Benchmark Attack

Benchmark attacks evaluate the robustness of machine learning models, particularly large language models and object detectors, against various adversarial manipulations. Current research focuses on developing comprehensive benchmark frameworks that encompass diverse attack types (e.g., prompt injection, physical attacks, model poisoning) and evaluate their effectiveness across different model architectures and datasets, often using simulation to control experimental conditions. These benchmarks are crucial for identifying vulnerabilities and driving the development of more secure and reliable AI systems, impacting fields ranging from autonomous driving to finance.