In modern mobile devices, baseband is an integral component running on top of cellular processors to handle crucial radio communications. However, recent research reveals significant vulnerabilities in these basebands, posing serious security risks like remote code execution. Yet, effectively scrutinizing basebands remains a daunting task, as they run closed-source and proprietary software on vendor-specific chipsets. Existing analysis methods are limited by their dependence on manual processes and heuristic approaches, reducing their scalability. This paper introduces a novel approach to unveil security issues in basebands from a unique perspective: to uncover vendor-specific baseband commands from the Radio Interface Layer (RIL), a hardware abstraction layer interfacing with basebands. To demonstrate this concept, we have designed and developed BaseMirror, a static binary analysis tool to automatically reverse engineer baseband commands from vendor-specific RIL binaries. It utilizes a bidirectional taint analysis algorithm to adeptly identify baseband commands from an enhanced control flow graph enriched with reconstructed virtual function calls. Our methodology has been applied to 28 vendor RIL libraries, encompassing a wide range of Samsung Exynos smartphone models on the market. Remarkably, BaseMirror has uncovered 873 unique baseband commands undisclosed to the public. Based on these results, we develop an automated attack discovery framework to successfully derive and validate 8 zero-day vulnerabilities that trigger denial of cellular service and arbitrary file access on a Samsung Galaxy A53 device. These findings have been reported and confirmed by Samsung and a bug bounty was awarded to us.

The evolution from 5G to 6G cellular networks signifies a crucial advancement towards enhanced robustness and automation driven by the promise of ubiquitous Artificial Intelligence (AI) to overhaul network operations, commonly referred to as AIOps. However, 6G network operators also need to deal with evolving threats at the edge to ensure data integrity and availability. We introduce 6G-XSEC, the first framework that seeks to automatically monitor, analyze, and explain anomalies and threats at the cellular network edge. Our framework enhances the emerging Open Radio Access Network (O-RAN) control plane with run-time analytic capabilities and explainability. A distinguishing aspect of our framework is the use of expert referencing, a coupling of lightweight unsupervised deep learning-based anomaly detection with large language models (LLMs) to first detect, analyze, and subsequently explain complicated real-world cellular threats and anomalies at run-time, based on enhanced security telemetry from the O-RAN data plane. We build a prototype 6G-XSEC framework and evaluate it against 5 end-to-end cellular attacks from the literature, achieving 100% detection rate with our best model. We also propose effective LLM prompt templates for attack analysis and present qualitative results from 5 popular LLMs.

Nowadays, there are a massive number of embedded Internet-of-Things (IoT) devices, each of which includes a microcontroller unit (MCU) that can support numerous peripherals. To detect security vulnerabilities of these embedded devices, there are a number of emulation (or rehosting) frameworks that enable scalable dynamic analysis by using only the device firmware code without involving the real hardware. However, we show that using only the firmware code for emulation is insufficient since there exists a special type of hardware-defined property among the peripheral registers that allows the bounded registers to be updated simultaneously without CPU interventions, which is called the hidden memory mapping. In this paper, we demonstrate that existing rehosting frameworks such as P2IM and μEMU have incorrect execution paths as they fail to properly handle hidden memory mapping during emulation. To address this challenge, we propose the first framework AutoMap that uses a differential hardware memory introspection approach to automatically reveal hidden memory mappings among peripheral registers for faithful firmware emulation. We have developed AutoMap atop the Unicorn emulator and evaluated it with 41 embedded device firmware developed based on the Nordic MCU and 9 real-world firmware evaluated by μEMU and P2IM on the two STMicroelectronics MCUs. Among them, AutoMap successfully extracted 2, 359 unique memory mappings in total which can be shared through a knowledge base with the rehosting frameworks. Moreover, by integrating AutoMap with μEMU, AutoMap is able to identify and correct the path of the program that will not run on the actual hardware.

Digital contact tracing offers significant promise to help reduce the spread of SARS-CoV-2 and other viruses. Google and Apple joined together in 2020 to create the Google/Apple Exposure Notification (GAEN) framework to determine encounters with anonymous users later diagnosed COVID-19 positive. However, as GAEN lacks geospatial awareness, it is susceptible to geographically distributed replay attacks. Anonymous, low-cost, crowd-sourced replay attack networks deployed by malicious actors (or far away nation-state attackers) who utilize malicious (or innocent) users’ smartphones to capture and replay GAEN advertisements can drastically increase false-positive rates even in areas that otherwise exhibit low positivity rates. In response to this powerful replay attack, we introduce GAEN+ , a solution that enhances GAEN with geospatial awareness while maintaining user privacy, and demonstrate its ability to effectively prevent geographically distributed replay attacks.

Today, Bluetooth 4.0, also known as Bluetooth Low Energy (BLE), has been widely used in many IoT devices (e.g., smart locks, smart sensors, and wearables). However, BLE devices could contain a number of vulnerabilities at the BLE link layer during broadcasting, pairing, and message transmission. To detect these vulnerabilities directly from the bare-metal firmware, we present FirmXRay, the first static binary analysis tool with a set of enabling techniques including a novel base address identification algorithm for robust firmware disassembling, precise data structure recognition, and configuration value resolution. As a proof-of-concept, we focus on the BLE firmware from two leading SoC vendors (i.e., Nordic and Texas Instruments), and implement a prototype of FirmXRay atop Ghidra. We have evaluated FirmXRay with 793 unique firmware (corresponding to 538 unique devices) collected using a mobile app based approach, and our experiment results show that 98.1% of the devices have configured random static MAC addresses, 71.5% Just Works pairing, and 98.5% insecure key exchanges. With these vulnerabilities, we demonstrate identity tracking, spoofing, and eavesdropping attacks on real-world BLE devices.

Being an easy-to-deploy and cost-effective low power wireless solution, Bluetooth Low Energy (BLE) has been widely used by Internet-of-Things (IoT) devices. In a typical IoT scenario, an IoT device first needs to be connected with its companion mobile app which serves as a gateway for its Internet access. To establish a connection, a device first broadcasts advertisement packets with UUIDs to nearby smartphone apps. Leveraging these UUIDs, a companion app is able to identify the device, pairs and bonds with it, and allows further data communication. However, we show that there is a fundamental flaw in the current design and implementation of the communication protocols between a BLE device and its companion mobile app, which allows an attacker to precisely fingerprint a BLE device with static UUIDs from the apps. Meanwhile, we also discover that many BLE IoT devices adopt "just works" pairing, allowing attackers to actively connect with these devices if there is no app-level authentication. Even worse, this vulnerability can also be directly uncovered from mobile apps. Furthermore, we also identify that there is an alarming number of vulnerable app-level authentication apps, which means the devices connected by these apps can be directly controlled by attackers. To raise the public awareness of IoT device fingerprinting and also uncover these vulnerable BLE IoT devices before attackers, we develop an automated mobile app analysis tool BLESCOPE and evaluate it with all of the free BLE IoT apps in Google Play store. Our tool has identified 1,757 vulnerable mobile apps in total. We also performed a field test in a 1.28 square miles region, and identified 5,822 real BLE devices, among them 5,509 (94.6%) are fingerprintable by attackers, and 431 (7.4%) are vulnerable to unauthorized access. We have made responsible disclosures to the corresponding app developers, and also reported the fingerprinting issues to the Bluetooth Special Interest Group.

During the development of web-based mobile apps, third-party SDKs (Software Development Kit) are frequently used to facilitate the integration of certain functionality such as push notification and mobile payment. Unfortunately, security issues are often considered as a second-tier problem and app developers are prone to implement apps with SDK misuses. Among those typical SDK misuses, the misuse of credentials is the one that introduces serious security threats. A credential is a set of unique information (e.g., APP ID, App Token, etc) allocated to a specific developer to help app authenticate the identity. However, if not properly used, the credential can be easily obtained by attackers and leads to not only the leak of confidential information of mobile developers but also direct threats to the privacy of end users. To investigate the SDK credential misuse issue on iOS platform, in this paper we conduct an empirical study against 100 popular iOS apps using two popular mobile SDKs (each SDK are widely used by at least 40 million users). We implemented iCredFinder, an automated analysis tool to search credential misuses in those apps and our experiment demonstrates 68 apps contain at least one misuse case. Our study demonstrates the severity of credential misuse on iOS platform: even for those well-developed SDKs and apps, credentials are not well protected and can be easily discovered. We expect that our study could help developers fix those flaws and promote better implementations.