From Assistance to Autonomy:
An Empirical Study of AI Use in a Live Capture-the-Flag (CTF) Competition

Capture-the-Flag (CTF) competitions are increasingly becoming a testbed for evaluating AI capabilities at solving security tasks, due to their controlled environments and objective success criteria. Existing evaluations have focused on how successful AI is at solving individual CTF challenges in isolation from human CTF players. As AI usage increases in both academic and industrial settings, it is equally likely that human CTF players may collaborate with AI agents to solve CTF challenges. This possibility exposes a key knowledge gap: how do human players perceive AI CTF assistance; when assistance is provided, in what ways do they collaborate and is it effective with respect to human performance; how do humans assisted by AI compare to the performance of fully autonomous AI agents on the same set of challenges. We address this gap with the first empirical study of AI assistance in a live, onsite CTF. In a study with 41 participants (out of the total 95 that participated in the CTF), we qualitatively study (i) how participants' perception, trust, and expectations shift before versus after hands-on AI use, and (ii) how participants collaborate with an instrumented AI assistant. Moreover, we also (iii) benchmark four autonomous CTF agents on the same fresh challenge set to compare outcomes with human teams and analyze agent trajectories. We find that, for human players, AI literacy and domain knowledge are complementary competencies, and both have irreplaceable advantages. Proficient and efficient use of AI amplifies professional skills. Importantly, although advanced autonomous agents showed outstanding performance, human-in-the-loop is the winning paradigm where AI accelerates exploration while humans provide targeted guidance and verification. We conclude with implications for the future design of CTF competitions and for building effective human-in-the-loop AI systems for security.