The field of adversarial attacks is rapidly evolving, with a focus on developing more efficient and effective methods for generating adversarial examples. Recent research has explored new approaches to improving the transferability of adversarial attacks, including the use of ensemble models and meta-attack frameworks. These advances have significant implications for the robustness of AI models, particularly in high-stakes applications such as autonomous vehicles and medical imaging. Notably, the development of methods that can generate high-quality adversarial examples without requiring substantial computational resources is a key area of innovation. Some noteworthy papers in this area include: IPG, which generates adversarial patches up to 11.1 times more efficiently than existing approaches, and TAIGen, which produces adversarial examples using only 3-20 sampling steps from unconditional diffusion models. DAASH is also a notable contribution, as it introduces a fully differentiable meta-attack framework that generates effective and perceptually aligned adversarial examples.