The article Search-based multi-vulnerability testing of XML injections in web applications, written by Sadeeq Jan, Annibale Panichella, Andrea Arcuri, and Lionel Briand, was originally published electronically on the publisher’s internet portal (currently SpringerLink) on May 2019 without open access. With the author(s)’ decision to opt for Open Choice the copyright of the article changed on June 2019 to@en

Complex systems such as autonomous cars are typically built as a composition of features that are independent units of functionality. Features tend to interact and impact one another’s behavior in unknown ways. A challenge is to detect and manage feature interactions, in particular, those that violate system requirements, hence leading to failures. In this paper, we propose a technique to detect feature interaction failures by casting this problem into a search-based test generation problem. We define a set of hybrid test objectives (distance functions) that combine traditional coverage-based heuristics with new heuristics specifically aimed at revealing feature interaction failures. We develop a new search-based test generation algorithm, called FITEST, that is guided by our hybrid test objectives. FITEST extends recently proposed many-objective evolutionary algorithms to reduce the time required to compute fitness values. We evaluate our approach using two versions of an industrial self-driving system. Our results show that our hybrid test objectives are able to identify more than twice as many feature interaction failures as two baseline test objectives used in the software testing literature (i.e., coverage-based and failure-based test objectives). Further, the feedback from domain experts indicates that the detected feature interaction failures represent real faults in their systems that were not previously identified based on analysis of the system features and their requirements.@en

Many software engineering activities process the events contained in log files. However, before performing any processing activity, it is necessary to parse the entries in a log file, to retrieve the actual events recorded in the log. Each event is denoted by a log message, which is composed of a fixed part—called (event) template—that is the same for all occurrences of the same event type, and a variable part, which may vary with each event occurrence. The formats of log messages, in complex and evolving systems, have numerous variations, are typically not entirely known, and change on a frequent basis; therefore, they need to be identified automatically. The log message format identification problem deals with the identification of the different templates used in the messages of a log. Any solution to this problem has to generate templates that meet two main goals: generating templates that are not too general, so as to distinguish different events, but also not too specific, so as not to consider different occurrences of the same event as following different templates; however, these goals are conflicting. In this paper, we present the approach approach, which recasts the log message identification problem as a multi-objective problem. approach uses an evolutionary approach to solve this problem, by tailoring the NSGA-II algorithm to search the space of solutions for a Pareto optimal set of message templates. We have implemented approach in a tool, which we have evaluated on six real-world datasets, containing log files with a number of entries ranging from 2K to 300K. The experiments results show that approach extracts by far the highest number of correct log message templates, significantly outperforming two state-of-the-art approaches on all datasets.@en

In the past years, several automated repair strategies have been proposed to fix bugs in individual software programs without any human intervention. There has been, however, little work on how automated repair techniques can resolve failures that arise at the system-level and are caused by undesired interactions among different system components or functions. Feature interaction failures are common in complex systems such as autonomous cars that are typically built as a composition of independent features (i.e., units of functionality). In this paper, we propose a repair technique to automatically resolve undesired feature interaction failures in automated driving systems (ADS) that lead to the violation of system safety requirements. Our repair strategy achieves its goal by (1) localizing faults spanning several lines of code, (2) simultaneously resolving multiple interaction failures caused by independent faults, (3) scaling repair strategies from the unit-level to the system-level, and (4) resolving failures based on their order of severity. We have evaluated our approach using two industrial ADS containing four features. Our results show that our repair strategy resolves the undesired interaction failures in these two systems in less than 16h and outperforms existing automated repair techniques.@en

Modern web applications often interact with internal web services, which are not directly accessible to users. However, malicious user inputs can be used to exploit security vulnerabilities in web services through the application front-ends. Therefore, testing techniques have been proposed to reveal security flaws in the interactions with back-end web services, e.g., XML Injections (XMLi). Given a potentially malicious message between a web application and web services, search-based techniques have been used to find input data to mislead the web application into sending such a message, possibly compromising the target web service. However, state-of-the-art techniques focus on (search for) one single malicious message at a time. Since, in practice, there can be many different kinds of malicious messages, with only a few of them which can possibly be generated by a given front-end, searching for one single message at a time is ineffective and may not scale. To overcome these limitations, we propose a novel co-evolutionary algorithm (COMIX) that is tailored to our problem and uncover multiple vulnerabilities at the same time. Our experiments show that COMIX outperforms a single-target search approach for XMLi and other multi-target search algorithms originally defined for white-box unit testing.@en

Modern enterprise systems can be composed of many web services (e.g., SOAP and RESTful). Users of such systems might not have direct access to those services, and rather interact with them through a single entry point which provides a GUI (e.g., a web page or a mobile app). Although the interactions with such entry point might be secure, a hacker could trick such systems to send malicious inputs to those internal web services. A typical example is XML injection targeting SOAP communications. Previous work has shown that it is possible to automatically generate such kind of attacks using search-based techniques. In this paper, we improve upon previous results by providing more efficient techniques to generate such attacks. In particular, we investigate four different algorithms and two different fitness functions. A large empirical study, involving also two industrial systems, shows that our technique is effective at automatically generating XML injection attacks.@en

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%).@en

Web application firewalls (WAF) are an essential protection mechanism for online software systems. Because of the relentless flow of new kinds of attacks as well as their increased sophistication, WAFs have to be updated and tested regularly to prevent attackers from easily circumventing them. In this paper, we focus on testing WAFs for SQL injection attacks, but the general principles and strategy we propose can be adapted to other contexts. We present ML-Driven, an approach based on machine learning and an evolutionary algorithm to automatically detect holes in WAFs that let SQL injection attacks bypass them. Initially, ML-Driven automatically generates a diverse set of attacks and submit them to the system being protected by the target WAF. Then, ML-Driven selects attacks that exhibit patterns (substrings) associated with bypassing the WAF and evolve them to generate new successful bypassing attacks. Machine learning is used to incrementally learn attack patterns from previously generated attacks according to their testing results, i.e., if they are blocked or bypass the WAF. We implemented ML-Driven in a tool and evaluated it on ModSecurity, a widely used open-source WAF, and a proprietary WAF protecting a financial institution. Our empirical results indicate that ML-Driven is effective and efficient at generating SQL injection attacks bypassing WAFs and identifying attack patterns.@en

Web application firewalls (WAF) are an essential protection mechanism for online software systems. Because of the relentless flow of new kinds of attacks as well as their increased sophistication, WAFs have to be updated and tested regularly to prevent attackers from easily circumventing them. In this paper, we focus on testing WAFs for SQL injection attacks, but the general principles and strategy we propose can be adapted to other contexts. We present ML-Driven, an approach based on machine learning and an evolutionary algorithm to automatically detect holes in WAFs that let SQL injection attacks bypass them. Initially, ML-Driven automatically generates a diverse set of attacks and submit them to the system being protected by the target WAF. Then, ML-Driven selects attacks that exhibit patterns (substrings) associated with bypassing the WAF and evolve them to generate new successful bypassing attacks. Machine learning is used to incrementally learn attack patterns from previously generated attacks according to their testing results, i.e., if they are blocked or bypass the WAF. We implemented ML-Driven in a tool and evaluated it on ModSecurity, a widely used open-source WAF, and a proprietary WAF protecting a financial institution. Our empirical results indicate that ML-Driven is effective and efficient at generating SQL injection attacks bypassing WAFs and identifying attack patterns.@en