Despite enormous investments in airport security, terrorists have been able to find and exploit vulnerabilities at security checkpoints. Existing vulnerability assessment methodologies struggle with accounting for human behavior, and agent-based modelling forms a promising technique to overcome this limitation. This paper investigated how the decision-making and performance of human operators can be taken into account while assessing vulnerability at an airport security checkpoint. To this end, an agent-based model was designed, in which the performance of security operators was modelled using a functional state model, while decision making was modelled using decision field theory. Passengers and an attacker that brings a weapon to the security checkpoint were also explicitly modelled as agents. Simulation results indicate that the highest skilled operators outperformed their lowest skilled counterparts on analyzing X-ray images, but performed worse on both searching luggage and performing patdowns. Furthermore, results showed that a high focus on speed of security operators leads to a decrease in luggage searches and therefore increased vulnerability. More work is needed to calibrate and validate the simulation results, but initial results are promising. The agent-based model can be used by airport regulators and managers to understand the workings of their security checkpoint better and ultimately to reduce vulnerabilities.