The advent and growing popularity of Virtual Reality (VR) and Mixed Reality (MR) solutions have revolutionized the way we interact with digital platforms. The cutting-edge gaze-controlled typing methods, now prevalent in high-end models of these devices, e.g., Apple Vision Pro, have not only improved user experience but also mitigated traditional keystroke inference attacks that relied on hand gestures, head movements and acoustic side-channels. However, this advancement has paradoxically given birth to a new, potentially more insidious cyber threat, GAZEploit . In this paper, we unveil GAZEploit , a novel eye-tracking based attack specifically designed to exploit these eye-tracking information by leveraging the common use of virtual appearances in VR applications. This widespread usage significantly enhances the practicality and feasibility of our attack compared to existing methods. GAZEploit takes advantage of this vulnerability to remotely extract gaze estimations and steal sensitive keystroke information across various typing scenarios-including messages, passwords, URLs, emails, and passcodes. Our research, involving 30 participants, achieved over 80% accuracy in keystroke inference. Alarmingly, our study also identified over 15 top-rated apps in the Apple Store as vulnerable to the GAZEploit attack, emphasizing the urgent need for bolstered security measures for this state-of-the-art VR/MR text entry method.

The ubiquitous presence of printed circuit boards (PCBs) in modern electronic systems and embedded devices makes their integrity a top security concern. To take advantage of the economies of scale, today’s PCB design and manufacturing are often performed by suppliers around the globe, exposing them to many security vulnerabilities along the segmented PCB supply chain. Moreover, the increasing complexity of the PCB designs also leaves ample room for numerous sneaky board-level attacks to be implemented throughout each stage of a PCB’s lifetime, threatening many electronic devices. In this paper, we propose PDNPulse , a power delivery network (PDN) based PCB anomaly detection framework that can identify a wide spectrum of board-level malicious modifications. PDNPulse leverages the fact that the PDN’s characteristics are inevitably affected by modifications to the PCB. By detecting changes to the PDN impedance profile against the golden model and using the Frechet distance-based anomaly detection algorithms, PDNPulse can robustly and successfully discern malicious modifications across the system. Using PDNPulse, we conduct extensive experiments on seven commercial-off-the-shelf PCBs, covering different design scales, different threat models, and seven different anomaly types. The results confirm that PDNPulse creates an effective security asymmetry between attack and defense.

Deep learning (DL)-based side-channel analysis (SCA) has posed a severe challenge to the security and privacy of embedded devices. During its execution, an attacker exploits physical SCA leakages collected from profiling devices to create a DL model for recovering secret information from victim devices. Despite this success, recent works have demonstrated that certain countermeasures, such as random delay interrupts or clock jitters, would make these attacks more complex and less practical in real-world scenarios. To address this challenge, we present a novel denoising scheme that exploits the U-Net model to pre-process SCA traces for “noises” (i.e., countermeasures) removal. Specifically, we first pre-train the U-Net model on the paired noisy-clean profiling traces to obtain suitable parameters. This model is then fine-tuned on the noisy-only traces collected from the attacking device. The well-trained model will be finally deployed on the attacking device to remove the noises (i.e., countermeasures) from the measured power traces. In particular, a new inductive transfer learning method is also utilized in our scheme to transfer knowledge learned from the source domain (i.e., profiling device) to the target domain (i.e., attacking device) to improve the model’s generalization ability. During our experimental evaluations, we conduct a detailed analysis of various countermeasures separately or combined and show that the proposed denoising model outperforms current state-of-the-art work by a large margin, e.g., a reduction of at least 30% in computation costs and 5× in guessing entropy.

Trusted execution environments (TEE) are deployed on many platforms to provide both confidentiality and integrity, and their extensive use offers a secure environment for privacy-sensitive operations. Despite TEE prevalence in the smartphone and tablet market, vulnerability research into TEE security is relatively rare. This is, in part, due to the strong isolation guarantees provided by its implementation. In this paper, we propose a hardware assisted fuzzing framework, CROWBAR, that bypasses TEE isolation to natively evaluate trusted applications (TAs) on mobile devices by leveraging ARM CoreSight components. CROWBAR performs feedback-driven fuzzing on commercial, closed source TAs while running in a TEE protected environment. We implement CROWBAR on 2 prototype commercial-off-the-shelf (COTS) smartphones and one development board, finding 3 unique crashes in 5 closed source TAs that are previously unreported in the TrustZone fuzzing literature.

The rise of connected and automated vehicles has transformed in-vehicle infotainment (IVI) systems into critical gateways linking user interfaces, vehicular networks, and cloud-based fleet services. A concerning architectural reality is that hardcoded credentials like access point names (APNs) in IVI firmware create a cross-layer attack surface where local exposure can escalate into entire vehicle fleets being remotely compromised. To address this risk, we propose a cross-layer security framework that integrates firmware extraction, symbolic execution, and targeted fuzzing to reconstruct authentic IVI-to-backend interactions and uncover high-impact web vulnerabilities such as server-side request forgery (SSRF) and broken access control. Applied across seven diverse automotive systems, including major original equipment manufacturers (OEMs) (Mercedes-Benz, Tesla, SAIC, FAW-VW, Denza), Tier-1 supplier Bosch, and advanced driver assistance systems (ADAS) vendor Minieye, our approach exposes systemic anti-patterns and demonstrates a fully realized exploit that enables remote control of approximately six million Mercedes-Benz vehicles. All 23 discovered vulnerabilities, including seven CVEs, were patched within one month. In closed automotive ecosystems, we argue that the true measure of efficacy lies not in maximizing code coverage but in discovering actionable, fleet-wide attack paths, which is precisely what our approach delivers.