This study employs a quantile vector autoregression (QVAR) approach to investigate systemic risk interconnectedness across eight major NFT assets. The analysis reveals NFT markets exhibit moderate interconnectedness during normal conditions that significantly intensifies during extreme market states, creating vulnerability to volatility cascades. Research demonstrates asymmetric spillover patterns with frequent role reversals between risk transmitters and receivers, particularly when assets occupy opposite extreme quantiles. These findings provide crucial insights for constructing resilient NFT portfolios and developing effective regulatory frameworks addressing tail-risk events in digital asset markets.