Penetration testing is an important means to discover the weaknesses of significant network information systems and protect network security. Traditional penetration testing relies heavily on manual labor and has high technical requirements for testers, limiting the popularization depth and breadth. By introducing artificial intelligence technology into the whole penetration testing process, automated penetration testing lowers the technical threshold of penetration testing based on greatly solving the problem of heavy dependence on manual labor. Automated penetration testing can be mainly divided into model-based and rule-based automated penetration testing, and the research of the two has their respective focuses. The former utilizes model algorithms to simulate hacker attacks with attention paid to attack scene perception and attack decision-making models. The latter concentrates on how to efficiently adapt attack rules and attack scenarios. This study mainly analyzes the implementation principles of automated penetration testing from three aspects of attack scenario modeling, penetration testing modeling, and decision-making reasoning model. Finally, the future development direction of automated penetration is explored from the dimensions of attack-defense confrontation and vulnerability combination utilization.