Abstract:

In recent years, cyberattacks have become increasingly organized and automated. With the support of artificial intelligence technologies, particularly large language models, attackers are able to rapidly write and derive malicious code, and construct automated and distributed reconnaissance and attack processes targeting specific objectives through botnets. This has posed severe threats and risks to cybersecurity defenses. To effectively address these challenges, this thesis proposed and designed a novel automated penetration testing system based on a multi-agent architecture. The system decomposed traditional penetration testing tasks into atomic sub-tasks, which were then collaboratively completed by multiple agents. Experimental results show that the system significantly outperforms traditional vulnerability scanning tools across multiple testing metrics, being capable of comprehensively identifying various types of security vulnerabilities in the target information system, and providing highly credible evidence chains for vulnerability disclosure. Furthermore, the system can generate executable remediation recommendations, achieving the automation and engineering of the penetration testing process, thus offering an advanced, efficient, and stable solution for organizations to conduct regular network security vulnerability management.

Key words: penetration testing system, multi-agent architecture, autonomous mission planning, system and network security