Abstract-Memory corruption vulnerabilities are an everpresent risk in software, which attackers can exploit to obtain unauthorized access to confidential information. As products with access to sensitive data are becoming more prevalent, the number of potentially exploitable systems is also increasing, resulting in a greater need for automated software vetting tools. DARPA recently funded a competition, with millions of dollars in prize money, to further research focusing on automated vulnerability finding and patching, showing the importance of research in this area. Current techniques for finding potential bugs include static, dynamic, and concolic analysis systems, which each having their own advantages and disadvantages. A common limitation of systems designed to create inputs which trigger vulnerabilities is that they only find shallow bugs and struggle to exercise deeper paths in executables.We present Driller, a hybrid vulnerability excavation tool which leverages fuzzing and selective concolic execution in a complementary manner, to find deeper bugs. Inexpensive fuzzing is used to exercise compartments of an application, while concolic execution is used to generate inputs which satisfy the complex checks separating the compartments. By combining the strengths of the two techniques, we mitigate their weaknesses, avoiding the path explosion inherent in concolic analysis and the incompleteness of fuzzing. Driller uses selective concolic execution to explore only the paths deemed interesting by the fuzzer and to generate inputs for conditions that the fuzzer cannot satisfy. We evaluate Driller on 126 applications released in the qualifying event of the DARPA Cyber Grand Challenge and show its efficacy by identifying the same number of vulnerabilities, in the same time, as the top-scoring team of the qualifying event.

Social networking has become a popular way for users to meet and interact online. Users spend a significant amount of time on popular social network platforms (such as Facebook, MySpace, or Twitter), storing and sharing a wealth of personal information. This information, as well as the possibility of contacting thousands of users, also attracts the interest of cybercriminals. For example, cybercriminals might exploit the implicit trust relationships between users in order to lure victims to malicious websites. As another example, cybercriminals might find personal information valuable for identity theft or to drive targeted spam campaigns.In this paper, we analyze to which extent spam has entered social networks. More precisely, we analyze how spammers who target social networking sites operate. To collect the data about spamming activity, we created a large and diverse set of "honey-profiles" on three large social networking sites, and logged the kind of contacts and messages that they received. We then analyzed the collected data and identified anomalous behavior of users who contacted our profiles. Based on the analysis of this behavior, we developed techniques to detect spammers in social networks, and we aggregated their messages in large spam campaigns. Our results show that it is possible to automatically identify the accounts used by spammers, and our analysis was used for take-down efforts in a real-world social network. More precisely, during this study, we collaborated with Twitter and correctly detected and deleted 15,857 spam profiles.

Anti-virus vendors are confronted with a multitude of potentially malicious samples today. Receiving thousands of new samples every day is not uncommon. The signatures that detect confirmed malicious threats are mainly still created manually, so it is important to discriminate between samples that pose a new unknown threat and those that are mere variants of known malware. This survey article provides an overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples. It also covers analysis programs that leverage these It also covers analysis programs that employ these techniques to assist human analysts in assessing, in a timely and appropriate manner, whether a given sample deserves closer manual inspection due to its unknown malicious behavior.

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Finding and exploiting vulnerabilities in binary code is a challenging task. The lack of high-level, semantically rich information about data structures and control constructs makes the analysis of program properties harder to scale. However, the importance of binary analysis is on the rise. In many situations binary analysis is the only possible way to prove (or disprove) properties about the code that is actually executed. In this paper, we present a binary analysis framework that implements a number of analysis techniques that have been proposed in the past. We present a systematized implementation of these techniques, which allows other researchers to compose them and develop new approaches. In addition, the implementation of these techniques in a unifying framework allows for the direct comparison of these approaches and the identification of their advantages and disadvantages. The evaluation included in this paper is performed using a recent dataset created by DARPA for evaluating the effectiveness of binary vulnerability analysis techniques. Our framework has been open-sourced and is available to the security community.

Developers use cryptographic APIs in Android with the intent of securing data such as passwords and personal information on mobile devices. In this paper, we ask whether developers use the cryptographic APIs in a fashion that provides typical cryptographic notions of security, e.g., IND-CPA security. We develop program analysis techniques to automatically check programs on the Google Play marketplace, and find that 10,327 out of 11,748 applications that use cryptographic APIs -88% overall -make at least one mistake. These numbers show that applications do not use cryptographic APIs in a fashion that maximizes overall security. We then suggest specific remediations based on our analysis towards improving overall cryptographic security in Android applications.

The web has become an essential part of our society and is currently the main medium of information delivery. Billions of users browse the web on a daily basis, and there are single websites that have reached over one billion user accounts. In this environment, the ability to track users and their online habits can be very lucrative for advertising companies, yet very intrusive for the privacy of users.In this paper, we examine how web-based device fingerprinting currently works on the Internet. By analyzing the code of three popular browser-fingerprinting code providers, we reveal the techniques that allow websites to track users without the need of client-side identifiers. Among these techniques, we show how current commercial fingerprinting approaches use questionable practices, such as the circumvention of HTTP proxies to discover a user's real IP address and the installation of intrusive browser plugins.At the same time, we show how fragile the browser ecosystem is against fingerprinting through the use of novel browseridentifying techniques. With so many different vendors involved in browser development, we demonstrate how one can use diversions in the browsers' implementation to distinguish successfully not only the browser-family, but also specific major and minor versions. Browser extensions that help users spoof the user-agent of their browsers are also evaluated. We show that current commercial approaches can bypass the extensions, and, in addition, take advantage of their shortcomings by using them as additional fingerprinting features.

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