Maximilian von Tschirschnitz (Technical University of Munich), Ludwig Peuckert (Technical University of Munich), Moritz Buhl (Technical University of Munich), Jens Grossklags (Technical University of Munich)

Previous works have shown that Bluetooth is susceptible to so-called Method Confusion attacks. These attacks manipulate devices into conducting conflicting key establishment methods, leading to compromised keys. An increasing amount of security-sensitive applications, like payment terminals, organizational asset tracking systems and conferencing technologies now rely on the availability of a technology like Bluetooth.
It is thus an urgent goal to find and validate a mitigation to these attacks or to provide an appropriate replacement for Bluetooth without introducing additional requirements
that exclude device or user groups.
Despite recent solution proposals, existing threat models overlook certain attack vectors or dismiss important scenarios and consequently suffer under new variants of Method Confusion.

We first propose an extended threat model that appreciates the root issue of Method Confusion and also considers multiple pairing attempts and one-sided pairings as security risks.
Evaluating existing solution proposals with our threat model, we are able to detect known Method Confusion attacks, and identify new vulnerabilities in previous solution proposals.
We demonstrate the viability of these attacks on real-world Bluetooth devices. We further discuss a novel solution approach offering enhanced security, while maintaining compatibility with existing hardware and Bluetooth user behavior.
We conduct a formal security proof of our proposal and implement it on commonplace Bluetooth hardware, positioning it as the currently most promising update proposal for Bluetooth.

View More Papers

AI-Assisted RF Fingerprinting for Identification of User Devices in...

Aishwarya Jawne (Center for Connected Autonomy & AI, Florida Atlantic University), Georgios Sklivanitis (Center for Connected Autonomy & AI, Florida Atlantic University), Dimitris A. Pados (Center for Connected Autonomy & AI, Florida Atlantic University), Elizabeth Serena Bentley (Air Force Research Laboratory)

Read More

Heimdall: Towards Risk-Aware Network Management Outsourcing

Yuejie Wang (Peking University), Qiutong Men (New York University), Yongting Chen (New York University Shanghai), Jiajin Liu (New York University Shanghai), Gengyu Chen (Carnegie Mellon University), Ying Zhang (Meta), Guyue Liu (Peking University), Vyas Sekar (Carnegie Mellon University)

Read More

DUMPLING: Fine-grained Differential JavaScript Engine Fuzzing

Liam Wachter (EPFL), Julian Gremminger (EPFL), Christian Wressnegger (Karlsruhe Institute of Technology (KIT)), Mathias Payer (EPFL), Flavio Toffalini (EPFL)

Read More

BARBIE: Robust Backdoor Detection Based on Latent Separability

Hanlei Zhang (Zhejiang University), Yijie Bai (Zhejiang University), Yanjiao Chen (Zhejiang University), Zhongming Ma (Zhejiang University), Wenyuan Xu (Zhejiang University)

Read More