Attack Dataset

Attack datasets are crucial for evaluating and improving the robustness of machine learning models, particularly large language models (LLMs) and intrusion detection systems, against various adversarial attacks. Current research focuses on developing diverse and representative attack datasets for different applications, including code generation, network security, and audio deepfakes, often employing techniques like adversarial prompt injection and data augmentation to create realistic and challenging scenarios. These datasets are essential for benchmarking model performance, identifying vulnerabilities, and driving the development of more secure and reliable AI systems with practical applications in cybersecurity and other fields.