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Using SQL Hotspots in a Prioritization Heuristic for Detecting All Types of Web Application Vulnerabilities

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B. Smith, L. Williams, "Using SQL Hotspots in a Prioritization Heuristic for Detecting All Types of Web Application Vulnerabilities", Proceedings of the International Conference on Software Testing, Verification and Validation (ICST 2011), Berlin, Germany, pp. 220-229, 2011.

Abstract

Development organizations often do not have time to perform security fortification on every file in a product before release. One way of prioritizing security efforts is to use metrics to identify core business logic that could contain vulnerabilities, such as database interaction code. Database code is a source of SQL injection vulnerabilities, but importantly may be home to unrelated vulnerabilities. The goal of this research is to improve the prioritization of security fortification efforts by investigating the ability of SQL hotspots to be used as the basis for a heuristic for prediction of all vulnerability types. We performed empirical case studies of 15 releases of two open source PHP web applications: WordPress, a blogging application, and WikkaWiki, a wiki management engine. Using statistical analysis, we show that the more SQL hotspots a file contains per line of code, the higher the probability that file will contain any type of vulnerability.

1. Introduction

We can get good designs by following good practices instead of poor ones.
~F. Brooks, Jr.

The war for a trustworthy Internet continues. The popular social networking site Twitter was recently compromised by two cross-site scripting attacks, which are common and easy-to-execute exploits of a codelevel programming error[5]. Input validation vulnerabilities1 like this are in the CWE/SANS Top 25 Most Dangerous Programming Errors for 20102 despite the plethora of proposed techniques for protecting against code-level attacks (e.g. the context sensitive string evaluation method proposed by[11]). Additionally, the SANS list of Top Cyber Security Risks3 indicates that input validation vulnerabilities, such as SQL injection, cross-site scripting, and file inclusion continue to be the three most popular techniques used for compromising web sites.

Although techniques such as code reviews and design discussions can help developers reduce the number of vulnerabilities they introduce into the source code, the software development community currently has no single solution that will eliminate all security issues[7]. Furthermore, development organizations often do not have the time or resources to perform vulnerability detection efforts on every source file in a product before its release. Validation and verification (V&V) must be prioritized in such a way that the security fortification starts with the files that are most likely to be vulnerable first. SQL hotspots may help development organizations prioritize security fortification efforts. SQL hotspots (or just "hotspots" in this paper) are any point in the application source code where the system interacts with a database management system[3, 6]. Hotspots are typically associated with input validation vulnerabilities like SQL injection4, but they might also be useful for predicting any web application vulnerability since they protect the typical web application's most valuable asset: the database[3, 6].

The goal of this research is to improve the prioritization of security fortification efforts by investigating the ability of SQL hotspots to be used as the basis for a heuristic for the prediction of all vulnerability types. We have already defined the identification of hotspots[14], and demonstrated[15] that testers can target hotspots at the system level to expose error message information leakage vulnerabilities5. In this paper, we evaluate the ability of hotspots used in a model with number of lines of code to perform in prediction models that can help point testers to files in the source code that are likely to contain all types of web application vulnerabilities. We include lines of code in our model as a way of normalizing the number of SQL hotspots per file to make the comparison between files more accurate even as file sizes vary.

We built and analyzed a prediction model based on the security vulnerability reports of two open source PHP web applications: nine releases of WordPress6, a blogging application, and six releases of WikkaWiki7, a wiki management engine. We compared the evaluation of our model's ability to predict vulnerable files with a random guess calculated based on the distribution of vulnerabilities within each system. The contributions of this paper are as follows:

  • Empirical evidence that SQL hotspots can be used along with lines of code as the basis for a heuristic for prioritizing security V&V efforts because they are predictive of all types of web application vulnerabilities.
  • A resultant design strategy that recommends separating the database concern of an application into a single file to produce a lower proportion of input validation vulnerabilities.

The rest of this paper is organized as follows. Section 2 presents background information related to vulnerability identification. Then, Section 3 reviews related work. Next, Section 4 presents our methodology for gathering and analyzing the vulnerability data. Section 5 presents the results of the study and Section 6 presents the limitations of this study. Finally, Section 7 concludes.

2. Background

According to the ISO, a vulnerability is “..an instance of a [fault] in the specification, development, or configuration of software such that its execution can violate an [implicit or explicit] security policy” [4]. Since no single validation or verification practice can detect every vulnerability in a system[7], we have to assume that the file may have latent, undiscovered vulnerabilities. We call files vulnerable that have been changed due to a vulnerability report. We call files that have not been changed due to vulnerability reports neutral.

A predictive model for classifying components as being either vulnerable or neutral will make either correct or incorrect classifications. As such, for a given test of the model, there are true positives, where the model correctly classifies a component as vulnerable, and true negatives, where the model correctly classifies the component as neutral. When the model is wrong, there are false positives, where the model classifies the component as being vulnerable, but the component was neutral, and false negatives where the model failed to identify a vulnerable component. The performance of a given model to classify components as being one of two binary options has often been evaluated using two measurements: precision and recall[10].

Precision is defined in Equation 1, where tp is the number of true positives identified by the model, and fp is the number of false positives identified by the model. Precision can be viewed as a measure of exactness that a model exhibits.

Recall measures the number of vulnerable files the model retrieves, and is defined in Equation 2 where tp is the number of true positives, and fn is the number of false negatives.

 

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Other researchers have empirically examined the vulnerability reports of open source applications to determine the best predictive models for vulnerability locations. Nehaus et al. [9] use their tool, Vulture, to predict vulnerable software components in versions of the Mozilla web browser. They demonstrate that vulnerabilities correlate with component imports and that component imports in the Mozilla web browser can be used to consistently and accurately predict vulnerable components. Specifically, Nehaus et al. found that certain imports are almost guaranteed to produce security problems with the importing component later in time.

4. Methodology

5. Results

6. Limitations

7. Conclusion

8. Acknowledgements

9. References

[1] T. Fawcett, "An introduction to ROC analysis," Pattern Recognition Letters, vol. 27, no. 8, pp. 861-874, 2006.
[2] M. Gegick, L. Williams, J. Osborne, and M. Vouk, "Prioritizing software security fortification through code-level metrics," in ACM Workshop on Quality of Protection (QoP2008), Alexandria, Virginia, 2008, pp. 31-38.
[3] W. G. J. Halfond and A. Orso, "AMNESIA: analysis and monitoring for neutralizing SQLinjection attacks," in 20th IEEE/ACM Conference on Automated Software Engineering, Long Beach, CA, USA, 2005, pp. 174-183.
[4] ISO/IEC, "DIS 14598-1 Information technology -Software product evaluation," 1996.
[5] J. Kirk, "Twitter Contains Second worm in a Week," in PCWorld Business Center, 2010, http://www.pcworld.com/businesscenter/article/206232/twitter_contains_second_worm_in_a_week.html.
[6] Y. Kosuga, K. Kono, M. Hanaoka, M. Hishiyama, and Y. Takahama, "Sania: syntactic and semantic analysis for automated testing against SQL injection," in 23rd Annual Computer Security Applications Conference, Miami Beach, FL, 2007, pp. 107-117.
[7] G. McGraw, Software Security: Building Security In. Reading, Massachusetts: Addison-Wesley Professional, 2006.
[8] A. Meneely and L. Williams, "Secure open source collaboration: an empirical study of linus' law," in ACM Conference on Computer and Communications Security (CCS2009), Chicago, Illinois, 2009, pp. 453-462.
[9] S. Nehaus, T. Zimmerman, C. Holler, and A. Zeller, "Predicting vulnerable software components," in ACM Conference on computer and communications security, Alexandria, Virginia, USA, 2007, pp. 529-540.
[10] D. L. Olson and D. Delen, Advanced Data Mining Techniques. Berlin Heidelberg: Springer, 2008.
[11] T. Pietraszek and C. V. Berghe, "Defending Against Injection Attacks Through ContextSensitive String Evaluation," in Recent Advances in Intrusion Detection, Springer LNCS 3858, Seattle, Washington, 2006, pp. 124-145.
[12] Y. Shin, A. Meneely, L. Williams, and J. A. Osbourne, "Evaluating Complexity, Code Churn, and Developer Activity metrics as Indicators of Software Vulnerabilities," Transactions on Software Engineering, 2010, to appear. DOI 10.1109/TSE.2010.81.
[13] Y. Shin and L. Williams, "Is complexity really the enemy of software security?," in ACM workshop on Quality of protection (QoP2008), Alexandria, Virginia, 2008, pp. 47-50.
[14] B. Smith, Y. Shin, and L. Williams, "Proposing SQL Statement Coverage Metrics," in Software Engineering for Secure Systems (SESS2008), colocated with ICSE 2008., Leipzig, Germany, 2008, pp. 49-56.
[15] B. Smith, L. Williams, and A. Austin, "Idea: Using system level testing for revealing SQLinjection related error message information leaks," Lecture Notes in Computer Science, vol. 5965, pp. 192-200, Symposium on Engineering Secure Software and Systems 2010 (ESSoS 2010), 2010.
[16] J. Walden, M. Doyle, R. Lenhof, and J. Murray, "Idea: Java vs. PHP: Security Implications of Language Choice for Web Applications," in Engineering Secure Software and Systems, Springer LNCS 5965, Pisa, Italy, 2010, pp. 61-69.
[17] T. Zimmerman, N. Nagappan, and L. Williams, "Searching for a Needle in a Haystack: Predicting Security Vulnerabilities for Windows Vista," in International Conference on Software Testing (ICST 2010), Paris, France, 2010, pp. 421-428.

10. End Notes

  1. Input validation vulnerabilities occur when a system does not assert that input falls within an acceptable range, allowing the system to be exploited perform unintended functionality.
  2. http://cwe.mitre.org/top25/
  3. http://www.sans.org/critical-security-controls/#summary
  4. SQL injection vulnerabilities occur when a lack of input validation could allow a user to force unintended system behavior by altering the logical structure of a SQL statement using SQL reserved words and special characters.
  5. Error message vulnerabilities occur when the system does not correctly handle an exceptional condition, causing sensitive.
  6. http://wordpress.org/
  7. http://wikkawiki.org/HomePage

 

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