WiSec 2025: 18th ACM Conference on Security and Privacy in Wireless and Mobile Networks

Telecommunication networks form the backbone of our connected society, enabling global voice and data communication. Yet, beneath their seamless operation lies a complex interplay of signaling protocols, legacy systems, and evolving technologies that have, over the decades, exposed both opportunities for innovation and significant security challenges. While these networks have changed over the past 50 years and complexity has migrated outwards to devices, one enduring challenge has been a lack of accessibility. In this talk, we will discuss some of our recent efforts to better understand functionality on mobile devices by examining command sets [3] and processors [2]. We also look at security assessment techniques and how they can expose vulnerabilities in core cellular infrastructure. A central focus of this talk is the application of fuzz testing-a powerful technique for discovering implementation flaws and specification ambiguities. Fuzzing involves generating random or mutated inputs and injecting them into target systems to observe unusual behaviors or crashes. In the context of telecom networks, a fuzzing campaign can target the message handling logic, permuting fields such as the Protocol Discriminator or Bearer Capabilities, causing ASN.1 decoders in some devices to fail, leading to denial-of-service or potentially more severe consequences [1]. Through case studies and demonstrations, the talk will reveal how seemingly innocuous fields in call setup messages can destabilize network components or user devices, while also examining how the analysis of messages rather than examining device behavior can lead to the detection of rogue elements [4] in a network.

As the race to define 6G intensifies, the focus has overwhelmingly centered on maximizing efficiency-whether in cost, spectral utilization, or energy consumption. Yet, resilience and security, the cornerstone attributes for deploying 6G in mission-critical industries such as energy, defense, and healthcare, have been conspicuously absent from the design discourse. This oversight, rooted in legacy approaches to cellular communication, risks undermining 6G's ability to meet the uncompromising demands of applications where reliability and adaptability are non-negotiable. In this keynote, I will redefine resilience in the context of next-generation wireless networks, distinguishing it from related concepts such as robustness and reliability, while highlighting how security is inherently embedded within resilience. I will explore the tension between efficiency, complexity, and resilience, revealing how the relentless pursuit of superior performance has led to overly intricate designs that inadvertently compromise the ability to withstand disruptions. By identifying the structural and systemic traits that characterize truly resilient networks, I will offer a roadmap for how 6G design must evolve to integrate resilience as a foundational principle. Finally, I will introduce practical frameworks for quantifying resilience, equipping researchers and engineers with tools to embed this critical attribute into the DNA of 6G systems. This keynote is a call to action-a rallying cry to rethink the priorities driving 6G innovation. By striking a deliberate balance between efficiency, complexity, and resilience, we can ensure that 6G not only delivers on its promise of performance but also becomes a transformative technology capable of thriving in the most demanding, high-stakes environments.

This paper presents WirelessTap, a system that demonstrates the potential for automated speech recognition (ASR) on phone call audio eavesdropped remotely using commercially available frequency modulated continuous wave millimeter-wave (mmWave) radars operating in the 77-81 GHz range. WirelessTap detects minute vibrations from smartphone earpieces, converts them into audio, and processes this audio for speech transcription. This work presents the first full-sentence ASR using mmWave radars on earpiece vibrations using a 10,000-word vocabulary, achieving a 300 cm attack range across multiple smartphone models. It surpasses prior radar-based eavesdropping studies limited to loudspeakers, small vocabularies, or constrained evaluations. To address challenges like the absence of large mmWave radar-based audio datasets, low signal-to-noise ratio, and limited voice frequency ranges extractable from radar data, WirelessTap incorporates synthetic data generation, domain adaptation, and inference using OpenAI's Whisper ASR model. Our experiments systematically show how word accuracy rate gradually decreases with distance, from as high as 59.25% at 50 cm to 2% at 300 cm; additionally, we deploy this attack to a real-world setting with a user study targeting a victim holding a smartphone to their ear. This paper highlights the evolving risks of artificial intelligence and sensor systems being misused as technology advances.

In run-time attestation schemes, including Control Flow Attestation (CFA) and Data Flow Attestation (DFA), a remote Verifier (Vrf) requests a potentially compromised Prover device (Prv) to generate evidence of its execution control flow path (in CFA) and optionally execution data inputs (in DFA). Recent advances in this space also guarantee that Vrf eventually receives run-time evidence from Prv, even when Prv is fully compromised. Reliable delivery, in theory, enables run-time auditing in addition to attestation, allowing Vrf to examine run-time compromise traces to pinpoint/remediate attack root causes. However, Vrf's perspective in this security service remains unexplored, with most prior work focusing on the secure generation of authentic run-time evidence on Prv.

In this work, we argue that run-time attestation/auditing is only effective if Vrf can analyze the received evidence. From this premise, we characterize different types of evidence produced by run-time attestation/auditing architectures based on Vrf's ability to use them for vulnerability detection/remediation. As a case study, we propose SABRE: a Security Analysis and Binary Repair Engine that enables Vrf to use run-time evidence to detect control flow attacks, to pinpoint specific instructions that corrupted control data, and to automatically generate binary patches to buffer overflow and use-after-free vulnerabilities without source code knowledge.

Global Positioning System (GPS) signals, though critical to numerous civilian and industrial applications, remain susceptible to spoofing due to their unencrypted nature. While many existing defenses focus on single-antenna spoofing, multi-antenna spoofing has been theorized as a significantly more potent threat. However, practical realizations of multi-antenna spoofing have been limited by the stringent requirement of nanosecond-level synchronization.

In this paper, we present the first low-cost, end-to-end implementation of a distributed multi-antenna GPS spoofing attack using off-the-shelf devices. We systematically examine the technical prerequisites, establishing sub-50 ns alignment among spoofing signals as the requirement for successfully spoofing standard GPS receivers. Building on this analysis, we design a multi-antenna spoofing system that continuously monitors and adaptively adjusts relative signal timing, mitigating hardware imperfections and oscillator drift in real time. Our prototype, built using HackRFs and Raspberry Pis, demonstrates that it can successfully spoof devices such as Android phones and commercial GPS receivers. Through controlled experiments in an anechoic chamber, we show that our attack can steer these receivers to falsified locations with an average error of 30~m, while also evading detection by robust angle-of-arrival-based systems. Finally, we discuss practical considerations for wide-area deployments, along with countermeasures that may mitigate this emerging threat.

This paper analyzes the Embedded SIM card's Machine-to-Machine Remote Provisioning Protocol's (eSIM M2M RSP) design. The eSIM M2M RSP simplifies 5G connectivity for IoT devices by securely delivering connection bootstrapping information over the air without human intervention. As IoT adoption with 5G connectivity surges, the eSIM infrastructure must handle a growing number of concurrent remote SIM provisioning requests. The statefulness and shared states of the RSP make it challenging and error-prone to implement concurrency without data races. The GSMA eSIM standard does not explicitly define any atomicity assumptions required for concurrent execution. A formal analysis of the standard-prescribed M2M RSP design reveals that explicit atomicity assumptions are necessary; without them, 31 data races can violate key invariants. During the responsible disclosure process, discussions with the standards body revealed that the M2M RSP design relies on unstated and implicit atomicity assumptions. However, we find that the standard prescribed implicit assumptions are not strong enough to maintain all the invariants. The identified race conditions can be exploited by third-party eSIM management platforms to defraud network operators. To mitigate these risks, we developed a fine-grained synchronization mechanism that we formally verified for correctness and empirically evaluated for performance. Empirical evaluations show that our synchronization mechanism ensures correctness while outperforming a baseline with a 6× speed up.

The advent of Open Radio Access Networks (O-RAN) has transformed the telecommunications industry by promoting interoperability, vendor diversity, and rapid innovation. However, its disaggregated architecture introduces complex testing challenges, particularly in validating multi-vendor components against O-RAN ALLIANCE and 3GPP specifications. Existing frameworks, such as those provided by Open Testing and Integration Centres (OTICs), rely heavily on manual processes, are fragmented and prone to human error, leading to inconsistency and scalability issues. To address these limitations, we present AI5GTest -- an AI-powered, specification-aware testing framework designed to automate the validation of O-RAN components. AI5GTest leverages a cooperative Large Language Models (LLM) framework consisting of Gen-LLM, Val-LLM, and Debug-LLM. Gen-LLM automatically generates expected procedural flows for test cases based on 3GPP and O-RAN specifications, while Val-LLM cross-references signaling messages against these flows to validate compliance and detect deviations. If anomalies arise, Debug-LLM performs root cause analysis, providing insight to the failure cause. To enhance transparency and trustworthiness, AI5GTest incorporates a human-in-the-loop mechanism, where the Gen-LLM presents top-k relevant official specifications to the tester for approval before proceeding with validation. Evaluated using a range of test cases obtained from O-RAN TIFG and WG5-IOT test specifications, AI5GTest demonstrates a significant reduction in overall test execution time compared to traditional manual methods, while maintaining high validation accuracy.

Privacy is a fundamental right concerned with the protection and control of personal and sensitive information. A common threat to user privacy is the monitoring and tracking of individuals during web browsing without their consent. A common technique used for this purpose is browser fingerprinting, which exploits characteristics of a user's device to create a unique identifier. Traditionally, software-based fingerprints have been used for this purpose. Recently, hardware-based fingerprinting has gained attention due to its resilience to privacy-enhancing technologies. WebGPU is a modern JavaScript API that enables webpages to efficiently utilize a device's Graphics Processing Unit (GPU) for general-purpose computation. This paper focuses on the impact of WebGPU on hardware-based fingerprinting. We show that by using this web API, the execution behaviour of a device's GPU can be characterized to reidentify the device while evading privacy mechanisms. We introduce AtomicIncrement, a novel fingerprinting approach based on the scheduling behaviour of compute shaders which is usable within WebGPU with both high accuracy and low computational impact. In our evaluation, a classifier can reidentify a device with an accuracy of 70% from a pool of 500 devices using AtomicIncrement fingerprints, highlighting the privacy threat of WebGPU-based fingerprinting for modern web browsing. A robustness analysis with over 2 million fingerprints shows that the accuracy remains stable under various device conditions.

In-flight Wi-Fi provides high-speed Internet connectivity to travelers at 30,000 feet at premium fees. In this paper, we present the first systematic study of the architecture and security policies of in-flight Wi-Fi paywall systems using network tomography analysis. We discover that attackers can exploit the inherent architectural shortcomings of airborne networks to create covert channels and conceal data packets within certain ''always-allowed'' traffic for free Internet access. Moreover, broken device authentication policies in these systems allow unlimited complimentary Internet connectivity. Finally, insecure ARP policies allow attackers to steal paid users' bandwidth to access the free Internet even faster. We validate these issues in practice over two major in-flight Wi-Fi providers using common protocols, e.g., UDP, DNS, etc. We also find that the root causes of these issues stem from different design choices in the architectures of these systems and propose countermeasures to address these flaws and prevent similar attacks.

Location-based service (LBS) applications proliferate and support transportation, entertainment, and more. Modern mobile platforms, with smartphones being a prominent example, rely on terrestrial and satellite infrastructures (e.g., global navigation satellite system (GNSS) and crowdsourced Wi-Fi, Bluetooth, cellular, and IP databases) for correct positioning. However, they are vulnerable to attacks that manipulate positions to control and undermine LBS functionality-thus enabling the scamming of users or services. Our work reveals that GNSS spoofing attacks succeed even though smartphones have multiple sources of positioning information. Moreover, that Wi-Fi spoofing attacks with GNSS jamming are surprisingly effective. More concerning is the evidence that sophisticated, coordinated spoofing attacks are highly effective. Attacks can target GNSS in combination with other positioning methods, thus defenses that assume that only GNSS is under attack cannot be effective. More so, resilient GNSS receivers and special-purpose antennas are not feasible on smartphones. To address this gap, we propose an extended receiver autonomous integrity monitoring (RAIM) framework that leverages the readily available, redundant, often so-called opportunistic positioning information on off-the-shelf platforms. We jointly use onboard sensors, terrestrial infrastructures, and GNSS. We show that our extended RAIM framework improves resilience against location spoofing, e.g., achieving a detection accuracy improvement of up to 24-58% compared to the state-of-the-art algorithms and location providers; detecting attacks within 5 seconds, with a low false positive rate.

This work investigates how to monitor access to Android zero-permission sensors which could cause privacy leakage to users. Moreover, monitoring such sensitive access allows security researchers to characterize potential sensor abuse patterns. Zero-permission sensors such as accelerometers have become an indispensable part of Android devices. The critical information they provide has attracted extensive research investigating how data collectors could capture more sensor data to enable both benign and exploitative applications. In contrast, little work has explored how to enable data providers, such as end users, to understand sensor usage. While existing methods such as static analysis and hooking-based dynamic analysis face challenges of requiring complicated development chains, rooting privilege, and app-specific reverse engineering analysis, our work aims to bridge this gap by developing ARMOUR for user-space runtime monitoring, leveraging the intrinsic sampling rate variation and convergence behaviors of Android. ARMOUR enables privacy-aware users to easily monitor how third-party apps use sensor data and support security researchers to perform rapid app-agnostic sensor access analysis. Our evaluation with 1,448 commercial applications shows the effectiveness of ARMOUR in detecting sensor usage in obfuscated code and other conditions, and observes salient sensor abuse patterns such as 50% of apps from seemingly sensor-independent categories accessing data of multiple zero-permission sensors. We analyze the impact of Android's recent policy changes on zero-permission sensors and remaining technical and regulatory problems.

Despite the increasing popularity of voice assistants such as Amazon Alexa, the security implications of dynamic skill content (content modifiable without resubmission) in voice assistant skills (voice-activated applications) remain largely unexplored. This paper presents the first large-scale analysis of Alexa's dynamic content ecosystem using D-Explorer, a ChatGPT powered chatbot. From a dataset of 10,407 skill interactions, we investigate: 1) the mechanisms of Alexa dynamic content, 2) the associated security risks, and 3) the prevalence of these risks in published skills. Our analysis reveals that 34% of skills contain dynamic content in interactions, 95% access external resources (increasing attack vectors), 7% of skill conversations exhibit problematic (potentially harmful or privacy-infringing) interactions related to dynamic content, and 90% of skills connect to a potentially vulnerable dynamic resource during interaction. These findings expose significant vulnerabilities, highlighting the critical need for stricter developer rules and security measures to prevent unpredictable, harmful, and privacy compromising interactions within the Alexa skill ecosystem.

IMSI catchers are surveillance tools that intercept cellular signals to capture user identifiers, such as the IMSI. By imitating a legitimate BS and compelling phones to connect via jamming or stronger signal, they cause temporary disconnection, risking service disruption and raising suspicion, which limits their covert effectiveness. This paper presents FlashCatch, an IMSI-catching approach that significantly accelerates the two primary factors leading to service disruption. First, by exploiting 3GPP standard vulnerabilities, FlashCatch reduces IMSI capture time by over 200 times. Second, it improves the detachment phase through intentional authentication failures, inducing a rapid switch back to a legitimate cell and barring the FBS cell from further reconnection, thus reducing overall noticeable service disruption by at least 50-fold. Laboratory experiments on 7 devices featuring basebands from three different manufacturers demonstrate sub-second IMSI retrieval with seamless service continuity, while dedicated VoLTE and VoIP experiments confirm that ongoing calls resume without termination after a transient small disruption. In-the-wild field tests with 50 volunteers further validate its stealth and operational effectiveness. Moreover, FlashCatch preserves the UE's security context, retaining temporary identifiers that enable linkability attacks for subsequent tracking.

In recent years, we have witnessed a remarkable proliferation of Internet of Things (IoT) devices, which are quickly penetrating into almost every industry and making tremendous impacts on the national economy and the entire society. However, security and privacy remain a fundamental hurdle in the collection, transmission, and processing of IoT data. This work focuses on privacy-preserved data access that is critical for implementing and exploiting the full potential of future IoT. This work represents the first endeavor to develop practical Compact Inner Product Encryption (C-IPE) aiming to achieve privacy-preserved data access in IoTs. We propose a practical scheme that provides effective, fine-grained, and privacy-preserved access to IoT data while at the same time, is computationally efficient for practical deployment on resource-constrained IoT devices to preserve the designers of energy-efficient embedded systems and applications a balance between performance, power, security, and cost-effectiveness. We also analyze the efficiency of C-IPE. Compared with the original IPE, the key size is reduced from n + 1 to a small constant; the ciphertext size is reduced by half, i.e., from 2n + 2 to n + 1; and the decryption effectively avoids the high cost of cryptographic pairing. These salient properties result in high efficiency in computation and storage, making C-IPE well-suited for IoT applications. To demonstrate the practicality of our scheme, we implement C-IPE in three representative privacy-preserving applications: privacy-preserved attribute matching, distance matching, and linear regression in IoT settings. We carry out extensive experiments on four platforms, i.e., Dell workstation, Raspberry Pi 3 with an ARM Cortex processor, Samsung Galaxy 7, and ultra-low-power Arduino Nano 33 micro-controller using 32-bit ARM Cortex-M0 CPU with 256KB Flash and 16KB RAM. The experimental results demonstrate significant improvements over existing IPE schemes, supported by detailed numerical evidence and comparative figures across practical application settings.

Signal injection attacks pose a serious threat to systems that rely on sensor information to determine their behavior. Using such an attack, an attacker can remotely manipulate the values of a sensor by transmitting appropriately formed RF signals that induce a current in the sensor wires. For example, to manipulate the temperature sensor in a battery management system, to trigger thermal protection, and shut down the battery. While several defense mechanisms have been proposed, they all need additional hardware to work. In this paper, we present RIPPLE, a fully software-based detection mechanism that can reliably detect signal injection attacks against drone sensor systems. A software-only solution is a practical way to add protection to an existing fleet of drones, and it is a cost effective alternative to the existing proposals for new drones. Our detection mechanism exploits a physical layer property known as small-scale (fast) fading, which causes the wireless channel between the attacker and drone to change unpredictably. As a result, the power induced by the attacker's transmission will oscillate rapidly, whenever the drone is in motion. We show for the first time that this effect occurs even with extremely minimal motion, such as a drone hovering in place on a calm, windless day. This oscillation is used as the basis of our detection system. We conduct an in-depth evaluation of RIPPLE on drones in several different environments. Our results show that RIPPLE reliably detects signal injection attacks. Even for weak attacks, changing the temperature by as little as 2°C, and with a drone movement of only a few millimeters, we have a success rate of over 98%. The performance only improves with stronger attack signals or more movement.

Motivated by the growing prevalence of increasingly advanced satellite jamming attacks, we introduce and systematically analyze protocol-aware jammers: the worst-case scenario that maximally exploits the protocol to deny service whilst remaining as difficult to detect as possible. This extends existing satellite jamming and anti-jamming literature, which to date considers only conventional jamming waveforms. We find that protocol-aware jammers are significantly more effective than conventional jammers against all major standardized satellite protocols, including when anti-jamming countermeasures in the form of interleaving and adaptive coding and modulation are employed. This performance is possible since current protocols have a cyclic and predictable nature. We assess the required capabilities in terms of synchronization, and show that many of these performance gains can be realized even by completely desynchronized jammers. We experimentally evaluate protocol-aware strategies against both a hardware and software receiver. The results show that over 15dB of performance gains over Gaussian jamming are possible against all tested satellite protocols. Furthermore, we find that the attack can be optimized in simulation and deployed against the hardware receiver without performance degradation. We conclude with a discussion of countermeasures, primarily at the protocol level, to improve the availability of these systems.

The rapid evolution of millimeter-wave radar sensing technology has given rise to a proliferation of open-source radar datasets, creating an urgent need for innovative digital copyright protection techniques. However, conventional image and audio watermarking techniques are inadequate for radar copyright protection due to radar signals' sparsity, vulnerability and complexity. In this paper, we present RFinger, an ownership verification framework for static indoor millimeter-wave radar datasets. Our approach encodes environmental information extracted from radar signals into digital watermarks, strategically embedding these within carefully selected data frames to establish robust verification credentials. We develop statistical hypothesis testing metrics to detect unauthorized access to RFinger-protected data in black-box setting. Our strategic watermark design ensures that the unauthorized models exhibit distinctly anomalous performance on verification data compared to legitimate models. Through experiments on two large millimeter-wave radar datasets, we have validated that our designed strategy provides high watermark retrieval accuracy without compromising downstream tasks.

Many computing systems need to be protected against physical attacks using active tamper detection based on sensors. One technical solution is to employ an Anti-Tamper Radio (ATR) approach, analyzing the radio wave propagation effects within a protected device to detect unauthorized physical alterations. However, ATR systems face key challenges in terms of susceptibility to signal manipulation attacks, limited reliability due to environmental noise, and regulatory constraints from wide bandwidth usage.

In this work, we propose and experimentally evaluate an ATR system complemented by a Reconfigurable Intelligent Surface (RIS) to dynamically reconfigure the wireless propagation environment. We show that this approach can enhance resistance against signal manipulation attacks, reduce bandwidth requirements from several GHz down to as low as 20 MHz, and improve robustness to environmental disturbances such as internal fan movements.

Our work demonstrates that RIS integration can strengthen the ATR performance to enhance security, sensitivity, and robustness, recognizing the potential of smart radio environments for ATR-based tamper detection.

Ensuring user privacy remains a critical concern within mobile cellular networks, particularly given the proliferation of interconnected devices and services. In fact, a lot of user privacy issues have been raised in 2G, 3G, and 4G networks. Acknowledging these concerns, 3GPP has prioritized addressing these issues in the development of 5G, implementing numerous modifications to enhance user privacy since 5G Release 15. In this systematization of knowledge paper, we first provide a framework for studying privacy and security related attacks in cellular networks, setting as privacy objective the User Identity Confidentiality defined in 3GPP standards. Using this framework, we discuss existing privacy and security attacks in pre-5G networks, analyzing the weaknesses that lead to these attacks. Furthermore, we thoroughly study the security characteristics of 5G up to the latest 5G-Advanced Release 19, and examine mitigation mechanisms of 5G to the identified pre-5G attacks. Afterwards, we analyze how recent 5G attacks try to overcome these mitigation mechanisms. Finally, we identify current limitations and open problems in security of 5G, and propose directions for future work.

Ultrasound and near-ultrasound acoustic frequencies offer non-intrusive and low-overhead mediums for data transmission protocols. These protocols and the technologies built upon them are becoming more prevalent, yet their security remains largely unexplored. We present the first systematization of ultrasound and near-ultrasound enabled applications, developing a unified threat model to address their security. Our analysis reveals misguided assumptions, missing protections, and the need for standardization. Through our taxonomy, we highlight key insights, future research directions, and propose a framework for securing these protocols.

We present a study that enhances wireless security research through a systematic comparison of high- and low-cost equipment for experiment reproduction. Our study evaluates wireless power transfer security by replicating experiments in both a controlled anechoic chamber and an uncontrolled Radio Frequency (RF) lab.

We examine the growing security and privacy concerns associated with the widely used Qi wireless charging protocol. Prior research has identified critical vulnerabilities, including device tracking and the detection of active applications during charging. Addressing these risks requires a comprehensive study of the Qi standard and other emerging wireless power protocols to identify and mitigate potential threats.

This study assesses the effectiveness of high-cost versus low-cost research setups, benchmarking their performance. Our findings suggest that in most cases, a low-cost approach is sufficient for wireless security research, offering a viable alternative without compromising experimental integrity.

In 2021, researchers disclosed vulnerabilities in the IEEE 802.11 standard related to frame fragmentation and aggregation, also known as the FragAttacks. In this paper, we design novel methods to measure whether real-world Wi-Fi networks are still affected by these vulnerabilities. Using our methods, we conducted surveys in three cities at two points in time (2023 and 2025) and found many networks still vulnerable. Concretely, we detected 52691 networks, found that in one city, 30% are still affected by one of the FragAttacks, and that for some ISPs, nearly all their routers are still affected. Motivated by this, we also present a design flaw in the 802.11 standard's defense against one of these vulnerabilities.

As 5G and future generations of mobile networks aim to provide faster and more secure wireless connections, 5G broadcast messages remain unprotected. Hence, a user device cannot verify the identity of a base station before establishing the connection and starting the registration procedure. This long-existing loophole enables various types of fake base station (FBS) attacks. To protect end-users from these attacks, a practical solution is to introduce a digital signature for these broadcast messages. However, an FBS may also have the ability to relay a digitally signed broadcast message from a benign base station to bypass the protection. Considering that a relayed message needs extra time to reach a user device, a time-bounded defense mechanism can be used on top of the digital signature to offer replay protection. Although previous work proposed such a solution, none have implemented it or evaluated it against relay attacks. Hence, to evaluate the performance of our proposed digital signature scheme and the time-bounded defense, we implemented the solution against relay attacks using an open-source 5G system. Our results show that the overhead introduced is acceptable and that the time-bounded defense is effective against relay attacks.

Cellular baseband processors represent critical security components in modern mobile devices, yet they remain challenging to analyze due to their complexity and restricted access. Recent advances in baseband research introduced FirmWire, the state-of-the-art emulator enabling full-system baseband emulation with extensive features debugging capabilities. However, it lacks protocol state awareness, significantly limiting its coverage and fidenlity. While implementing such support demands substantial engineering effort, accurately modeling protocol states remains essential for comprehensive baseband security analysis. In this paper, we present FirmState, a state-aware methodology that augments baseband emulation, specifically targeting Samsung Shannon baseband. FirmState semi-automatically recovers and applies state information extracted from physical devices during actual network communication, enabling more complete code coverage and authentic behavior reproduction without extensive reverse engineering. Our evaluation demonstrates a significant improvement in code coverage, achieving 7.5% for RRC--2.7× higher than previous work. Additionally, our system newly supports NAS over FirmWire, with code coverage ranging from 4.5% to 9.2%, depending on the protocol state. Using our approach, we discovered and analyzed two 1-day vulnerabilities in Samsung's baseband implementation, demonstrating FirmState's effectiveness for baseband security. We make FirmState open-source to support further research in baseband security.

Prior security analyses of 3GPP systems primarily focus on upper layers of the stack. Unfortunately, the physical and MAC layers are not as thoroughly analyzed, even though they are neither encrypted nor integrity protected. Furthermore, the latest 5G releases significantly increase the number of low-layer control messages and procedures. We conduct a systematic vulnerability analysis of these low layers, and demonstrate that current cellular systems are susceptible to passive attacks, and active spoofing of PHY/MAC messages. For instance, we find that sniffing beamforming information enables fingerprinting-based localization and tracking of users. We also show that signal spoofing is possible in 5G NR, and more efficient compared to LTE networks. We also evaluate active attacks against COTS UEs, showing it is possible to disrupt user communications by tricking connected UEs into acting as jammers, or by stealthily disconnecting active users. In our experiments we achieve user localization within 20-meters 96% of the time, user path tracking within 15 meters for 81% of the paths, and throughput reduction by over 95% within 2 seconds (by spoofing a 39-bit DCI).

LoRaWAN has become a widely adopted, cost-effective solution for Low-Power Wide-Area Networks (LPWANs), bridging the gap between short-range wireless protocols and high-power cellular networks. Its affordable hardware and robust physical layer make it a key enabler for Internet of Things (IoT) applications across sectors like agriculture, smart cities, and industrial automation-domains where security is of central importance. In this paper, we present BlindSpot, a novel jamming attack that enables efficient selective jamming of LoRaWAN gateways. Unlike traditional approaches that rely on creating high-power interference, BlindSpot exploits the limited number of demodulation paths in LoRaWAN gateways to continuously occupy the gateways with fabricated frames, blinding them for any other legitimate transmissions. Compared to existing approaches, this reduces the attacker's power requirements and allows them to decode the legitimate transmissions with a high probability. Selectively retransmitting these frames, the attacker has precise control over which transmissions can be decoded by the gateway. Using a Software-Defined Radio (SDR)-based LoRa transceiver, we demonstrate the effectiveness of the attack against commercial LoRaWAN gateways and propose detection and mitigation strategies to improve the security of LoRaWAN deployments.

In contrast to its predecessors, 5G supports a wide range of commercial, industrial, and critical infrastructure scenarios. One key feature of 5G, ultra-reliable low latency communication, is particularly appealing to such scenarios for its real-time capabilities. However, 5G's enhanced security, mostly realized through optional security controls, imposes additional overhead on the network performance, potentially hindering its real-time capabilities. To better assess this impact and guide operators in choosing between different options, we measure the latency overhead of IPsec when applied over the N3 and the service based interfaces to protect user and control plane data, respectively. Furthermore, we evaluate whether WireGuard constitutes an alternative to reduce this overhead. Our findings show that IPsec, if configured correctly, has minimal latency impact and thus is a prime candidate to secure real-time critical scenarios.

As spoofing attacks on GNSS-based aircraft navigation systems become more common in commercial aviation, independent local- ization methods such as ground-based distributed multilateration are increasingly being adopted for enhanced safety. While previous work has suggested these systems may be susceptible to multi- device spoofing, no successful real-world multilateration spoofing attacks have been documented so far. In this study, we examined the feasibility and potential impact of wireless spoofing on two deployed commercial multilateration systems. Our findings reveal that these systems share vulnerabilities with GNSS-based solutions such as ADS-B, although considerably greater effort is required for a successful attack. Using a testbed with a reception range ex- ceeding 300 km, we evaluated the requirements and constraints for executing such attacks and compared the efficacy of ghost injection, flooding, and trajectory manipulation tactics. These insights can help inform measures to secure existing multilateration systems.

Radiometric fingerprinting is a promising passive security measure for low-power IoT devices, that exploits the hardware imperfections of their radio signals. In this paper, we focus on extracting radiometric fingerprints from Bluetooth Low Energy (BLE) devices. We depart from previous work in that we facilitate fingerprinting in embedded network scenarios by extracting features from I/Q samples collected using a widely used BLE System-on-Chip. We introduce a novel approach that leverages instantaneous frequency analysis of signal dynamics to reveal hardware imperfections during symbol transitions in the packet payload. Our objective is to identify the most significant features contributing to device identification. Our experimental evaluation demonstrates that augmenting traditional aggregated FFT-based features with our proposed transition-based features increases identification accuracy from 56% to 74%.

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.

The proliferation of propaganda on mobile platforms raises critical concerns around detection accuracy and user privacy. To address this, we propose TRIDENT -a three-tier propaganda detection model implementing transformers, adversarial learning, and differential privacy which integrates syntactic obfuscation and label perturbation to mitigate privacy leakage while maintaining propaganda detection accuracy. TRIDENT leverages multilingual back-translation to introduce semantic variance, character-level noise, and entity obfuscation for differential privacy enforcement, and combines these techniques into a unified defense mechanism. Using a binary propaganda classification dataset, baseline transformer models (BERT, GPT-2) we achieved F1 scores of 0.89 and 0.90. Applying TRIDENT's third-tier defense yields a reduced but effective cumulative F1 of 0.83, demonstrating strong privacy protection across mobile ML deployments with minimal degradation.

SIM tracing - the ability to inspect, modify, and relay communication between a SIM card and modem - has become a significant technique in cellular network research. It enables essential security- and development-related applications such as fuzzing communication interfaces, extracting session keys, monitoring hidden SIM activity (e.g., proactive SIM commands or over-the-air updates), and facilitating scalable, distributed measurement platforms through SIM reuse. Traditionally, achieving these capabilities has relied on specialized hardware, which can pose financial and logistical burdens for researchers, particularly those new to the field.

In this work, we show that full SIM tracing functionality can be achieved using only simple, widely available components, such as UART interfaces and GPIO ports. We port these capabilities to low-cost microcontrollers, exemplified by the Raspberry Pi Pico (4 USD). Unlike other approaches, it dramatically reduces hardware complexity by electrically decoupling the SIM and the modem and only transferring on APDU level.

By significantly reducing hardware requirements and associated costs, we aim to make SIM tracing techniques accessible to a broader community of researchers and hobbyists, fostering wider exploration and experimentation in cellular network research.

The rapid expansion of wireless IoT devices in domains such as smart homes, healthcare, and critical infrastructure has heightened concerns about data privacy, especially as many devices continue to access cloud services without encryption. Private Information Retrieval (PIR) protocols offer a way to protect query privacy by hiding the accessed record index, but most existing approaches rely on computationally intensive operations that exceed the capabilities of low-power IoT hardware. We present VeilPIR, a lightweight single-server PIR protocol designed specifically for resource-constrained devices connected over wireless links like BLE and Wi-Fi. VeilPIR shifts the heavy cryptographic workload to the server, allowing clients to send small encrypted queries and receive constant-size responses that preserve semantic privacy. We implemented VeilPIR in Python using Flask and deployed it to simulate realistic IoT conditions. Evaluation on a client and a cloud server shows 900 ms round-trip for 1,000 records and <1 J energy per query, while still allowing live record insertions and deletions without full re-encryption.

Smishing, or SMS-based phishing, poses an increasing threat to mobile users by mimicking legitimate communications through culturally adapted, concise, and deceptive messages, which can result in the loss of sensitive data or financial resources. In such, we present a multi-channel smishing detection model that combines country-specific semantic tagging, structural pattern tagging, character-level stylistic cues, and contextual phrase embeddings. We curated and relabeled over 84,000 messages across five datasets, including 24,086 smishing samples. Our unified architecture achieves 97.89% accuracy, an F1 score of 0.963, and an AUC of 99.73%, outperforming single-stream models by capturing diverse linguistic and structural cues. This work demonstrates the effectiveness of multi-signal learning in robust and region-aware phishing.

Bluetooth Low Energy (BLE) is increasingly prevalent in IoT networks, particularly in high-risk environments such as medical IoT. However, existing security research in BLE often focuses on isolated device types or controlled environments, overlooking the complexities of real-world BLE interactions, including environmental noise and the heterogeneous nature of devices. This paper proposes a multilayered framework for BLE data extraction designed to capture, process, and correlate data from diverse sources to construct comprehensive behavioral signatures. Our approach explores traffic analysis and introduces the potential of spectrum analysis to identify malicious activity beyond conventional packet-level monitoring. By identifying key behavioral markers and deviations from expected norms, this approach enhances BLE anomaly detection and strengthens the security of IoT networks.

A Diameter firewall is essential for mobile operators to protect their subscribers, as Diameter networks lack end-to-end authentication and heavily rely on trust among partner operators. Among the various attacks exploiting the Diameter protocol, one notable example involves falsely updating a subscriber's location and illegitimately requesting authentication data. Conventional methods, known as velocity checks, validate the originating network of messages by assessing the plausibility of subscriber movement based solely on network-provided information. However, attackers can still bypass these checks by simulating their presence in nearby or bordering countries. This poster introduces a user-interactive Diameter firewall, which incorporates user engagement by allowing subscribers to confirm suspicious requests.

The transition to Open Radio Access Networks (O-RAN) introduces testing challenges due to multi-vendor interoperability requirements, with existing manual frameworks being error-prone and unscalable. To address this, we propose AI5GTest, an AI-driven framework that automates O-RAN component testing using cooperative Large Language Models (LLMs). Gen-LLM generates test flows from O-RAN/3GPP specifications, Val-LLM validates signaling compliance, and Debug-LLM diagnoses failures. A human-in-the-loop mechanism ensures transparency by verifying specifications before validation. Evaluated on 24 test cases with the srsRAN 5G stack, AI5GTest reduces test execution time significantly compared to manual methods while maintaining high accuracy, demonstrating scalable, trustworthy automation for O-RAN ecosystems.

LoRaWAN has become a widely adopted, cost-effective solution for Low-Power Wide-Area Networks (LPWANs), bridging the gap between short-range wireless protocols and high-power cellular networks. Its affordable hardware and robust physical layer make it a key enabler for Internet of Things (IoT) applications across sectors like agriculture, smart cities, and industrial automation-domains where security is of central importance. Investigating LoRaWAN's resilience against physical-layer attacks, we developed BlindSpot, a novel jamming attack targeting state-of-the-art LoRaWAN gateways. Unlike traditional jammers, BlindSpot does not rely on overpowering transmissions but prevents their reception by exhausting resources at the gateway. This is possible since commercial gateway processors have only a limited number of demodulators for parallel frame reception. In this demo, we show the effectiveness of the attack against a commercial LoRaWAN gateway, compare it to traditional jamming, and show how our Software-Defined Radio (SDR)-based receiver can overcome the attack.

Wireless eavesdropping on phone conversations has become a major security concern as attackers repurpose advanced wireless capabilities in 5G and beyond featuring higher frequencies and higher sensing resolution. Recent studies have demonstrated that attackers can exploit off-the-shelf millimeter-wave radars to covertly detect even micron-scale vibrations of smartphones caused by the earpiece during the phone conversation, eavesdropping on audio information without the victim ever noticing. In our IEEE S&P'25 paper, we present a new architecture that not only thwarts such attacks but also injects false signatures to fool eavesdroppers into believing they have succeeded. Here, we demonstrate the eavesdropping countermeasure technique that enables the user to hide his private acoustic signals and simultaneously inject an alternative signal via a low-profile, reconfigurable metasurface. We present a metasurface-based audio encoding method that generates artificial audio-vibration signatures to send deceptive audio information to eavesdroppers. We showcase experimental audio samples from both the attack and the proposed countermeasure, which transforms defensive strategies from merely reactive to proactively deceptive.

While the disaggregated architecture of the industry-driven Open Radio Access Network (O-RAN) promises to foster vendor competition, accelerate innovation, and reduce cost for 5G/6G cellular network deployments, it also exposes the cellular network to various new cybersecurity and privacy vulnerabilities. This demo paper highlights one such new potential cybersecurity vulnerability in the Radio Unit (RU) of O-RAN networks, where an adversary can infer RU activity by analyzing electromagnetic side-channel emissions. We present a custom-built, open-source cellular O-RAN testbed equipped with EM measurement capabilities that enables direct observation of the FPGA-based RU during operation. By capturing EM emissions from the RU, we extract side-channel traces that reveal the underlying RU activity. These traces are then analyzed using a Random Forest-based machine learning classifier, which accurately distinguishes between different RU activity patterns. Our preliminary findings demonstrate the feasibility of inferring RU-level operations via passive EM observation, highlighting a previously unexplored security threat in O-RAN systems. All code and experimental artifacts are made publicly available at https://github.com/SPIRE-GMU/NextGRadio_Sidechanel.