On automated prepared statement generation to remove SQL injection vulnerabilities

Thomas, Stephen W.; Williams, Laurie; Xie, Tao

doi:10.1016/j.infsof.2008.08.002

Cited by 58 publications

(40 citation statements)

References 20 publications

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“…Vulnerabilities" [18] Thomas et al [18] presented an algorithm in which the prepared statements in SQL queries are replaced by secure prepared statements for removing SQL vulnerabilities. These prepared statements have a static structure that prevents SQLIAs from changing the logical structure of a prepared statement.…”

Section: "On Automated Prepared Statement Generation To Remove Sql Inmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on the Detection of SQL Injection Attacks and Their Countermeasures

2015

J Inf Process Syst

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The Structured Query Language (SQL) Injection continues to be one of greatest security risks in the world according to the Open Web Application Security Project's (OWASP) [1] Top 10 Security vulnerabilities 2013. The ease of exploitability and severe impact puts this attack at the top. As the countermeasures become more sophisticated, SOL Injection Attacks also continue to evolve, thus thwarting the attempt to eliminate this attack completely. The vulnerable data is a source of worry for government and financial institutions. In this paper, a detailed survey of different types of SQL Injection and proposed methods and theories are presented, along with various tools and their efficiency in intercepting and preventing SQL attacks.

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Section: "On Automated Prepared Statement Generation To Remove Sql Inmentioning

confidence: 99%

“…14 Thomas et al [18] Proposes secure prepared statement to prevent attempt to change logical structure of queries.…”

mentioning

confidence: 99%

A Survey on the Detection of SQL Injection Attacks and Their Countermeasures

2015

J Inf Process Syst

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“…Since it is easy to miss unsanitized inputs, some techniques automatically support developers in this task. However, they either statically analyze and change the server side code before deployment [27,19,18,3], or they monitor and sanitize queries at runtime on the deployed system [8,7,10,21,22,28]. The first ones require access to the server side code, which is not always available, and are bound to specific programming languages.…”

Section: Detection or Prevention Of Sql Injectionsmentioning

confidence: 99%

Search-based security testing of web applications

Thomé

Gorla

Zeller

2014

Proceedings of the 7th International Workshop on Search-Based Software Testing

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SQL injections are still the most exploited web application vulnerabilities. We present a technique to automatically detect such vulnerabilities through targeted test generation. Our approach uses search-based testing to systematically evolve inputs to maximize their potential to expose vulnerabilities. Starting from an entry URL, our BIOFUZZ prototype systematically crawls a web application and generates inputs whose effects on the SQL interaction are assessed at the interface between Web server and database. By evolving those inputs whose resulting SQL interactions show best potential, BIOFUZZ exposes vulnerabilities on real-world Web applications within minutes. As a black-box approach, BIO-FUZZ requires neither analysis nor instrumentation of server code; however, it even outperforms state-of-the-art whitebox vulnerability scanners.

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“…Attributes [16][17][18][19][20][21][22] in Table 1 represent the classifications presented above. We shall provide more details on the classification methods in our example section.…”

Section: A Hybrid Attributesmentioning

confidence: 99%

Mining SQL injection and cross site scripting vulnerabilities using hybrid program analysis

Shar

Tan

Briand

2013

2013 35th International Conference on Software Engineering (ICSE)

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Abstract-In previous work, we proposed a set of static attributes that characterize input validation and input sanitization code patterns. We showed that some of the proposed static attributes are significant predictors of web application vulnerabilities related to SQL injection and cross site scripting. Static attributes have the advantage of reflecting general properties of a program. Yet, dynamic attributes collected from execution traces may reflect more specific code characteristics that are complementary to static attributes. Hence, to improve our initial work, in this paper, we propose the use of dynamic attributes to complement static attributes in the prediction of vulnerabilities. Furthermore, since existing work relies on supervised learning, it is dependent on the availability of training data labeled with known vulnerabilities. This paper presents prediction models that are based on both classification and clustering in order to predict vulnerabilities, working in the presence or absence of labeled training data, respectively. In our experiments across six applications, our new supervised vulnerability predictors based on hybrid (static and dynamic) attributes achieved, on average, 90% recall and 85% precision, that is a sharp increase in recall when compared to static analysis-based predictions. Though not nearly as accurate, our unsupervised predictors based on clustering achieved, on average, 76% recall and 39% precision, thus suggesting they can be useful in the absence of labeled training data.Index Terms-Defect prediction, vulnerability, input validation and sanitization, static and dynamic analysis, empirical study.

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On automated prepared statement generation to remove SQL injection vulnerabilities

Cited by 58 publications

References 20 publications

A Survey on the Detection of SQL Injection Attacks and Their Countermeasures

A Survey on the Detection of SQL Injection Attacks and Their Countermeasures

Search-based security testing of web applications

Mining SQL injection and cross site scripting vulnerabilities using hybrid program analysis

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