The paper introduces a stealthy, scenario-realistic data fabrication attack that subtly manipulates object poses in shared perception data to induce unsafe driving behaviors in connected and autonomou…

This paper systematically analyzes 48 studies on perception attacks against autonomous vehicles, revealing that the increasing reliance on multi-sensor fusion creates new, complex vulnerabilities that…

The paper demonstrates a coordinated, cross-modal spoofing attack that successfully deceives state-of-the-art multi-sensor fusion systems in autonomous vehicles by making multiple sensors agree on a f…

The paper introduces a novel adversarial attack that uses static, view-dependent camouflage on a vehicle to induce consistent feature drift, causing autonomous systems to predict false, yet plausible,…

The paper proposes a proactive, resilient architecture for autonomous vehicles by integrating redundancy, diversity, and adaptive reconfiguration to defend against various cyber and physical attacks.

The SAFE approach enhances fault-tolerant trust management in VANETs by ensuring vehicles send updated feedback reports before leaving a witness area, significantly reducing erroneous penalization of…

FedTrident proposes a comprehensive framework to defend Federated Learning-based Road Condition Classification against Targeted Label-Flipping Attacks, achieving robust performance comparable to non-a…

The paper proposes IPEK, a context-aware trust mechanism for VANETs, which significantly improves detection of intelligent attackers by incorporating event and location severity into trust calculation…

This paper demonstrates that reasoning-enabled Vision-Language-Action (VLA) models for autonomous driving are highly vulnerable to realistic input perturbations, significantly compromising both reason…

This paper proposes a cloud-edge-end collaborative defense framework to secure UAV swarms against various threats like GPS spoofing and multi-hop intrusions, demonstrating its effectiveness through ex…

The paper introduces a threat-oriented digital twinning methodology to enable reproducible and controllable cybersecurity evaluation of autonomous platforms, overcoming limitations in accessing real-w…

This paper provides a comprehensive review of network optimization aspects for Connected and Autonomous Vehicles (CAVs), aiming to clarify misconceptions and outline future research directions.

The paper demonstrates that current safety probes designed to detect deceptive AI fail when the model adopts a coherent misalignment, where the model genuinely believes its harmful behavior is virtuou…

The paper introduces the Street-legal Physical Adversarial Rim (SPAR), a physically realizable and street-legal white-box attack that significantly degrades the accuracy of modern Automatic License Pl…

This paper systematically analyzes the high cross-architecture transferability of physical adversarial attacks on Vision-Language Models (VLMs) used in autonomous driving, demonstrating that attacks e…

The paper develops a trust-aware framework to model how connected vehicles adapt their routing decisions and overall traffic flow when exposed to misinformation, showing that endogenous trust provides…

This survey provides a comprehensive, practical guide to ensuring the trustworthiness of complex, autonomous agentic AI systems by focusing on safety, robustness, privacy, and system security.

The paper demonstrates that current defenses against malicious fine-tuning of foundation models are insufficient because they only address fixed attacks, and introduces a unified adaptive attack that…

The paper identifies the Misattribution Gap, showing that memory-layer attacks (Semantic Norm Drift) can mimic model failure in multi-agent AI systems, and proposes novel detection and mitigation tech…

The paper identifies a critical vulnerability, the Camouflage Detection Gap (CDG), where standard LLM injection detectors fail dramatically when malicious payloads mimic the target domain's language a…