The field of autonomous systems is rapidly evolving, with a growing focus on security and vulnerability assessment. Recent developments have highlighted the importance of considering multi-task and cross-task attacks, which can compromise the integrity of autonomous driving systems. Researchers are exploring innovative approaches to adversarial scenario generation, including steerable and preference-aligned methods, to improve the efficiency and flexibility of safety assessments. Furthermore, the development of universal camouflage attacks and physical distance-pulling attacks has significant implications for the security of autonomous target tracking systems. Noteworthy papers in this area include: BiTAA, which introduces a bi-task adversarial attack for object detection and depth estimation via 3D Gaussian Splatting, demonstrating practical risks for multi-task camera-only perception. SAGE, which enables fine-grained test-time control over the trade-off between adversariality and realism in adversarial scenario generation, providing a more effective approach to safety assessment. UCA, which proposes a universal camouflage attack framework for vision-language models in autonomous driving, exhibiting strong generalization across different user commands and model architectures. FlyTrap, which presents a novel physical-world attack framework that exploits vulnerabilities in autonomous target tracking systems, highlighting urgent security risks and practical implications for safe deployment.