As an important means to solve information overload, recommender systems have been widely applied in many fields, such as e-commerce and advertising. However, recent studies have shown that recommender systems are vulnerable to poisoning attacks; that is, injecting a group of carefully designed user profiles into the recommender system can severely affect recommendation quality. Despite the development from shilling attacks to optimization-based attacks, the imperceptibility and harmfulness of the generated data in most attacks are arduous to balance. To this end, we propose a triple adversarial learning for influence based poisoning attack (TrialAttack), a flexible end-to-end poisoning framework to generate non-notable and harmful user profiles. Specifically, given the input noise, TrialAttack directly generates malicious users through triple adversarial learning of the generator, discriminator, and influence module. Besides, to provide reliable influence for TrialAttack training, we explore a new approximation approach for estimating each fake user's influence. Through theoretical analysis, we prove that the distribution characterized by TrialAttack approximates to the rating distribution of real users under the premise of performing an efficient attack. This property allows the injected users to attack in an unremarkable way. Experiments on three real-world datasets show that TrialAttack's attack performance outperforms state-of-the-art attacks, and the generated fake profiles are more difficult to detect compared to baselines.