Why is the survival probability decreasing the more variables I use? I'm using coxph in combination with survfit and I observed that, e.g., when I use the following syntax
coxph(Surv(time, event) ~ 1, data = df)

coxph(Surv(time, event) ~ position, data = df)

coxph(Surv(time, event) ~ position + country, data = df)

that the survival functions move more and more leftwards as more and more variables are used, thus, decreasing in time a certain survival probability has achieved. I wonder why this as the data, especially time and event, do not change when considering more variables?!!
The data comprises ~4500 devices which have components built in described by levels of position. Furthermore, they are used across the world with 20-30 countries.

The admonition to "read the manual" particularly applies to survival-curve estimates from Cox proportional hazards (PH) models. If you are, say, using the R survminer package, make sure that you read and understand the default settings.*
You can think about the baseline survival curve** similarly to the intercept in a linear regression. An intercept is the outcome estimate when all covariates are at their reference levels. The value of an intercept depends on how covariates are coded: the choices of reference levels for categorical covariates and whether there's been centering of continuous covariates.
This gets further complicated with Cox models. First, instead of a single intercept you have a whole survival curve as a function of time. Second, the software can make unexpected choices for "reference levels" of covariates. I'm hesitant to say anything about the details of the baseline curves you got, as they depend on what software you used (perhaps unconsciously, depending on what other functions your software invoked).
The R survfit.coxph() package reports baseline survival at some "average" values even of categorical covariates! Even the manual page says: "The resulting curve(s) almost never make sense." Once you specify particular values of covariates you get appropriate covariate adjustment for corresponding conditional survival curves, but those baseline curves aren't useful on their own. Other software packages, like rms, can provide realistic baseline curves that are more representative of the data.
So don't worry about apparent differences in default baseline survival curves. Make sure that the Cox model is appropriate for your data and then examine predictions for realistic scenarios.

*I infer that's one of the tools you're using, based on other questions you've posted recently. That has some strengths but I've seen a few questions here that had more to do with not understanding the survminer defaults than with survival analysis per se. Such helper packages don't necessarily use the same default settings as the underlying survival package. You need to check.
**The baseline is inferred after fitting the regression; the survfit() default is the Breslow estimate
