Common uncertainties in stock assessment relate to parameters or assumptions that strongly determine both the estimates of quantities of management interest (e.g. stock depletion) and related reference points (e.g. biomass at maximum sustainable yield). The risks associated with these uncertainties are often presented to managers in the form of decision tables. However, a formal evaluation of the risks from mis‐specifying an assessment model over time‐horizons spanning multiple assessment cycles requires closed‐loop simulation. There were two aims of this study: (a) develop an approach to identify and evaluate asymmetries in risk to yields and spawning biomass due to biases in key parameters and data sources in a stock assessment model, (b) quantify the relative importance of correctly specifying the various assessment attributes. A computationally efficient stock reduction analysis was evaluated using closed‐loop simulation to identify risks associated with a stock assessment with persistent positive and negative biases in the key parameters and inaccurate assumptions regarding data sources. Six types of assessment misspecification were examined, namely the assumed natural mortality rate, the assumed recruitment compensation ratio, the assumed age of maturity, a hyper‐stable or hyper‐deplete index of abundance, over‐ or under‐reporting of historical catch, and misspecification of the assumed shape of the selectivity curve. This study reveals large asymmetries in risk associated with common uncertainties in stock assessment processes. We highlight the value of reproducible and computationally efficient stock assessment models that can be investigated by closed‐loop simulation before being used for fisheries management.