5G and beyond cellular systems embrace the disaggregation of Radio Access Network (RAN) components, exemplified by the evolution of the fronthaul (FH) connection between cellular baseband and radio unit equipment. Crucially, synchronization over the FH is pivotal for reliable 5G services. In recent years, there has been a push to move these links to an Ethernet-based packet network topology, leveraging existing standards and ongoing research for Time-Sensitive Networking (TSN). However, TSN standards, such as Precision Time Protocol (PTP), focus on performance with little to no concern for security. This increases the exposure of the open FH to security risks. Attacks targeting synchronization mechanisms pose significant threats, potentially disrupting 5G networks and impairing connectivity. In this article, we demonstrate the impact of successful spoofing and replay attacks against PTP synchronization. We show how a spoofing attack is able to cause a production-ready O-RAN and 5G-compliant private cellular base station to catastrophically fail within 2 seconds of the attack, necessitating manual intervention to restore full network operations. To counter this, we design a Machine Learning (ML)-based monitoring solution capable of detecting various malicious attacks with over 97.5% accuracy.

The past decade has witnessed the wide adoption of AI code generators, such as GitHub Copilot, AskCodi, and OpenAI Codex. They offer intelligent solution code for code completion to achieve faster development, cleaner code, and a significant boost in overall productivity. However, such significant productivity advantages also inadvertently lead to the generation of insecure solution code because most AI code generators derive their knowledge from existing projects, which typically prioritize functionality over security. Although numerous tools have been developed to integrate with the code generators for identifying vulnerabilities, the inconsistency in syntactic features and variability in coding rules make the detection task challenging across different programming languages. To address the challenges, we devise a prompt-enhancing approach, PECKER. It examines textual prompts provided by users to identify risky prompts that could lead to insecure code generation. Given the risky prompts, PECKER conducts security-centric rewriting to strengthen the "potentially insecure" descriptions, thereby guiding AI code generators in mitigating vulnerabilities during code generation. We integrated PECKER with one of the most prevalent AI code generators, GitHub Copilot for evaluation. Among 509 risky prompts, PECKER successfully identified and rectified 471 risky prompts.

Federated learning (FL) is well-suited to 5G networks, where many mobile devices generate sensitive edge data. Secure aggregation protocols enhance privacy in FL by ensuring that individual user updates reveal no information about the underlying client data. However, the dynamic and large-scale nature of 5G-marked by high mobility and frequent dropouts-poses significant challenges to the effective adoption of these protocols. Existing protocols often require multi-round communication or rely on fixed infrastructure, limiting their practicality. We propose a lightweight, single-round secure aggregation protocol designed for 5G environments. By leveraging base stations for assisted computation and incorporating precomputation, key-homomorphic pseudorandom functions, and t-out-of-k secret sharing, our protocol ensures efficiency, robustness, and privacy. Experiments show strong security guarantees and significant gains in communication and computation efficiency, making the approach well-suited for real-world 5G FL deployments.

Federated learning (FL) enables collaborative model training while preserving user data privacy by keeping data local. Despite these advantages, FL remains vulnerable to privacy attacks on user updates and model parameters during training and deployment. Secure aggregation protocols have been proposed to protect user updates by encrypting them, but these methods often incur high computational costs and are not resistant to quantum computers. Additionally, differential privacy (DP) has been used to mitigate privacy leakages, but existing methods focus on secure aggregation or DP, neglecting their potential synergies. To address these gaps, we introduce$\texttt {Beskar}$, a novel framework that provides post-quantum secure aggregation, optimizes computational overhead for FL settings, and defines a comprehensive threat model that accounts for a wide spectrum of adversaries. We also integrate DP into different stages of FL training to enhance privacy protection in diverse scenarios. Our framework provides a detailed analysis of the trade-offs between security, performance, and model accuracy, representing the first thorough examination of secure aggregation protocols combined with various DP approaches for post-quantum secure FL.$\texttt {Beskar}$aims to address the pressing privacy and security issues FL while ensuring quantum-safety and robust performance.

Modern smart TVs often communicate with their remote controls (including the smartphone simulated ones) using multiple wireless channels (e.g., Infrared, Bluetooth, and Wi-Fi). However, this multi-channel remote control communication introduces a new attack surface. An inherent security flaw is that remote controls of most smart TVs are designed to work in a benign environment rather than an adversarial one, and thus wireless communications between a smart TV and its remote controls are not strongly protected. Attackers can leverage such a flaw to abuse the remote control communication and compromise smart TV systems. In this paper, we propose EVILSCREEN, a novel attack that exploits ill-protected remote control communications to access protected resources of a smart TV or even control the screen. EVILSCREEN exploits a multi-channel remote control mimicry vulnerability present in today smart TVs. Unlike other attacks, which compromise the TV system by exploiting code vulnerabilities or malicious third-party apps, EVILSCREEN directly reuses commands of different remote controls, combines them together to circumvent deployed authentication and isolation policies, and finally accesses or controls TV resources remotely. We evaluated eight mainstream smart TVs and found that they are all vulnerable to EVILSCREEN attacks, including a Samsung product adopting the ISO/IEC security specification.

Today users can access and/or control their IoT devices using mobile apps. Such interactions often rely on IoT-to-Mobile communication that supports direct data exchanges between IoT devices and smartphones. To guarantee mutual authentication and encrypted data transmission in IoT-to-Mobile communications while keeping lightweight implementation, IoT devices and smartphones often share credentials in advance with the help of a cloud server. Since these credentials impact communication security, in this paper we seek to understand how such sensitive materials are implemented. We design a set of analysis techniques and implement them in KingFisher, an analysis framework. KingFisher identifies shared credentials, tracks their uses, and examines violations against nine security properties that the implementation of credentials should satisfy. With an evaluation of eight real-world IoT solutions with more than 35 million deployed devices, KingFisher revealed that all these solutions involve insecurely used credentials, and are subject to privacy leakage or device hijacking.

Existing tools for the automated detection of memory corruption bugs are not very effective in practice. They typically recognize only standard memory management (MM) APIs (e.g., malloc and free) and assume a naive paired-use model—an allocator is followed by a specific deallocator. However, we observe that programmers very often design their own MM functions and that these functions often manifest two major characteristics: (1) Custom allocator functions perform multi-object or nested allocation which then requires structure-aware deallocation functions. (2) Custom allocators and deallocators follow an unpaired-use model. A more effective detection thus needs to adapt those characteristics and capture memory bugs related to non-standard MM behaviors. In this paper, we present a MM function aware memory bug detection technique by introducing the concept of structure-aware and object-centric Memory Operation Synopsis (MOS). A MOS abstractly describes the memory objects of a given MM function, how they are managed by the function, and their structural relations. By utilizing MOS, a bug detection could explore much less code but is still capable of handling multi-object or nested allocations and does not rely on the paired-use model. In addition, to extensively find MM functions and automatically generate MOS for them, we propose a new identification approach that combines natural language processing (NLP) and data flow analysis, which enables the efficient and comprehensive identification of MM functions, even in very large code bases. We implement a MOS-enhanced memory bug detection system, Goshawk, to discover memory bugs caused by complex and custom MM behaviors. We applied Goshawk to well-tested and widely-used open source projects including OS kernels, server applications, and IoT SDKs. Goshawk outperforms the state-of-the-art data flow analysis driven bug detection tools by an order of magnitude in analysis speed and the number of accurately identified MM functions, reports the discovered bugs with a developer-friendly, MOS based description, and successfully detects 92 new double-free and use-after-free bugs.