InternalBlue - Bluetooth Binary Patching and Experimentation Framework

Bluetooth is one of the most established technologies for short range digital wireless data transmission. With the advent of wearables and the Internet of Things (IoT), Bluetooth has again gained importance, which makes security research and protocol optimizations imperative. Surprisingly, there is a lack of openly available tools and experimental platforms to scrutinize Bluetooth. In particular, system aspects and close to hardware protocol layers are mostly uncovered. We reverse engineer multiple Broadcom Bluetooth chipsets that are widespread in off-the-shelf devices. Thus, we offer deep insights into the internal architecture of a popular commercial family of Bluetooth controllers used in smartphones, wearables, and IoT platforms. Reverse engineered functions can then be altered with our InternalBlue Python framework---outperforming evaluation kits, which are limited to documented and vendor-defined functions. The modified Bluetooth stack remains fully functional and high-performance. Hence, it provides a portable low-cost research platform. InternalBlue is a versatile framework and we demonstrate its abilities by implementing tests and demos for known Bluetooth vulnerabilities. Moreover, we discover a novel critical security issue affecting a large selection of Broadcom chipsets that allows executing code within the attacked Bluetooth firmware. We further show how to use our framework to fix bugs in chipsets out of vendor support and how to add new security features to Bluetooth firmware

MagicPairing: Apple's Take on Securing Bluetooth Peripherals

Device pairing in large Internet of Things (IoT) deployments is a challenge for device manufacturers and users. Bluetooth offers a comparably smooth trust on first use pairing experience. Bluetooth, though, is well-known for security flaws in the pairing process. In this paper, we analyze how Apple improves the security of Bluetooth pairing while still maintaining its usability and specification compliance. The proprietary protocol that resides on top of Bluetooth is called MagicPairing. It enables the user to pair a device once with Apple's ecosystem and then seamlessly use it with all their other Apple devices. We analyze both, the security properties provided by this protocol, as well as its implementations. In general, MagicPairing could be adapted by other IoT vendors to improve Bluetooth security. Even though the overall protocol is well-designed, we identified multiple vulnerabilities within Apple's implementations with over-the-air and in-process fuzzing

Inside Job: Diagnosing Bluetooth Lower Layers Using Off-the-Shelf Devices

Bluetooth is among the dominant standards for wireless short-range communication with multi-billion Bluetooth devices shipped each year. Basic Bluetooth analysis inside consumer hardware such as smartphones can be accomplished observing the Host Controller Interface (HCI) between the operating system's driver and the Bluetooth chip. However, the HCI does not provide insights to tasks running inside a Bluetooth chip or Link Layer (LL) packets exchanged over the air. As of today, consumer hardware internal behavior can only be observed with external, and often expensive tools, that need to be present during initial device pairing. In this paper, we leverage standard smartphones for on-device Bluetooth analysis and reverse engineer a diagnostic protocol that resides inside Broadcom chips. Diagnostic features include sniffing lower layers such as LL for Classic Bluetooth and Bluetooth Low Energy (BLE), transmission and reception statistics, test mode, and memory peek and poke

Steps toward accurate large-area analyses of Genesis solar wind samples: evaluation of surface cleaning methods using total reflection X-ray fluorescence spectrometry

Total reflection X-ray fluorescence spectrometry (TXRF) was used to analyze residual surface contamination on Genesis solar wind samples and to evaluate different cleaning methods. To gauge the suitability of a cleaning method, two samples were analyzed following cleaning by lab-based TXRF. The analysis comprised an overview and a crude manual mapping of the samples by orienting them with respect to the incident X-ray beam in such a way that different regions were covered. The results show that cleaning with concentrated hydrochloric acid and a combination of hydrochloric acid and hydrofluoric acid decreased persistent inorganic contaminants substantially on one sample. The application of CO2 snow for surface cleaning tested on the other sample appears to be effective in removing one persistent Genesis contaminant, namely germanium. Unfortunately, the TXRF analysis results of the second sample were impacted by relatively high background contamination. This was mostly due to the relatively small sample size and that the solar wind collector was already mounted with silver glue for resonance ion mass spectrometry (RIMS) on an aluminium stub. Further studies are planned to eliminate this problem. In an effort to identify the location of very persistent contaminants, selected samples were also subjected to environmental scanning electron microscopy. The results showed excellent agreement with TXRF analysis

Quality of experience study for multiple sensorial media delivery

Traditional video sequences make use of both visual images and audio tracks which are perceived by human eyes and ears, respectively. In order to present better ultra-reality virtual experience, the comprehensive human sensations (e.g. olfaction, haptic, gustatory, etc) needed to be exploited. In this paper, a multiple sensorial media (mulsemedia) delivery system is introduced to deliver multimedia sequences integrated with multiple media components which engage three or more of human senses such as sight, hearing, olfaction, haptic, gustatory, etc. Three sensorial effects (i.e. haptic, olfaction, and air-flowing) are selected for the purpose of demonstration. Subjective test is conducted to analyze the user perceived quality of experience of the mulsemedia service. It is concluded that the mulsemedia sequences can partly mask the decreased movie quality. Additionally the most preferable sensorial effect is haptic, followed by air-flowing and olfaction.This work was supported in part by Enterprise Ireland Innovation Partnership programme

Frankenstein: Advanced Wireless Fuzzing to Exploit New Bluetooth Escalation Targets

Wireless communication standards and implementations have a troubled history regarding security. Since most implementations and firmwares are closed-source, fuzzing remains one of the main methods to uncover Remote Code Execution (RCE) vulnerabilities in deployed systems. Generic over-the-air fuzzing suffers from several shortcomings, such as constrained speed, limited repeatability, and restricted ability to debug. In this paper, we present Frankenstein, a fuzzing framework based on advanced firmware emulation, which addresses these shortcomings. Frankenstein brings firmware dumps "back to life", and provides fuzzed input to the chip's virtual modem. The speed-up of our new fuzzing method is sufficient to maintain interoperability with the attached operating system, hence triggering realistic full-stack behavior. We demonstrate the potential of Frankenstein by finding three zero-click vulnerabilities in the Broadcom and Cypress Bluetooth stack, which is used in most Apple devices, many Samsung smartphones, the Raspberry Pis, and many others. Given RCE on a Bluetooth chip, attackers may escalate their privileges beyond the chip's boundary. We uncover a Wi-Fi/Bluetooth coexistence issue that crashes multiple operating system kernels and a design flaw in the Bluetooth 5.2 specification that allows link key extraction from the host. Turning off Bluetooth will not fully disable the chip, making it hard to defend against RCE attacks. Moreover, when testing our chip-based vulnerabilities on those devices, we find BlueFrag, a chip-independent Android RCE.Comment: To be published at USENIX Securit

Firmware Insider: Bluetooth Randomness is Mostly Random

Bluetooth chips must include a Random Number Generator (RNG). This RNG is used internally within cryptographic primitives but also exposed to the operating system for chip-external applications. In general, it is a black box with security-critical authentication and encryption mechanisms depending on it. In this paper, we evaluate the quality of RNGs in various Broadcom and Cypress Bluetooth chips. We find that the RNG implementation significantly changed over the last decade. Moreover, most devices implement an insecure Pseudo-Random Number Generator (PRNG) fallback. Multiple popular devices, such as the Samsung Galaxy S8 and its variants as well as an iPhone, rely on the weak fallback due to missing a Hardware Random Number Generator (HRNG). We statistically evaluate the output of various HRNGs in chips used by hundreds of millions of devices. While the Broadcom and Cypress HRNGs pass advanced tests, it remains indistinguishable for users if a Bluetooth chip implements a secure RNG without an extensive analysis as in this paper. We describe our measurement methods and publish our tools to enable further public testing.Comment: WOOT'2

Preface - Creativity and HCI: From Experience to Design in Education

Abstract included in text