The recent advancements in adversarial attacks and defenses for neural networks, particularly in the context of Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL), have seen significant innovations. Researchers are increasingly focusing on developing methods to protect models from backdoor attacks, where malicious triggers can manipulate model behavior without altering the model's overall performance. Notably, there is a shift towards creating defenses that are robust against both out-of-distribution (OOD) and in-distribution (ID) backdoor attacks, with a particular emphasis on maintaining high model accuracy while effectively mitigating these threats. Additionally, the field is witnessing the emergence of novel attack strategies that minimize the need for large-scale data poisoning or extensive computational resources, making them more practical and dangerous. These developments highlight the ongoing arms race between attackers and defenders, pushing the boundaries of what is possible in both securing and compromising neural network models.

Noteworthy papers include one that introduces a backdoor attack framework specifically designed for Graph Prompt Learning (GPL), demonstrating high attack success rates without modifying pre-trained GNN encoders. Another paper proposes a new class of backdoor attacks against DRL that achieve state-of-the-art performance by minimally altering the agent's rewards, showcasing a sophisticated approach to adversarial behavior induction.