Biblio

The ubiquitously-installed Java Runtime Environment (JRE) provides a complex, flexible set of mechanisms that support the execution of untrusted code inside a secure sandbox. However, many recent exploits have successfully escaped the sandbox, allowing attackers to infect numerous Java hosts. We hypothesize that the Java security model affords developers more flexibility than they need or use in practice, and thus its complexity compromises security without improving practical functionality. We describe an empirical study of the ways benign open-source Java applications use and interact with the Java security manager. We found that developers regularly misunderstand or misuse Java security mechanisms, that benign programs do not use all of the vast flexibility afforded by the Java security model, and that there are clear differences between the ways benign and exploit programs interact with the security manager. We validate these results by deriving two restrictions on application behavior that restrict (1) security manager modifications and (2) privilege escalation. We demonstrate that enforcing these rules at runtime stop a representative proportion of modern Java 7 exploits without breaking backwards compatibility with benign applications. These practical rules should be enforced in the JRE to fortify the Java sandbox.

Today's social-coding tools foreshadow a transformation of the software industry, as it relies increasingly on open libraries, frameworks, and code fragments. Our vision calls for new intelligently transparent services that support rapid development of innovative products while helping developers manage risk and issuing them early warnings of looming failures. Intelligent transparency is enabled by an infrastructure that applies analytics to data from all phases of the life cycle of open source projects, from development to deployment. Such an infrastructure brings stakeholders the information they need when they need it.

A self-managing software system should be able to monitor and analyze its runtime behavior and make adaptation decisions accordingly to meet certain desirable objectives. Traditional software adaptation techniques and recent “models@runtime” approaches usually require an a priori model for a system’s dynamic behavior. Oftentimes the model is difficult to define and labor-intensive to maintain, and tends to get out of date due to adaptation and architecture decay. We propose an alternative approach that does not require defining the system’s behavior model beforehand, but instead involves mining software component interactions from system execution traces to build a probabilistic usage model, which is in turn used to analyze, plan, and execute adaptations. In this article, we demonstrate how such an approach can be realized and effectively used to address a variety of adaptation concerns. In particular, we describe the details of one application of this approach for safely applying dynamic changes to a running software system without creating inconsistencies. We also provide an overview of two other applications of the approach, identifying potentially malicious (abnormal) behavior for self-protection, and improving deployment of software components in a distributed setting for performance self-optimization. Finally, we report on our experiments with engineering self-management features in an emergency deployment system using the proposed mining approach.

Sandboxes are increasingly important building materials for secure software systems. In recognition of their potential to improve the security posture of many systems at various points in the development lifecycle, researchers have spent the last several decades developing, improving, and evaluating sandboxing techniques. What has been done in this space? Where are the barriers to advancement? What are the gaps in these efforts? We systematically analyze a decade of sandbox research from five top-tier security and systems conferences using qualitative content analysis, statistical clustering, and graph-based metrics to answer these questions and more. We find that the term “sandbox” currently has no widely accepted or acceptable definition. We use our broad scope to propose the first concise and comprehensive definition for “sandbox” that consistently encompasses research sandboxes. We learn that the sandboxing landscape covers a range of deployment options and policy enforcement techniques collectively capable of defending diverse sets of components while mitigating a wide range of vulnerabilities. Researchers consistently make security, performance, and applicability claims about their sandboxes and tend to narrowly define the claims to ensure they can be evaluated. Those claims are validated using multi-faceted strategies spanning proof, analytical analysis, benchmark suites, case studies, and argumentation. However, we find two cases for improvement: (1) the arguments researchers present are often ad hoc and (2) sandbox usability is mostly uncharted territory. We propose ways to structure arguments to ensure they fully support their corresponding claims and suggest lightweight means of evaluating sandbox usability.

Designing secure cyber-physical systems (CPS) is a particularly difficult task since security vulnerabilities stem not only from traditional cybersecurity concerns, but also physical ones. Many of the standard methods for CPS design make strong and unverified assumptions about the trustworthiness of physical devices, such as sensors. When these assumptions are violated, subtle inter-domain vulnerabilities are introduced into the system model. In this paper we use formal specification of analysis contracts to expose security assumptions and guarantees of analyses from reliability, control, and sensor security domains. We show that this specification allows us to determine where these assumptions are violated, opening the door to malicious attacks. We demonstrate how this approach can help discover and prevent vulnerabilities using a self-driving car example.

Android is the most popular platform for mobile devices. It facilitates sharing of data and services among applications using a rich inter-app communication system. While access to resources can be controlled by the Android permission system, enforcing permissions is not sufficient to prevent security violations, as permissions may be mismanaged, intentionally or unintentionally. Android's enforcement of the permissions is at the level of individual apps, allowing multiple malicious apps to collude and combine their permissions or to trick vulnerable apps to perform actions on their behalf that are beyond their individual privileges. In this paper, we present COVERT, a tool for compositional analysis of Android inter-app vulnerabilities. COVERT's analysis is modular to enable incremental analysis of applications as they are installed, updated, and removed. It statically analyzes the reverse engineered source code of each individual app, and extracts relevant security specifications in a format suitable for formal verification. Given a collection of specifications extracted in this way, a formal analysis engine (e.g., model checker) is then used to verify whether it is safe for a combination of applications-holding certain permissions and potentially interacting with each other-to be installed together. Our experience with using COVERT to examine over 500 real-world apps corroborates its ability to find inter-app vulnerabilities in bundles of some of the most popular apps on the market.

Pervasiveness of smartphones and the vast number of corresponding apps have underlined the need for applicable automated software testing techniques. A wealth of research has been focused on either unit or GUI testing of smartphone apps, but little on automated support for end-to-end system testing. This paper presents SIG-Droid, a framework for system testing of Android apps, backed with automated program analysis to extract app models and symbolic execution of source code guided by such models for obtaining test inputs that ensure covering each reachable branch in the program. SIG-Droid leverages two automatically extracted models: Interface Model and Behavior Model. The Interface Model is used to find values that an app can receive through its interfaces. Those values are then exchanged with symbolic values to deal with constraints with the help of a symbolic execution engine. The Behavior Model is used to drive the apps for symbolic execution and generate sequences of events. We provide an efficient implementation of SIG-Droid based in part on Symbolic PathFinder, extended in this work to support automatic testing of Android apps. Our experiments show SIG-Droid is able to achieve significantly higher code coverage than existing automated testing tools targeted for Android.

The ubiquitously-installed Java Runtime Environment (JRE) provides a complex, flexible set of mechanisms that support the execution of untrusted code inside a secure sandbox. However, many recent exploits have successfully escaped the sandbox, allowing attackers to infect numerous Java hosts. We hypothesize that the Java security model affords developers more flexibility than they need or use in practice, and thus its complexity compromises security without improving practical functionality. We describe an empirical study of the ways benign open-source Java applications use and interact with the Java security manager. We found that developers regularly misunderstand or misuse Java security mechanisms, that benign programs do not use all of the vast flexibility afforded by the Java security model, and that there are clear differences between the ways benign and exploit programs interact with the security manager. We validate these results by deriving two restrictions on application behavior that restrict (1) security manager modifications and (2) privilege escalation. We demonstrate that enforcing these rules at runtime stop a representative proportion of modern Java 7 exploits without breaking backwards compatibility with benign applications. These practical rules should be enforced in the JRE to fortify the Java sandbox.

The overarching vision of social machines is to facilitate social processes by having computers provide administrative support. We conceive of a social machine as a sociotechnical system (STS): a software-supported system in which autonomous principals such as humans and organizations interact to exchange information and services.  Existing approaches for social machines emphasize the technical aspects and inadequately support the meanings of social processes, leaving them informally realized in human interactions. We posit that a fundamental rethinking is needed to incorporate accountability, essential for addressing the openness of the Web and the autonomy of its principals.

We introduce Interaction-Oriented Software Engineering (IOSE) as a paradigm expressly suited to capturing the social basis of STSs. Motivated by promoting openness and autonomy, IOSE focuses not on implementation but on social protocols, specifying how social relationships, characterizing the accountability of the concerned parties, progress as they interact.  Motivated by providing computational support, IOSE adopts the accountability representation to capture the meaning of a social machine's states and transitions.

We demonstrate IOSE via examples drawn from healthcare.  We reinterpret the classical software engineering (SE) principles for the STS setting and show how IOSE is better suited than traditional software engineering for supporting social processes.  The contribution of this paper is a new paradigm for STSs, evaluated via conceptual analysis.