The integration of microphones into a broad array of devices, from consumer electronics to industrial sensors, introduces vulnerabilities to out-of-band injection attacks, including ultrasound, laser, electromagnetic, and magnetic field attacks. These attacks enable adversaries to inject inaudible or imperceptible commands, compromising systems without direct physical access. This paper presents a robust, passive detection framework designed to address the full spectrum of out-of-band attacks on microphone-equipped devices. Unlike prior approaches, our system leverages advanced speech disentanglement to separate semantic and acoustic features from recorded audio, enabling a refined analysis of injection artifacts within each feature domain. By quantifying entropy-based chaos within the disen tangled representations, we detect subtle spectral and structural irregularities indicative of injected signals. The system further incorporates a preliminary stage to identify carrier traces where applicable, expediting detection in cases such as ultrasound and laser attacks. Extensive evaluations across various device types, including smartphones, tablets, and microphones, demonstrate the system's high accuracy and stability, achieving an AUC of 98% under diverse conditions and attack configurations.

LiDAR sensors measure the environment by emitting lasers and, when combined with deep neural networks (DNNs), can effectively identify surrounding obstacles such as vehicles and pedestrians. Given its crucial role in autonomous driving perception, the security of LiDAR is closely tied to driving safety. Some studies have explored its vulnerabilities to physical-world attacks, such as laser-based attacks or adversarial objects. However, these methods are either extremely difficult to execute or lack stealth and flexibility. In this paper, we propose a novel attack method called EMTrig, which leverages common roadside objects combined with controlled intentional electromagnetic interference (IEMI) targeting specific LiDARs to create flexible and covert adversarial attacks against designated vehicles. This causes the victim vehicle to misidentify roadside objects as obstacles, such as other vehicles, leading to dangerous driving behaviors like sudden stops and lane changes. Unlike conventional adversarial examples, our deployed objects are common items (e.g., signboards) that are harmless without the IEMI trigger but pose a threat only under IEMI attacks, providing better stealthiness and flexibility. Extensive experiments in both digital and physical domains validate the effectiveness of EMTrig, demonstrating its significant threat to LiDAR perception.