Adversarial triggers are specifically crafted sequences of words, characters, or tokens designed to manipulate machine learning and natural language processing models into producing unintended, incorrect, or harmful outputs. When appended or prepended to an otherwise benign prompt, these sequences exploit underlying vulnerabilities in a model to consistently provoke targeted failures, such as bypassing safety filters, inducing classification errors, or causing text generators to produce toxic, biased, or degenerate content. Because these triggers are often transferable and can function across a wide range of contexts without altering the core model parameters, they serve as significant tools for stress-testing model robustness and assessing security risks in automated language systems.
