Program debloating aims to enhance the performance and reduce the attack surface of bloated applications. Several techniques have been recently proposed to specialize programs. These approaches are either based on unsound strategies or demanding techniques, leading to unsafe results or a high-overhead debloating process. In this paper, we address these limitations by applying partial-evaluation principles to generate specialized applications. Our approach relies on a simple observation that an application typically consists of configuration logic, followed by the main logic of the program. The configuration logic specifies what functionality in the main logic should be executed. LMCAS performs partial interpretation to capture a precise program state of the configuration logic based on the supplied inputs. LMCAS then applies partial-evaluation optimizations to generate a specialized program by propagating the constants in the captured partial state, eliminating unwanted code, and preserving the desired functionalities. Our evaluation of LMCAS-on commonly used benchmarks and real-world applications-shows that it successfully removes unwanted features while preserving the functionality and robustness of the debloated programs, runs faster than prior tools, and reduces the attack surface of specialized programs. LMCAS runs 1500x, 4.6x, and 1.2x faster than the state-of-the-art debloating tools CHISEL, RAZOR, and OCCAM, respectively; achieves 25% reduction in the binary size; demonstrates favorable gadgets elimination trade-off; and eliminates 87.5% of the known CVE vulnerabilities in our test corpus.

Wide adoption of artificial neural networks in various domains has led to an increasing interest in defending adversarial attacks against them. Preprocessing defense methods such as pixel discretization are particularly attractive in practice due to their simplicity, low computational overhead, and applicability to various systems. It is observed that such methods work well on simple datasets like MNIST, but break on more complicated ones like ImageNet under recently proposed strong white-box attacks. To understand the conditions for success and potentials for improvement, we study the pixel discretization defense method, including more sophisticated variants that take into account the properties of the dataset being discretized. Our results again show poor resistance against the strong attacks. We analyze our results in a theoretical framework and offer strong evidence that pixel discretization is unlikely to work on all but the simplest of the datasets. Furthermore, our arguments present insights why some other preprocessing defenses may be insecure.

An emerging problem in trustworthy machine learning is to train models that produce robust interpretations for their predictions. We take a step towards solving this problem through the lens of axiomatic attribution of neural networks. Our theory is grounded in the recent work, Integrated Gradients (IG) [STY17], in axiomatically attributing a neural network’s output change to its input change. We propose training objectives in classic robust optimization models to achieve robust IG attributions. Our objectives give principled generalizations of previous objectives designed for robust predictions, and they naturally degenerate to classic soft-margin training for one-layer neural networks. We also generalize previous theory and prove that the objectives for different robust optimization models are closely related. Experiments demonstrate the effectiveness of our method, and also point to intriguing problems which hint at the need for better optimization techniques or better neural network architectures for robust attribution training.

We address the problem of discovering communication links between applications in the popular Android mobile operating system, an important problem for security and privacy in Android. Any scalable static analysis in this complex setting is bound to produce an excessive amount of false-positives, rendering it impractical. To improve precision, we propose to augment static analysis with a trained neural-network model that estimates the probability that a communication link truly exists. We describe a neural-network architecture that encodes abstractions of communicating objects in two applications and estimates the probability with which a link indeed exists. At the heart of our architecture are type-directed encoders (TDE), a general framework for elegantly constructing encoders of a compound data type by recursively composing encoders for its constituent types. We evaluate our approach on a large corpus of Android applications, and demonstrate that it achieves very high accuracy. Further, we conduct thorough interpretability studies to understand the internals of the learned neural networks.

Mobile users are increasingly becoming targets of malware infections and scams. In order to curb such attacks it is important to know how these attacks originate. We take a previously unexplored step in this direction. Numerous in-app advertisements work at this interface: when the user taps on the advertisement, she is led to a web page which may further redirect until the user reaches the final destination. Even though the original applications may not be malicious, the Web destinations that the user visits could play an important role in propagating attacks. We develop a systematic static analysis methodology to find ad libraries embed in applications and dynamic analysis methodology consisting of three components related to triggering web links, detecting malware and scam campaigns, and determining the provenance of such campaigns reaching the user. Our static analysis system identified 242 different ad libraries and dynamic analysis system was deployed for a two-month period and analyzed over 600,000 applications while triggering a total of about 1.5 million links in applications to the Web. We gain a general understanding of attacks through the app-web interface and make several interesting findings including a rogue antivirus scam, free iPad scams, and advertisements propagating SMS trojans.

Application containers, such as those provided by Docker, have recently gained popularity as a solution for agile and seamless software deployment. These light-weight virtualization environments run applications that are packed together with their resources and configuration information, and thus can be deployed across various software platforms. Unfortunately, the ease with which containers can be created is oftentimes a double-edged sword, encouraging the packaging of logically distinct applications, and the inclusion of significant amount of unnecessary components, within a single container. These practices needlessly increase the container size-sometimes by orders of magnitude. They also decrease the overall security, as each included component-necessary or not-may bring in security issues of its own, and there is no isolation between multiple applications packaged within the same container image. We propose algorithms and a tool called Cimplifier, which address these concerns: given a container and simple user-defined constraints, our tool partitions it into simpler containers, which (i) are isolated from each other, only communicating as necessary, and (ii) only include enough resources to perform their functionality. Our evaluation on real-world containers demonstrates that Cimplifier preserves the original functionality, leads to reduction in image size of up to 95%, and processes even large containers in under thirty seconds.

Secure Sockets Layer (SSL) and Transport Layer Security (TLS) protocols have become the security backbone of the Web and Internet today. Many systems including mobile and desktop applications are protected by SSL/TLS protocols against network attacks. However, many vulnerabilities caused by incorrect use of SSL/TLS APIs have been uncovered in recent years. Such vulnerabilities, many of which are caused due to poor API design and inexperience of application developers, often lead to confidential data leakage or man-in-the-middle attacks. In this paper, to guarantee code quality and logic correctness of SSL/TLS applications, we design and implement SSLINT, a scalable, automated, static analysis system for detecting incorrect use of SSL/TLS APIs. SSLINT is capable of performing automatic logic verification with high efficiency and good accuracy. To demonstrate it, we apply SSLINT to one of the most popular Linux distributions -- Ubuntu. We find 27 previously unknown SSL/TLS vulnerabilities in Ubuntu applications, most of which are also distributed with other Linux distributions.

The booming popularity of smartphones is partly a result of application markets where users can easily download wide range of third-party applications. However, due to the open nature of markets, especially on Android, there have been several privacy and security concerns with these applications. On Google Play, as with most other markets, users have direct access to natural-language descriptions of those applications, which give an intuitive idea of the functionality including the security-related information of those applications. Google Play also provides the permissions requested by applications to access security and privacy-sensitive APIs on the devices. Users may use such a list to evaluate the risks of using these applications. To best assist the end users, the descriptions should reflect the need for permissions, which we term description-to-permission fidelity. In this paper, we present a system AutoCog to automatically assess description-to-permission fidelity of applications. AutoCog employs state-of-the-art techniques in natural language processing and our own learning-based algorithm to relate description with permissions. In our evaluation, AutoCog outperforms other related work on both performance of detection and ability of generalization over various permissions by a large extent. On an evaluation of eleven permissions, we achieve an average precision of 92.6% and an average recall of 92.0%. Our large-scale measurements over 45,811 applications demonstrate the severity of the problem of low description-to-permission fidelity. AutoCog helps bridge the long-lasting usability gap between security techniques and average users.

Mobile malware threats (e.g., on Android) have recently become a real concern. In this paper, we evaluate the state-of-the-art commercial mobile anti-malware products for Android and test how resistant they are against various common obfuscation techniques (even with known malware). Such an evaluation is important for not only measuring the available defense against mobile malware threats, but also proposing effective, next-generation solutions. We developed DroidChameleon, a systematic framework with various transformation techniques, and used it for our study. Our results on 10 popular commercial anti-malware applications for Android are worrisome: none of these tools is resistant against common malware transformation techniques. In addition, a majority of them can be trivially defeated by applying slight transformation over known malware with little effort for malware authors. Finally, in light of our results, we propose possible remedies for improving the current state of malware detection on mobile devices.

Mobile malware threats have recently become a real concern. In this paper, we evaluate the state-of-the-art commercial mobile antimalware products for Android and test how resistant they are against various common obfuscation techniques (even with known malware). Such an evaluation is important for not only measuring the available defense against mobile malware threats but also proposing effective, next-generation solutions. We developed DroidChameleon, a systematic framework with various transformation techniques, and used it for our study. Our results on ten popular commercial anti-malware applications for Android are worrisome: none of these tools is resistant against common malware transformation techniques. Moreover, the transformations are simple in most cases and anti-malware tools make little effort to provide transformation-resilient detection. Finally, in the light of our results, we propose possible remedies for improving the current state of malware detection on mobile devices.

Today's smartphone application markets host an ever increasing number of applications. The sheer number of applications makes their review a daunting task. We propose AppsPlayground for Android, a framework that automates the analysis smartphone applications. AppsPlayground integrates multiple components comprising different detection and automatic exploration techniques for this purpose. We evaluated the system using multiple large scale and small scale experiments involving real benign and malicious applications. Our evaluation shows that AppsPlayground is quite effective at automatically detecting privacy leaks and malicious functionality in applications.

With the advent of Web 2.0, web developers have designed multiple additions to break SOP boundary, such as splitting and combining traditional web browser protection boundaries (security principals). However, these newly generated principals lack a new label to represent its security property. To address the inconsistent label problem, this paper proposes a new way to define a security principal and its labels in the browser. In particular, we propose a Configurable Origin Policy (COP), in which a browser's security principal is defined by a configurable ID rather than a fixed triple. The server-side and client-side code of a web application can create, join, and destroy its own principals. We perform a formal security analysis on COP to ensure session integrity. Then we also show that COP is compatible with legacy web sites, and those sites utilizing COP are also compatible with legacy browsers.

Third party JavaScripts not only offer much richer features to the web and its applications but also introduce new threats. These scripts cannot be completely trusted and executed with the privileges given to host web sites. Due to incomplete virtualization and lack of tracking all the data flows, all existing approaches without native sandbox support can secure only a subset of third party JavaScripts, and they are vulnerable to attacks encoded in non-standard HTML/-JavaScript (browser quirks) as these approaches will parse third party JavaScripts independently at server side without considering client-side non-standard parsing quirks. At the same time, native sandboxes are vulnerable to attacks based on unknown native JavaScript engine bugs.

Third-party JavaScript offers much more diversity to Web and its applications but also introduces new threats. Those scripts cannot be completely trusted and executed with the privileges given to host web sites. Due to incomplete virtualization and lack of tracking all the data flows, all the existing works in this area can secure only a subset of third-party JavaScript. At the same time, because of the existence of not so well documented browser quirks, attacks may be encoded in non standard HTML/JavaScript so that they can bypass existing approaches as these approaches will parse third-party JavaScript twice, at both server and client side.

The ease of deployment and the self configurable aspect of wireless mesh networks has generated lot of interest in them during the recent years. The one laptop per child (OLPC) project uses mesh networking, closely adhering to IEEE 802.11s WLAN mesh standard, to provide networking in a classroom environment. Our work measures and analyzes the performance of OLPC mesh networks. It includes several experiments concerning performance of UDP and TCP across a range of network topologies. Our experiments provide useful information about several aspects of these networks, including connectivity range, effect of RTS/CTS and Tx/Rx capabilities.