it comes to creating secure software. The article presented anecdotal evidence taken from an article written by John Viega, the original author of GNU Mailman, to illustrate its point. This article follows up the anecdotal evidence presented in the original paper by providing an analysis of similar software applications, their development methodology and the frequency of the discovery of security vulnerabilities.

The purpose of this article is to expose the fallacy of the belief in the "inherent security" of Open Source software and instead point to a truer means of ensuring the quality of the security of a piece software is high.

Apples, Oranges, Penguins and Daemons

When performing experiments to confirm a hypothesis on the effect of a particular variable on an event or observable occurence, it is common practice to utilize control groups. In an attempt to establish cause and effect in such experiments, one tries to hold all variables that may affect the outcome constant except for the variable that the experiment is interested in. Comparisons of the security of software created by Open Source processes and software produced in a proprietary manner have typically involved several variables besides development methodology.

A number of articles have been written that compare the security of Open Source development to proprietary development by comparing security vulnerabilities in Microsoft products to those in Open Source products. Noted Open Source pundit, Eric Raymond wrote an article on NewsForge where he compares Microsoft Windows and IIS to Linux, BSD and Apache. In the article, Eric Raymond states that Open Source development implies that "security holes will be infrequent, the compromises they cause will be relatively minor, and fixes will be rapidly developed and deployed.". However, upon investigation it is disputable that Linux distributions have less frequent or more minor security vulnerabilities when compared to recent versions of Windows. In fact the belief in the inherent security of Open Source software over proprietary software seems to be the product of a single comparison, Apache versus Microsoft IIS.

There are a number of variables involved when one compares the security of software such as Microsoft Windows operating systems to Open Source UNIX-like operating systems including the disparity in their market share, the requirements and dispensations of their user base, and the differences in system design. To better compare the impact of source code licensing on the security of the software, it is wise to reduce the number of variables that will skew the conclusion. To this effect it is best to compare software with similar system design and user base than comparing software applications that are significantly distinct. The following section analyzes the frequency of the discovery of security vulnerabilities in UNIX-like operating systems including HP-UX, FreeBSD, RedHat Linux, OpenBSD, Solaris, Mandrake Linux, AIX and Debian GNU/Linux.

Security Vulnerability Face-Off

Below is a listing of UNIX and UNIX-like operating systems with the number of security vulnerabilities that were discovered in them in 2001 according to the Security Focus Vulnerability Archive.

AIX

10 vulnerabilities[6 remote, 3 local, 1 both]

Debian GNU/Linux

13 vulnerabilities[1 remote, 12 local] + 1 Linux kernel vulnerability[1 local]

FreeBSD

24 vulnerabilities[12 remote, 9 local, 3 both]

HP-UX

25 vulnerabilities[12 remote, 12 local, 1 both]

Mandrake Linux

17 vulnerabilities[5 remote, 12 local] + 12 Linux kernel vulnerabilities[5 remote, 7 local]

OpenBSD

13 vulnerabilities[7 remote, 5 local, 1 both]

Red Hat Linux

28 vulnerabilities[5 remote, 22 local, 1 unknown] + 12 Linux kernel vulnerabilities[6 remote, 6 local]

Solaris

38 vulnerabilities[14 remote, 22 local, 2 both]

From the above listing one can infer that source licensing is not a primary factor in determining how prone to security flaws a software application will be. Specifically proprietary and Open Source UNIX family operating systems are represented on both the high and low ends of the frequency distribution.

Factors that have been known to influence the security and quality of a software application are practices such as code auditing (peer review), security-minded architecture design, strict software development practices that restrict certain dangerous programming constructs (e.g. using the str* or scanf* family of functions in C) and validation & verification of the design and implementation of the software. Also reducing the focus on deadlines and only shipping when the system the system is in a satisfactory state is important.

Both the Debian and OpenBSD projects exhibit many of the aforementioned characteristics which help explain why they are the Open Source UNIX operating systems with the best security record. Debian's track record is particularly impressive when one realizes that the Debian Potato consists of over 55 million lines of code (compared to RedHat's 30,000,000 million lines of code).

The Road To Secure Software

Exploitable security vulnerabilities in a software application are typically evidence of bugs in the design or implementation of the application. Thus the process of writing secure software is an extension of the process behind writing robust, high quality software. Over the years a number of methodolgies have been developed to tackle the problem of producing high quality software in a repeatable manner within time and budgetary constraints. The most successful methodologies have typically involved using the following software quality assurance, validation and verification techniques; formal methods, code audits, design reviews, extensive testing and codified best practices.

1. Formal Methods: One can use formal proofs based on mathematical methods and rigor to verify the correctness of software algorithms. Tools for specifying software using formal techniques exist such as VDM and Z. Z (pronounced 'zed') is a formal specification notation based on set theory and first order predicate logic. VDM stands for "The Vienna Development Method" which consists of a specification language called VDM-SL, rules for data and operation refinement which allow one to establish links between abstract requirements specifications and detailed design specifications down to the level of code, and a proof theory in which rigorous arguments can be conducted about the properties of specified systems and the correctness of design decisions.The previous descriptions were taken from the Z FAQ and the VDM FAQ respectively. A comparison of both specification languages is available in the paper, Understanding the differences between VDM and Z by I.J. Hayes et al.
   
   
2. Code Audits: Reviews of source code by developers other than the author of the code are good ways to catch errors that may have been overlooked by the original developer. Source code audits can vary from informal reviews with little structure to formal code inspections or walkthroughs. Informal reviews typically involve the developer sending the reviewers source code or descriptions of the software for feedback on any bugs or design issues. A walkthrough involves the detailed examination of the source code of the software in question by one or more reviewers. An inspection is a formal process where a detailed examination of the source code is directed by reviewers who act in certain roles. A code inspection is directed by a "moderator", the source code is read by a "reader" and issues are documented by a "scribe".
   
   
3. Testing: The purpose of testing is to find failures. Unfortunately, no known software testing method can discover all possible failures that may occur in a faulty application and metrics to establish such details have not been forthcoming. Thus a correlation between the quality of a software application and the amount of testing it has endured is practically non-existent.
   
   There are various categories of tests including unit, component, system, integration, regression, black-box, and white-box tests. There is some overlap in the aforementioned mentioned testing categories.
   
   Unit testing involves testing small pieces of functionality of the application such as methods, functions or subroutines. In unit testing it is usual for other components that the software unit interacts with to be replaced with stubs or dummy methods. Component tests are similar to unit tests with the exception that dummmy and stub methods are replaced with the actual working versions. Integration testing involves testing related components that communicate with each other while system tests involve testing the entire system after it has been built. System testing is necessary even if extensive unit or component testing has occured because it is possible for seperate subroutines to work individually but fail when invoked sequentialy due to side effects or some error in programmer logic. Regression testing involves the process of ensuring that modifications to a software module, component or system have not introduced errors into the software. A lack of sufficient regression testing is one of the reasons why certain software patches break components that worked prior to installation of the patch.
   
   Black-box testing also called functional testing or specification testing test the behavior of the component or system without requiring knowledge of the internal structure of the software. Black-box testing is typically used to test that software meets its functional requirements. White-box testing also called structural or clear-box testing involves tests that utilize knowledge of the internal structure of the software. White-box testing is useful in ensuring that certain statements in the program are excercised and errors discovered. The existence of code coverage tools aid in discovering what percentages of a system are being excercised by the tests.
   
   More information on testing can be found at the comp.software.testing FAQ .
   
   
4. Design Reviews: The architecture of a software application can be reviewed in a formal process called a design review. In design reviews the developers, domain experts and users examine that the design of the system meets the requirements and that it contains no significant flaws of omission or commission before implementation occurs.
   
   
5. Codified Best Practices: Some programming languages have libraries or language features that are prone to abuse and are thus prohibited in certain disciplined software projects. Functions like strcpy, gets, and scanf in C are examples of library functions that are poorly designed and allow malicious individuals to use buffer overflows or format string attacks to exploit the security vulnerabilities exposed by using these functions. A number of platforms explicitly disallow gets especially since alternatives exist. Programming guidelines for such as those written by Peter Galvin in a Unix Insider article on designing secure software are used by development teams to reduce the likelihood of security vulnerabilities in software applications.

Projects such as the OpenBSD project that utilize most of the aforementioned techniques in developing software typically have a low incidence of security vulnerabilities.

Issues Preventing Development of Secure Open Source Software

One of the assumptions that is typically made about Open Source software is that the availability of source code translates to "peer review" of the software application. However, the anecdotal experience of a number of Open Source developers including John Viega belies this assumption.

The term "peer review" implies an extensive review of the source code of an application by competent parties. Many Open Source projects do not get peer reviewed for a number of reasons including

• complexity of code in addition to a lack of documentation makes it difficult for casual users to understand the code enough to give a proper review
  
  
• developers making improvements to the application typically focus only on the parts of the application that will affect the feature to be added instead of the whole system.
  
  
• ignorance of developers to security concerns.
  
  
• complacency in the belief that since the source is available that it is being reviewed by others.
  
  

Also the lack of interest in unglamorous tasks like documentation and testing amongst Open Source contributors adversely affects quality of the software. However, all of these issues can and are solved in projects with a disciplined software development process, clearly defined roles for the contributers and a semi-structured leadership hierarchy.

Benefits of Open Source to Security-Conscious Users

Despite the fact that source licensing and source code availability are not indicators of the security of a software application, there is still a significant benefit of Open Source to some users concerned about security. Open Source allows experts to audit their software options before making a choice and also in some cases to make improvements without waiting for fixes from the vendor or source code maintainer.

One should note that there are constraints on the feasibility of users auditing the software based on the complexity and size of the code base. For instance, it is unlikely that a user who wants to make a choice of using Linux as a web server for a personal homepage will scrutinize the TCP/IP stack code.

References

1. Frankl, Phylis et al. Choosing a Testing Method to Deliver Reliability. Proceedings of the 19th International Conference on Software Engineering, pp. 68--78, ACM Press, May 1997. < http://citeseer.nj.nec.com/frankl97choosing.html >
   
   
2. Hamlet, Dick. Software Quality, Software Process, and Software Testing. 1994. < http://citeseer.nj.nec.com/hamlet94software.html>
   
   
3. Hayes, I.J., C.B. Jones and J.E. Nicholls. Understanding the differences between VDM and Z. Technical Report UMCS-93-8-1, University of Manchester, Computer Science Dept., 1993. < http://citeseer.nj.nec.com/hayes93understanding.html >
   
   
4. Miller, Todd C. and Theo De Raadt. strlcpy and strlcat - consistent, safe, string copy and concatenation. Proceedings of the 1999 USENIX Annual Technical Conference, FREENIX Track, June 1999. < http://www.usenix.org/events/usenix99/full_papers/millert/millert_html/ >
   
   
5. Viega, John. The Myth of Open Source Security. Earthweb.com. < http://www.earthweb.com/article/0,,10455_626641,00.html >
   
   
6. Gonzalez-Barona, Jesus M. et al. Counting Potatoes: The Size of Debian 2.2. < http://people.debian.org/~jgb/debian-counting/counting-potatoes/ >
   
   
7. Wheeler, David A. More Than A Gigabuck: Estimating GNU/Linux's Size. < http://www.counterpane.com/crypto-gram-0003.html >
   
   

Acknowledgements

The following people helped in proofreading this article and/or offering suggestions about content: Jon Beckham, Graham Keith Coleman, Chris Bradfield, and David Dagon.

About the Author

Dare Obasanjo is a recent graduate of the Georgia Institute of Technology, with a degree with honors in computer science. (This article was written there.) The author is a vigorous participant in discussion forums such as Slashdot, Kuro5hin, and Advogato, on various aspects of software development. He has written numerous articles on the subject.

(c) 2002 Dare Obasanjo