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The networked nature of interbank connections creates vulnerability to systemic risk, which arises from inter-dependencies caused by common asset holdings when faced with exogenous negative shocks. This paper employs Exponential Random Graph Models (ERGMs) to reconstruct the network system of asset-holding correlations from the balance sheets of Chinese commercial banks from 2016 to 2022. The reconstructed network is designed to accurately mimic the topology of the real banking system. Subsequently, a novel framework for measuring aggregate network vulnerability is applied. This framework incorporates factors such as bank size, initial shocks, connectedness, leverage, and asset fire sales to identify financial contagion effects. The findings indicate that the reconstructed network system exhibits a good fit to real-world data in both its linkage structure and weight distribution. Furthermore, the cumulative aggregate vulnerability of the network increases non-linearly with the magnitude of the initial shock and the discount level of asset fire sales. The indirect vulnerability for individual banks, resulting from risk contagion triggered by deleveraging and fire sales, is substantially higher than the direct losses from initial shocks. The risk contribution to systemic vulnerability is concentrated in large state-owned banks and national joint-stock commercial banks. In contrast, the institutions most affected by risk shocks are predominantly small and medium-sized rural and urban commercial banks.