Input Sanitization Research Articles

Web Application Vulnerability Prediction Using Hybrid Program Analysis and Machine Learning

Due to limited time and resources, web software engineers need support in identifying vulnerable code. A practical approach to predicting vulnerable code would enable them to prioritize security auditing efforts. In this paper, we propose using a set of hybrid (static+dynamic) code attributes that characterize input validation and input sanitization code patterns and are expected to be significant indicators of web application vulnerabilities. Because static and dynamic program analyses complement each other, both techniques are used to extract the proposed attributes in an accurate and scalable way. Current vulnerability prediction techniques rely on the availability of data labeled with vulnerability information for training. For many real world applications, past vulnerability data is often not available or at least not complete. Hence, to address both situations where labeled past data is fully available or not, we apply both supervised and semi-supervised learning when building vulnerability predictors based on hybrid code attributes. Given that semi-supervised learning is entirely unexplored in this domain, we describe how to use this learning scheme effectively for vulnerability prediction. We performed empirical case studies on seven open source projects where we built and evaluated supervised and semi-supervised models. When cross validated with fully available labeled data, the supervised models achieve an average of 77 percent recall and 5 percent probability of false alarm for predicting SQL injection, cross site scripting, remote code execution and file inclusion vulnerabilities. With a low amount of labeled data, when compared to the supervised model, the semi-supervised model showed an average improvement of 24 percent higher recall and 3 percent lower probability of false alarm, thus suggesting semi-supervised learning may be a preferable solution for many real world applications where vulnerability data is missing.

Data-Parallel String-Manipulating Programs

String-manipulating programs are an important class of programs with applications in malware detection, graphics, input sanitization for Web security, and large-scale HTML processing. This paper extends prior work on BEK, an expressive domain-specific language for writing string-manipulating programs, with algorithmic insights that make BEK both analyzable and data-parallel. By analyzable we mean that unlike most general purpose programming languages, many algebraic properties of a BEK program are decidable (i.e., one can check whether two programs commute or compute the inverse of a program). By data-parallel we mean that a BEK program can compute on arbitrary subsections of its input in parallel, thus exploiting parallel hardware. This latter requirement is particularly important for programs which operate on large data: without data parallelism, a programmer cannot hide the latency of reading data from various storage media (i.e., reading a terabyte of data from a modern hard drive takes about 3 hours). With a data-parallel approach, the system can split data across multiple disks and thus hide the latency of reading the data. A BEK program is expressive: a programmer can use conditionals, switch statements, and registers--or local variables--in order to implement common string-manipulating programs. Unfortunately, this expressivity induces data dependencies, which are an obstacle to parallelism. The key contribution of this paper is an algorithm which automatically removes these data dependencies by mapping a B EK program into a intermediate format consisting of symbolic transducers, which extend classical transducers with symbolic predicates and symbolic assignments. We present a novel algorithm that we call exploration which performs symbolic loop unrolling of these transducers to obtain simplified versions of the original program. We show how these simplified versions can then be lifted to a stateless form, and from there compiled to data-parallel hardware. To evaluate the efficacy of our approach, we demonstrate up to 8x speedups for a number of real-world, BEK programs, (e.g., HTML encoder and decoder) on data-parallel hardware. To the best of our knowledge, these are the first data parallel implementation of these programs. To validate that our approach is correct, we use an automatic testing technique to compare our generated code to the original implementations and find no semantic deviations.

Predicting SQL injection and cross site scripting vulnerabilities through mining input sanitization patterns

ContextSQL injection (SQLI) and cross site scripting (XSS) are the two most common and serious web application vulnerabilities for the past decade. To mitigate these two security threats, many vulnerability detection approaches based on static and dynamic taint analysis techniques have been proposed. Alternatively, there are also vulnerability prediction approaches based on machine learning techniques, which showed that static code attributes such as code complexity measures are cheap and useful predictors. However, current prediction approaches target general vulnerabilities. And most of these approaches locate vulnerable code only at software component or file levels. Some approaches also involve process attributes that are often difficult to measure. ObjectiveThis paper aims to provide an alternative or complementary solution to existing taint analyzers by proposing static code attributes that can be used to predict specific program statements, rather than software components, which are likely to be vulnerable to SQLI or XSS. MethodFrom the observations of input sanitization code that are commonly implemented in web applications to avoid SQLI and XSS vulnerabilities, in this paper, we propose a set of static code attributes that characterize such code patterns. We then build vulnerability prediction models from the historical information that reflect proposed static attributes and known vulnerability data to predict SQLI and XSS vulnerabilities. ResultsWe developed a prototype tool called PhpMinerI for data collection and used it to evaluate our models on eight open source web applications. Our best model achieved an averaged result of 93% recall and 11% false alarm rate in predicting SQLI vulnerabilities, and 78% recall and 6% false alarm rate in predicting XSS vulnerabilities. ConclusionThe experiment results show that our proposed vulnerability predictors are useful and effective at predicting SQLI and XSS vulnerabilities.

Structural Learning of Attack Vectors for Generating Mutated XSS Attacks

Web applications suffer from cross-site scripting (XSS) attacks that resulting from incomplete or incorrect input sanitization. Learning the structure of attack vectors could enrich the variety of manifestations in generated XSS attacks. In this study, we focus on generating more threatening XSS attacks for the state-of-the-art detection approaches that can find potential XSS vulnerabilities in Web applications, and propose a mechanism for structural learning of attack vectors with the aim of generating mutated XSS attacks in a fully automatic way. Mutated XSS attack generation depends on the analysis of attack vectors and the structural learning mechanism. For the kernel of the learning mechanism, we use a Hidden Markov model (HMM) as the structure of the attack vector model to capture the implicit manner of the attack vector, and this manner is benefited from the syntax meanings that are labeled by the proposed tokenizing mechanism. Bayes theorem is used to determine the number of hidden states in the model for generalizing the structure model. The paper has the contributions as following: (1) automatically learn the structure of attack vectors from practical data analysis to modeling a structure model of attack vectors, (2) mimic the manners and the elements of attack vectors to extend the ability of testing tool for identifying XSS vulnerabilities, (3) be helpful to verify the flaws of blacklist sanitization procedures of Web applications. We evaluated the proposed mechanism by Burp Intruder with a dataset collected from public XSS archives. The results show that mutated XSS attack generation can identify potential vulnerabilities.

Sound and precise analysis of web applications for injection vulnerabilities

Web applications are popular targets of security attacks. One common type of such attacks is SQL injection, where an attacker exploits faulty application code to execute maliciously crafted database queries. Bothstatic and dynamic approaches have been proposed to detect or prevent SQL injections; while dynamic approaches provide protection for deployed software, static approaches can detect potential vulnerabilities before software deployment. Previous static approaches are mostly based on tainted information flow tracking and have at least some of the following limitations: (1) they do not model the precise semantics of input sanitization routines; (2) they require manually written specifications, either for each query or for bug patterns; or (3) they are not fully automated and may require user intervention at various points in the analysis. In this paper, we address these limitations by proposing a precise, sound , and fully automated analysis technique for SQL injection. Our technique avoids the need for specifications by consideringas attacks those queries for which user input changes the intended syntactic structure of the generated query. It checks conformance to this policy byconservatively characterizing the values a string variable may assume with a context free grammar, tracking the nonterminals that represent user-modifiable data, and modeling string operations precisely as language transducers. We have implemented the proposed technique for PHP, the most widely-used web scripting language. Our tool successfully discovered previously unknown and sometimes subtle vulnerabilities in real-world programs, has a low false positive rate, and scales to large programs (with approx. 100K loc).