Baseband processors (BPs) in cellular devices implement complex radio protocols, and memory corruption vulnerabilities in these implementations can lead to critical security breaches, including remote code execution. Traditional approaches to detecting such vulnerabilities rely on reverse engineering or emulation. However, these methods face significant scalability challenges due to proprietary firmware and architectural complexities. Over-the-air (OTA) testing offers broader applicability but poses challenges in managing UE state, detecting crashes, and ensuring protocol coverage. We present OTABase, an OTA testing framework that enables efficient detection of memory crashes in LTE base-bands by leveraging protocol specifications. OTABase combines three key techniques: a network-side state control mechanism for efficient management of UE states and connections, a specification-guided test case generation targeting memory crashes in NAS and RRC protocols, and a two-phase crash detection oracle utilizing protocol-based liveness checks and manufacturer debug features. Evaluating OTABase on six commercial BPs from three major manufacturers, unearthed seven previously unpatched memory crashes. Among these, three were assigned CVEs, including one out-of-bounds write vulnerability that allows remote code execution. Additionally, we extend OTABase to 5G basebands for PoC, demonstrating its generalizability and practical utility.

In cellular networks, authorities may need to physically locate user devices to track criminals or illegal equipment. This process involves authorized agents tracing devices by monitoring uplink signals with cellular operator assistance. However, tracking uncooperative uplink signal sources remains challenging, even for operators and authorities. Three key challenges persist for fine-grained localization: i) devices must generate sufficient, consistent uplink traffic over time, ii) target devices may transmit uplink signals at very low power, and iii) signals from cellular repeaters may hinder localization of the target device. While these challenges pose significant practical obstacles to localization, they have been largely overlooked in existing research.

LTE sniffers are important for security and performance analysis because they can passively capture the wireless traffic of users in LTE network. However, existing open-source LTE sniffers have only limited functionality and cannot decode data traffic. This paper introduces LTESNIFFER, the first open-source LTE sniffer that can passively decode both uplink and downlink data traffic. Implementing a sniffer is not trivial because one needs to understand detailed configurations and parameters to successfully decode each user's traffic. Using multiple techniques, we found mechanisms to understand these, which improves our decoding performance. We evaluated the performance of LTESNIFFER on both testbed and commercial network environments. We also compare the performance of LTESNIFFER with AirScope, a popular commercial LTE sniffer. Additionally, LTESNIFFER provides a proof-of-concept API with three functions that can be used for security applications, including identity mapping, identity collecting, and device capability profiling. We release LTESNIFFER as open-source for future research.