## 1 Introduction  
Autonomous vehicles (AVs) have the potential to revolutionize transportation systems worldwide, promising to reduce traffic accidents by up to 90% and improve mobility for disabled individuals [1, 2]. These vehicles rely on an array of sensors such as RGB cameras, LiDAR, radar, GPS, and the emerging event-based dynamic vision sensor (DVS) to perceive their surroundings and navigate autonomously [3, 4, 5, 6]. However, the increasing complexity and connectivity of AV systems introduce significant cybersecurity risks that could be exploited by malicious actors [7, 8, 9, 10, 11]. The security of sensor systems is paramount, as they form the critical interface between the vehicle and its environment [12]. Recent research has demonstrated that adversaries can manipulate sensor inputs through various attack vectors, including adversarial machine learning [13, 14], sensor spoofing [9, 15], and denial-of-service attacks [8, 16], posing serious safety threats to autonomous navigation systems. Comprehensive security research has revealed multiple attack vectors targeting AV sensor systems [17; 18]. Camera-based perception systems are vulnerable to adversarial examples that can cause misidentification of traffic signs or obstacles [13; 19; 20], with studies demonstrating that carefully crafted perturbations can achieve attack success rates exceeding 85% under real-world conditions [21]. LiDAR sensors face threats from spoofing attacks that inject false obstacles, leading to unnecessary emergency braking or collision avoidance maneuvers [9; 22; 23]. GPS systems are susceptible to spoofing attacks that can cause vehicles to deviate from their intended routes by manipulating position data [9; 24; 25]. Additionally, denial-of-service attacks targeting sensor communication channels can disrupt critical perception capabilities [8; 26]. These vulnerabilities highlight the pressing need for comprehensive security analysis and robust defense strategies across all AV sensor modalities [8; 9]. While traditional sensors such as RGB cameras and LiDAR have received significant attention regarding their security vulnerabilities [15; 27], the integration of a dynamic vision sensor (DVS) into AVs introduces both new opportunities and challenges that remain largely unexplored from a cybersecurity perspective [28]. Unlike conventional frame-based cameras that capture images at fixed intervals, DVSs operate by asynchronously detecting pixel-level brightness changes with microsecond temporal resolution [29; 30]. This event-driven approach provides significant advantages, including high dynamic range (> 120 dB), reduced motion blur, and lower data redundancy [31; 32; 33], making DVSs particularly suitable for high-speed driving scenarios and challenging weather conditions where traditional cameras fail [34; 35]. However, their unique operating principles and asynchronous data streams raise critical security questions that have not been systematically addressed: How resilient are DVSs to adversarial attacks compared to traditional cameras? Can their event-based nature be exploited through novel attack vectors such as event flooding or temporal manipulation? Which defense mechanisms are most effective for securing event-based perception systems? Furthermore, how does the integration of a DVS alongside traditional sensors affect the overall security posture of autonomous vehicles? This paper conducts a comprehensive analysis of cyberattacks targeting AV sensors, placing particular emphasis on evaluating the security implications of incorporating DVS technology into multi-modal sensor suites [36]. We systematically implement and evaluate seven distinct attack vectors: salt-and-pepper noise attacks on RGB cameras [37], event flooding attacks on DVSs, depth map tampering [38], LiDAR phantom object injection [22], GPS spoofing [24; 39], denial-of-service attacks [26], and steering bias manipulation [11].