Min Shi (Wuhan University), Yongkang Xiao (Wuhan University), Jing Chen (Wuhan University), Kun He (Wuhan University), Ruiying Du (Wuhan University), Meng Jia (Department of Computing, the Hong Kong Polytechnic University)

The Secure Connection (SC) pairing is the latest version of the security protocol designed to protect sensitive information transmitted over Bluetooth Low Energy (BLE) channels. A formal and rigorous analysis of this protocol is essential for improving security assurances and identifying potential vulnerabilities. However, the complexity of the protocol flow, difficulties in formalizing pairing method selection, and overly idealized user assumptions present significant obstacles to such analysis. In this paper, we address these challenges and present an accurate and comprehensive formal analysis of the BLE-SC pairing protocol using Tamarin. We extract state machines for each participant as the blueprint for modeling the protocol, and we use an equational theory to formalize the pairing method selection logic. Our model incorporates subtle user behaviors and considers stronger adversary capabilities, including the potential compromise of private channels such as the temporary out-of-band channel. We develop a verification strategy to automate protocol analysis and implement a script to parallelize verification tasks across multiple servers. We verify 84 pairing cases and identify the minimal security assumptions required for the protocol. Moreover, our results reveal a new Man-in-the-Middle (MitM) attack, which we call the PE confusion attack. We provide tools and Proof-of-Concept (PoC) exploits for simulating and understanding this attack within a controlled environment. Finally, we propose countermeasures to defend against this attack, improving the security of the BLE-SC pairing protocol.

View More Papers

ACE: A Security Architecture for LLM-Integrated App Systems

Evan Li (Northeastern University), Tushin Mallick (Northeastern University), Evan Rose (Northeastern University), William Robertson (Northeastern University), Alina Oprea (Northeastern University), Cristina Nita-Rotaru (Northeastern University)

Read More

Was My Data Used for Training? Membership Inference in...

Xue Tan (Fudan University), Hao Luan (Fudan University), Mingyu Luo (Fudan University), Zhuyang Yu (Fudan University), Jun Dai (Worcester Polytechnic Institute), Xiaoyan Sun (Worcester Polytechnic Institute), Ping Chen (Fudan University)

Read More

CryptPEFT: Efficient and Private Neural Network Inference via Parameter-Efficient...

Saisai Xia (Institute of Information Engineering, CAS), Wenhao Wang (Institute of Information Engineering, CAS), Zihao Wang (Nanyang Technological University (NTU)), Yuhui Zhang (Institute of Information Engineering, CAS), Yier Jin (University of Science and Technology of China), Dan Meng (Institute of Information Engineering, CAS), Rui Hou (Institute of Information Engineering, CAS)

Read More