Connected autonomous vehicles must accurately detect, and adhere, to traffic light signals to ensure safe and efficient traffic flow. Misinterpretation of traffic lights can result in potential safety issues for drivers and pedestrians. Recent work demonstrated attacks that projected structured light patterns onto vehicle cameras, causing traffic signs and traffic light color misinterpretation. In this work, we characterize a novel vulnerability of traffic light physical structures that can be exploited by attackers to deceive recognition systems. When visible and invisible laser light is projected onto traffic lights, it is scattered by its internal reflectors. To a vehicle’s camera, the reflected light appears the same as a genuine light source, resulting in dangerous red and green traffic light status misclassifications. We evaluate our attack against three state-of-the-art traffic light recognition models and show successful misclassification up to 25 m from the target traffic light. Furthermore, the attack succeeds both in daytime and nighttime conditions both in static and moving vehicle scenarios up to 10 km/h speed. To mitigate this threat, we propose a detection system based on light texture patterns that achieve 100% TPR and 1.8% FPR in our real-world scenarios.

Underwater data centers (UDCs) hold promise as next-generation data storage due to their energy efficiency and environmental sustainability benefits. While the natural cooling properties of water save power, the isolated aquatic environment and long-range sound propagation characteristics in water create unique vulnerabilities which differ from those of on-land data centers. Our research discovers the unique vulnerabilities of fault-tolerant storage devices, resource allocation software, and distributed file systems to acoustic injection attacks in UDCs. With a realistic testbed approximating UDC server operations, we empirically characterize the capabilities of acoustic injection underwater and find that an attacker can reduce fault-tolerant RAID 5 storage system throughput by 17% up to 100%. Our closed-water analyses reveal that an attacker can (i) cause unresponsiveness and automatic node removal in a distributed filesystem with only 2.4 minutes of sustained acoustic injection, (ii) induce a distributed database’s latency to increase by up to 92.7% to reduce system reliability, and (iii) induce load-balance managers to redirect up to 74% of resources to a target server to cause overload or force resource colocation. Furthermore, we perform open-water experiments in a lake and find that an attacker can cause controlled throughput degradation at the maximum allowable distance of 6.35 m using a commercial speaker. We also investigate and discuss the effectiveness of standard defenses against acoustic injection attacks. Finally, we formulate a novel machine learning-based detection system that reaches 0% False Positive Rate and 98.2% True Positive Rate trained on our dataset of profiled hard disk drives under 30-second FIO benchmark execution. With this work, we aim to help manufacturers proactively protect UDCs against acoustic injection attacks and ensure the security of subsea computing infrastructures.

Autonomous Vehicles (AVs) using LiDAR-based object detection systems are rapidly improving and becoming an increasingly viable method of transportation. While effective at perceiving the surrounding environment, these detection systems are shown to be vulnerable to attacks using lasers which can cause obstacle misclassifications or removal. These laser attacks, however, are challenging to perform, requiring precise aiming and accuracy. Our research exposes a new threat in the form of Intentional Electro-Magnetic-Interference (IEMI), which affects the time-of-flight (TOF) circuits that make up modern LiDARs. We show that these vulnerabilities can be exploited to force the AV Perception system to misdetect, misclassify objects, and perceive non-existent obstacles. We evaluate the vulnerability in three AV perception modules (PointPillars, PointRCNN, and Apollo) and show how the classification rate drops below 50%. We also analyze the impact of the IEMI injection on two fusion models (AVOD and Frustum-ConvNet) and in real-world scenarios. Finally, we discuss potential countermeasures and propose two strategies to detect signal injection.