No abstract

Web applications are ubiquitous, perform missioncritical tasks, and handle sensitive user data. Unfortunately, web applications are often implemented by developers with limited security skills, and, as a result, they contain vulnerabilities. Most of these vulnerabilities stem from the lack of input validation. That is, web applications use malicious input as part of a sensitive operation, without having properly checked or sanitized the input values prior to their use.Past research on vulnerability analysis has mostly focused on identifying cases in which a web application directly uses external input in critical operations. However, little research has been performed to analyze the correctness of the sanitization process. Thus, whenever a web application applies some sanitization routine to potentially malicious input, the vulnerability analysis assumes that the result is innocuous. Unfortunately, this might not be the case, as the sanitization process itself could be incorrect or incomplete.In this paper, we present a novel approach to the analysis of the sanitization process. More precisely, we combine static and dynamic analysis techniques to identify faulty sanitization procedures that can be bypassed by an attacker. We implemented our approach in a tool, called Saner, and we applied it to a number of real-world applications. Our results demonstrate that we were able to identify several novel vulnerabilities that stem from erroneous sanitization procedures.

Abstract. Black-box web vulnerability scanners are a class of tools that can be used to identify security issues in web applications. These tools are often marketed as "point-and-click pentesting" tools that automatically evaluate the security of web applications with little or no human support. These tools access a web application in the same way users do, and, therefore, have the advantage of being independent of the particular technology used to implement the web application. However, these tools need to be able to access and test the application's various components, which are often hidden behind forms, JavaScript-generated links, and Flash applications. This paper presents an evaluation of eleven black-box web vulnerability scanners, both commercial and open-source. The evaluation composes different types of vulnerabilities with different challenges to the crawling capabilities of the tools. These tests are integrated in a realistic web application. The results of the evaluation show that crawling is a task that is as critical and challenging to the overall ability to detect vulnerabilities as the vulnerability detection techniques themselves, and that many classes of vulnerabilities are completely overlooked by these tools, and thus research is required to improve the automated detection of these flaws.

Abstract. We present an automata-based approach for the verification of string operations in PHP programs based on symbolic string analysis. String analysis is a static analysis technique that determines the values that a string expression can take during program execution at a given program point. This information can be used to verify that string values are sanitized properly and to detect programming errors and security vulnerabilities. In our string analysis approach, we encode the set of string values that string variables can take as automata. We implement all string functions using a symbolic automata representation (MBDD representation from the MONA automata package) and leverage efficient manipulations on MBDDs, e.g., determinization and minimization. Particularly, we propose a novel algorithm for language-based replacement. Our replacement function takes three DFAs as arguments and outputs a DFA. Finally, we apply a widening operator defined on automata to approximate fixpoint computations. If this conservative approximation does not include any bad patterns (specified as regular expressions), we conclude that the program does not contain any errors or vulnerabilities. Our experimental results demonstrate that our approach works quite well in checking the correctness of sanitization operations in real-world PHP applications.

In recent years, web applications have become tremendously popular, and nowadays they are routinely used in security-critical environments, such as medical, financial, and military systems. As the use of web applications for critical services has increased, the number and sophistication of attacks against these applications have grown as well. Most approaches to the detection of web-based attacks analyze the interaction of a web application with its clients and back-end servers. Even though these approaches can effectively detect and block a number of attacks, there are attacks that cannot be detected only by looking at the external behavior of a web application.In this paper, we present Swaddler, a novel approach to the anomaly-based detection of attacks against web applications. Swaddler analyzes the internal state of a web application and learns the relationships between the application's critical execution points and the application's internal state. By doing this, Swaddler is able to identify attacks that attempt to bring an application in an inconsistent, anomalous state, such as violations of the intended workflow of a web application. We developed a prototype of our approach for the PHP language and we evaluated it with respect to several real-world applications.

Malicious web pages that use drive-by download attacks or social engineering techniques to install unwanted software on a user's computer have become the main avenue for the propagation of malicious code. To search for malicious web pages, the first step is typically to use a crawler to collect URLs that are live on the Internet. Then, fast prefiltering techniques are employed to reduce the amount of pages that need to be examined by more precise, but slower, analysis tools (such as honeyclients). While effective, these techniques require a substantial amount of resources. A key reason is that the crawler encounters many pages on the web that are benign, that is, the "toxicity" of the stream of URLs being analyzed is low.In this paper, we present EVILSEED, an approach to search the web more efficiently for pages that are likely malicious. EVILSEED starts from an initial seed of known, malicious web pages. Using this seed, our system automatically generates search engines queries to identify other malicious pages that are similar or related to the ones in the initial seed. By doing so, EVILSEED leverages the crawling infrastructure of search engines to retrieve URLs that are much more likely to be malicious than a random page on the web. In other words EVILSEED increases the "toxicity" of the input URL stream. Also, we envision that the features that EVILSEED presents could be directly applied by search engines in their prefilters. We have implemented our approach, and we evaluated it on a large-scale dataset. The results show that EVILSEED is able to identify malicious web pages more efficiently when compared to crawler-based approaches.

Abstract. Fuzzing is a well-known black-box approach to the security testing of applications. Fuzzing has many advantages in terms of simplicity and effectiveness over more complex, expensive testing approaches. Unfortunately, current fuzzing tools suffer from a number of limitations, and, in particular, they provide little support for the fuzzing of stateful protocols.In this paper, we present SNOOZE, a tool for building flexible, securityoriented, network protocol fuzzers. SNOOZE implements a stateful fuzzing approach that can be used to effectively identify security flaws in network protocol implementations. SNOOZE allows a tester to describe the stateful operation of a protocol and the messages that need to be generated in each state. In addition, SNOOZE provides attack-specific fuzzing primitives that allow a tester to focus on specific vulnerability classes. We used an initial prototype of the SNOOZE tool to test programs that implement the SIP protocol, with promising results. SNOOZE supported the creation of sophisticated fuzzing scenarios that were able to expose realworld bugs in the programs analyzed.

In recent years, web applications have become tremendously popular, and nowadays they are routinely used in security-critical environments, such as medical, financial, and military systems. As the use of web applications for critical services has increased, the number and sophistication of attacks against these applications have grown as well. Current approaches to securing web applications focus either on detecting and blocking web-based attacks using application-level firewalls, or on using vulnerability analysis techniques to identify security problems before deployment.The vulnerability analysis of web applications is made difficult by a number of factors, such as the use of scripting languages, the structuring of the application logic into separate pages and code modules, and the interaction with back-end databases. So far, approaches to web application vulnerability analysis have focused on single application modules to identify insecure uses of information provided as input to the application. Unfortunately, these approaches are limited in scope, and, therefore, they cannot detect multi-step attacks that exploit the interaction among multiple modules of an application.We have developed a novel vulnerability analysis approach that characterizes both the extended state and the intended workflow of a web application. By doing this, our analysis approach is able to take into account inter-module relationships as well as the interaction of an application's modules with back-end databases. As a result, our vulnerability analysis technique is able to identify sophisticated multi-step attacks against the application's workflow that were not addressed by previous approaches. We implemented our technique in a prototype tool, called MiMoSA, and tested it on several applications, identifying both known and new vulnerabilities.