Dennis Appelt

Automated testing for SQL injection vulnerabilities: an input mutation approach

Web services are increasingly adopted in various domains, from finance and e-government to social media. As they are built on top of the web technologies, they suffer also an unprecedented amount of attacks and exploitations like the Web. Among the attacks, those that target SQL injection vulnerabilities have consistently been top-ranked for the last years. Testing to detect such vulnerabilities before making web services public is crucial. We present in this paper an automated testing approach, namely μ4SQLi, and its underpinning set of mutation operators. μ4SQLi can produce effective inputs that lead to executable and harmful SQL statements. Executability is key as otherwise no injection vulnerability can be exploited. Our evaluation demonstrated that the approach is effective to detect SQL injection vulnerabilities and to produce inputs that bypass application firewalls, which is a common configuration in real world.

Behind an Application Firewall, Are We Safe from SQL Injection Attacks?

Web application firewalls are an indispensable layer to protect online systems from attacks. However, the fast pace at which new kinds of attacks appear and their sophistication require that firewalls be updated and tested regularly as otherwise they will be circumvented. In this paper, we focus our research on web application firewalls and SQL injection attacks. We present a machine learning-based testing approach to detect holes in firewalls that let SQL injection attacks bypass. At the beginning, the approach can automatically generate diverse attack payloads, which can be seeded into inputs of web- based applications, and then submit them to a system that is protected by a firewall. Incrementally learning from the tests that are blocked or passed by the firewall, our approach can then select tests that exhibit characteristics associated with bypassing the firewall and mutate them to efficiently generate new bypassing attacks. In the race against cyber attacks, time is vital. Being able to learn and anticipate more attacks that can circumvent a firewall in a timely manner is very important in order to quickly fix or fine-tune the firewall. We developed a tool that implements the approach and evaluated it on ModSecurity, a widely used application firewall. The results we obtained suggest a good performance and efficiency in detecting holes in the firewall that could let SQLi attacks go undetected.

Assessing the Impact of Firewalls and Database Proxies on SQL Injection Testing

This paper examines the effects and potential benefits of utilising Web Application Firewalls (WAFs) and database proxies in SQL injection testing of web applications and services. We propose testing the WAF itself to refine and evaluate its security rules and prioritise fixing vulnerabilities that are not protected by the WAF. We also propose using database proxies as oracles for black-box security testing instead of relying only on the output of the application under test. The paper also presents a case study of our proposed approaches on two sets of web services. The results indicate that testing through WAFs can be used to prioritise vulnerabilities and that an oracle that uses a database proxy finds more vulnerabilities with fewer tries than an oracle that relies only on the output of the application.

SOFIA: an automated security oracle for black-box testing of SQL-injection vulnerabilities

Security testing is a pivotal activity in engineering secure software. It consists of two phases: generating attack inputs to test the system, and assessing whether test executions expose any vulnerabilities. The latter phase is known as the security oracle problem. In this work, we present SOFIA, a Security Oracle for SQL-Injection Vulnerabilities. SOFIA is programming-language and source-code independent, and can be used with various attack generation tools. Moreover, because it does not rely on known attacks for learning, SOFIA is meant to also detect types of SQLi attacks that might be unknown at learning time. The oracle challenge is recast as a one-class classification problem where we learn to characterise legitimate SQL statements to accurately distinguish them from SQLi attack statements. We have carried out an experimental validation on six applications, among which two are large and widely-used. SOFIA was used to detect real SQLi vulnerabilities with inputs generated by three attack generation tools. The obtained results show that SOFIA is computationally fast and achieves a recall rate of 100% (i.e., missing no attacks) with a low false positive rate (0.6%).

Automatically Repairing Web Application Firewalls Based on Successful SQL Injection Attacks

Testing and fixing Web Application Firewalls (WAFs) are two relevant and complementary challenges for security analysts. Automated testing helps to cost-effectively detect vulnerabilities in a WAF by generating effective test cases, i.e., attacks. Once vulnerabilities have been identified, the WAF needs to be fixed by augmenting its rule set to filter attacks without blocking legitimate requests. However, existing research suggests that rule sets are very difficult to understand and too complex to be manually fixed. In this paper, we formalise the problem of fixing vulnerable WAFs as a combinatorial optimisation problem. To solve it, we propose an automated approach that combines machine learning with multi-objective genetic algorithms. Given a set of legitimate requests and bypassing SQL injection attacks, our approach automatically infers regular expressions that, when added to the WAF’s rule set, prevent many attacks while letting legitimate requests go through. Our empirical evaluation based on both open-source and proprietary WAFs shows that the generated filter rules are effective at blocking previously identified and successful SQL injection attacks (recall between 54.6% and 98.3%), while triggering in most cases no or few false positives (false positive rate between 0% and 2%).