Logistic regression, after adding covariate interaction term, the key IV becomes insignificant (R studio)

I’m trying to understand why adding a covariate and an interaction term gives different results on the significance of the key IV from adding only the covariate. I have a categorical IV (2 levels), and a binary outcome (coded as 1 and 0). I also have a continuous covariate.

If I only add IV (i.e., condition) to the logistic regression model, the IV has a significant effect on DV:

glm(formula = DV ~ condition, family = "binomial", data = df)

(AIC: 307.38)

If I add IV and the covariate to the logistic regression model, the IV still significant:

glm(formula = DV ~ condition + covariate, family = "binomial", data = df)

(AIC: 306.29)

But when I added IV, covariate and the interaction, the IV no longer significant but the covariate became significant:

glm(formula = DV ~ condition * covariate, family = "binomial", data = df)

(AIC: 306.48)

I’m so confused. It’s there any problem to add interaction to logistic regression, or it’s the fact that my IV doesn’t really have an effect on DV? I read a similar post here but I’m still confused.

Follow-up:
I tried to only include covariate in the model:

glm(formula = DV ~ covariate, family = "binomial", data = df)

(AIC: 308.69, not sure if this is relevant)