SweynTooth: Unleashing Mayhem over Bluetooth Low Energy

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SweynTooth: Unleashing Mayhem over Bluetooth Low Energy
Title:
SweynTooth: Unleashing Mayhem over Bluetooth Low Energy
Journal Title:
USENIX Annual Technical Conference (2020)
DOI:
Publication Date:
17 July 2020
Citation:
Abstract:
The Bluetooth Low Energy (BLE) is a promising short-range communication technology for Internet-of-Things (IoT) with reduced energy consumption. Vendors implement BLE protocols in their manufactured devices compliant to Bluetooth Core Specification. Recently, several vulnerabilities were discovered in the BLE protocol implementations of a few specific products via a manual approach. Considering the diversity and usage of BLE devices as well as the complexity of BLE protocols, we have developed a systematic and comprehensive testing framework, which, as an automated and general-purpose approach, can effectively fuzz any BLE protocol implementation. Our framework runs in a central device and tests a BLE device when the latter gets connected to the central as a peripheral. Our framework incorporates a state machine model of the suite of BLE protocols and monitors the peripheral's state through its responses. With the state machine and current state of the central, our framework either sends malformed packets or normal packets at a wrong time, or both, to the peripheral and awaits an expected response. Anomalous behaviours of the peripheral, e.g., a non-compliant response or unresponsiveness, indicate potential vulnerabilities in its BLE protocol implementation. To maximally expose such anomalies for a BLE device, our framework employs an optimization function to direct the fuzzing process. As of today, we have tested 12 devices from eight vendors and four IoT products, with a total of 11 new vulnerabilities discovered and 13 new Common Vulnerability Exposure (CVE) IDs assigned. We call such a bunch of vulnerabilities as SWEYNTOOTH, which highlights the efficacy of our framework.
License type:
Funding Info:
A*STAR SINGA Scholarship. This work is partially supported by Keysight Technologies grant no. RTKS171003.
Description:
ISBN:
978-1-939133-14-4
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