With　the　discernment　of　the　vulnerability　of　deep　neural　networks　recently,　adversarial　attack　methods　have　become　one　of　the　hot　spots　for　the　security　of　artificial　intelligence　technologies.　While　previous　researches　can　effectively　generate　adversarial　examples　in　white-box　attacks,　it　remains　challenging　to　transfer　these　adversarial　examples　to　black-box　models,　where　the　attacker　has　no　knowledge　about　the　model　structure　and　parameters.　This　paper　focuses　on　the　transferability　of　adversarial　examples　and　proposes　a　novel　approach　named　Model-Agnostic　Attack　(MAA),　in　which　meta-learning　is　explored　to　facilitate　the　transferability　of　adversarial　examples　crafted　on　vanilla　adversarial　attacks　across　diverse　black-box　models.　Specifically,　model-agnostic　meta-learning,　a　meta-learning　approach,　can　train　a　well-generalized　model　to　various　unknown　tasks　and　is　utilized　to　alleviate　the　overfitting　problem　of　adversarial　examples　for　the　specified　models,　so　that　the　adversarial　examples　can　be　easily　transferred　to　black-box　models.　Besides,　we　highlight　that　the　MAA　is　a　plug-and-play　approach　and　can　be　effortlessly　integrated　with　any　existing　technologies　to　further　boost　transferability.　Extensive　experiment　results　on　CIFAR-10　and　CIFAR-100　exhibit　the　superiority　of　MAA　that　achieves　higher　transferability　than　state-of-the-art　methods　on　average　against　black-box　models.