Not intuitive at all. Not all models are equally capable, just because they had the same training data. The model architecture (as a whole) is very important. To reduce capability, you can reduce layers, tool use, thinking, quantize it, etc. This is trivially proven by a cursory glance in the rough direction of any set of benchmarks (or actual use).

Using small models as a classifier "there might be a vulnerability here" is probably reasonable, if you have a model capable of proving it. There are many companies attempting this without the verification step, resulting in AI vulnerability checker being banned left and right, from the nonsense noise.