Task Attack

Task attack research focuses on exploiting vulnerabilities in machine learning models, particularly those handling multiple tasks or trained incrementally. Current efforts concentrate on developing sophisticated adversarial attacks, including multi-task and cross-task methods, that can manipulate models through data poisoning or the generation of realistic adversarial examples, often leveraging generative adversarial networks (GANs) or attention mechanisms. This research is crucial for understanding and mitigating security risks in increasingly complex AI systems, impacting the robustness and trustworthiness of applications across various domains. The development of effective defenses against these attacks is a parallel and equally important area of investigation.