Attack Method

Attack methods in machine learning and artificial intelligence focus on exploiting vulnerabilities in models to cause misbehavior or information leakage. Current research emphasizes attacks against large language models (LLMs), including jailbreaking (inducing harmful outputs), data extraction, and manipulating model behavior through prompt engineering or adversarial examples. These studies utilize various techniques, such as reinforcement learning for generating sophisticated attacks, graph neural networks for analyzing attack paths, and adversarial training for improving model robustness. Understanding and mitigating these attacks is crucial for ensuring the safety and reliability of AI systems across diverse applications.