Automatic creation of SQL Injection and cross-site scripting attacks
By Adam Kiezun, Philip J. Guo, Karthick Jayaraman, Michael D. Ernst. 2009 IEEE 31st International Conference on Software Engineering, 2009
425 citations recorded by the source corpus.
We present a technique for finding security vulnerabilities in Web applications. SQL Injection (SQLI) and cross-site scripting (XSS) attacks are widespread forms of attack in which the attacker crafts the input to the application to access or modify user data and execute malicious code. In the most serious attacks (called second-order, or persistent, XSS), an attacker can corrupt a database so as to cause subsequent users to execute malicious code.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/478b1699c2c3b1207629b127950f6d045adda5d7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Lionel Claude Briand
- Andrea Arcuri
- Peng Ning
- Dawn Song
- Prateek Saxena
- Guofei Gu
- Vijay Ganesh
- Stephen McCamant
In Information Systems
- Andrea Arcuri
- Lionel Claude Briand
- Michael D. Ernst
- Dawn Song
- Steve Hanna
- Vijay Ganesh
- Stephen McCamant
- Prateek Saxena
Citations in the corpus
- HAMPI: a solver for string constraints (2009, methodology): “We ran our modified Ardilla on 5 PHP applications (the same set as the original Ardilla study [25], totaling 14,941 lines of PHP code).”
- A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering (2014, methodology): “None of the papers surveyed in Section 3 report skewness or kurtosis values.”
- A Symbolic Execution Framework for JavaScript (2010, methodology): “Several previous tools have demonstrated the use of symbolic execution for finding SQL injection and reflected or stored cross-site scripting attacks to code written in PHP and Java [1], [18], [30].”
- A practical guide for using statistical tests to assess randomized algorithms in software engineering (2011, background): “[18] ICSE 1 None [33] ICSE 1 None [4] SSBSE 1000 Linear regression [21] SSBSE 30/500 None [14] SSBSE 100 U-test”
- AppIntent: analyzing sensitive data transmission in android for privacy leakage detection (2013, background): “generation [14, 17, 27, 28, 34, 39], fuzz testing [35], and security flaws detection [13, 15, 20, 26, 31, 42].”