Common-method variance (CMV) is the spurious "variance that is attributable to the measurement method rather than to the constructs the measures are assumed to represent" (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003).
You can control for this method variance by adding a 'methods factor' to the proposed measurement model. This methods factor will have all variables as indicators and the covariance between this methods factor and all other factors will be fixed to zero.
Because the CMV model requires many parameters to be estimated, there are often convergence issues. Therefore, you can also fix all indicators of the 'methods factor' to be equal. That way, all indicators of the methods factor will have the same loading and less parameters need to be estimated.
When the common method model has a better fit, you could look at the extent to which the intercorrelations among the main variables significantly differed from the proposed model (for an example, see Shin et al., 2017). You can find the correlations in the lavaan output in 'Detailed CFA Summary'. (i.e., std.all column at the covariance section).
References
Podsakoff, P.M., MacKenzie, S.B., Lee, J.-Y. & Podsakoff, N.P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology. 88 (5): 879–903.
Shin, Y., Kim, M. S., Choi, J. N., Kim, M., & Oh, W. K. (2017). Does leader-follower regulatory fit matter? The role of regulatory fit in followers’ organizational citizenship behavior. Journal of Management, 43(4), 1211-1233.