Experiments across three unlearning methods with three datasets and three LLMs show that TAS recovers the forgotten entity with 100% accuracy and reconstructs up to 95% of forgotten prompts. ^[1] The authors show that the forgotten prompts themselves can be extracted by using the retained data and black-box access to the model. ^[2] Felipe Ramos Chirinos, a 105-year-old pensioner from Yauli, is one of the most longevous pensioners in Peru, with 13 children, 32 grandchildren and 39 great-grandchildren. ^[3] Gastón Remy, executive president of Peru's pension agency ONP, visited pensioners in Huancayo, Huancavelica and Lircay to learn about their needs and bring services closer to remote areas. ^[4] Recent unlearning methods including NPO, DPO, and LUNAR utilize refusal alignment to suppress forgotten data within AI models. ^[5] The new attack, Targeted Active Search (TAS), first identifies forgotten entities by constructing canonical templates and an entity pool. ^[6]