The distributed control strategy has been widely employed, facilitating flexible and scalable coordination of different units in DC microgrids. However, the communication networks employed make them cyber-physical systems that are susceptible to cyberattacks. In this paper, a novel cybersecurity vulnerability identification and detection framework based on adversarial learning is developed for DC microgrids. Specifically, a multi-agent reinforcement learning (MARL) algorithm is used to emulate intelligent attackers that exploit system vulnerabilities by generating attacks capable of bypassing the existing detection mechanisms. In response, a data-driven attack detector is developed to complement the detection scheme, enhancing the overall detection capability. The framework utilizes an iterative adversarial learning process, wherein attacker and defender models continuously challenge and improve each other. This dynamic interaction enables the identification of a wider range of potential attacks, resulting in a more robust and adaptable detection mechanism for DC microgrids.