Backdoor Trigger

Backdoor attacks on machine learning models involve surreptitiously embedding triggers within training data to manipulate model outputs, causing malicious behavior only when the trigger is present. Current research focuses on detecting and mitigating these attacks across various model architectures, including generative diffusion models, deep reinforcement learning agents, large language models, and graph neural networks, with a particular emphasis on developing robust defenses against increasingly sophisticated and stealthy triggers. The significance of this research lies in securing the reliability and trustworthiness of AI systems deployed in critical applications, ranging from autonomous vehicles to healthcare and finance, where malicious manipulation could have severe consequences.