Low-power, single-purpose embedded devices (e.g., routers and IoT devices) have become ubiquitous. While they automate and simplify many aspects of users' lives, recent large-scale attacks have shown that their sheer number poses a severe threat to the Internet infrastructure. Unfortunately, the software on these systems is hardware-dependent, and typically executes in unique, minimal environments with non-standard configurations, making security analysis particularly challenging. Many of the existing devices implement their functionality through the use of multiple binaries. This multi-binary service implementation renders current static and dynamic analysis techniques either ineffective or inefficient, as they are unable to identify and adequately model the communication between the various executables. In this paper, we present KARONTE, a static analysis approach capable of analyzing embedded-device firmware by modeling and tracking multi-binary interactions. Our approach propagates taint information between binaries to detect insecure interactions and identify vulnerabilities. We first evaluated KARONTE on 53 firmware samples from various vendors, showing that our prototype tool can successfully track and constrain multi-binary interactions. This led to the discovery of 46 zero-day bugs. Then, we performed a large-scale experiment on 899 different samples, showing that KARONTE scales well with firmware samples of different size and complexity.

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The OS kernel is an attractive target for remote attackers. If compromised, the kernel gives adversaries full system access, including the ability to install rootkits, extract sensitive information, and perform other malicious actions, all while evading detection. Most of the kernel's attack surface is situated along the system call boundary. Ongoing kernel protection efforts have focused primarily on securing this boundary; several capable analysis and fuzzing frameworks have been developed for this purpose.

Abstract. Many peer-assisted content-distribution systems reward a peer based on the amount of data that this peer serves to others. However, validating that a peer did so is, to our knowledge, an open problem; e.g., a group of colluding attackers can earn rewards by claiming to have served content to one another, when they have not. We propose a puzzle mechanism to make contribution-aware peer-assisted content distribution robust to such collusion. Our construction ties solving the puzzle to possession of specific content and, by issuing puzzle challenges simultaneously to all parties claiming to have that content, our mechanism prevents one content-holder from solving many others' puzzles. We prove (in the random oracle model) the security of our scheme, describe our integration of bandwidth puzzles into a media streaming system, and demonstrate the resulting attack resilience via simulations.

Modern mobile devices place a wide variety of sensors and services within the personal space of their users. As a result, these devices are capable of transparently monitoring many sensitive aspects of these users’ lives (e.g., location, health, or correspondences). Users typically trade access to this data for convenient applications and features, in many cases without a full appreciation of the nature and extent of the information that they are exposing to a variety of third parties. Nevertheless, studies show that users remain concerned about their privacy and vendors have similarly been increasing their utilization of privacy-preserving technologies in these devices. Still, despite significant efforts, these technologies continue to fail in fundamental ways, leaving users’ private data exposed.In this work, we survey the numerous components of mobile devices, giving particular attention to those that collect, process, or protect users’ private data. Whereas the individual components have been generally well studied and understood, examining the entire mobile device ecosystem provides significant insights into its overwhelming complexity. The numerous components of this complex ecosystem are frequently built and controlled by different parties with varying interests and incentives. Moreover, most of these parties are unknown to the typical user. The technologies that are employed to protect the users’ privacy typically only do so within a small slice of this ecosystem, abstracting away the greater complexity of the system. Our analysis suggests that this abstracted complexity is the major cause of many privacy-related vulnerabilities, and that a fundamentally new, holistic, approach to privacy is needed going forward. We thus highlight various existing technology gaps and propose several promising research directions for addressing and reducing this complexity.