  A rapidly growing number of applications rely on a small set of closed-source
language models (LMs). This dependency might introduce novel security risks if
LMs develop self-recognition capabilities. Inspired by human identity
verification methods, we propose a novel approach for assessing
self-recognition in LMs using model-generated "security questions". Our test
can be externally administered to keep track of frontier models as it does not
require access to internal model parameters or output probabilities. We use our
test to examine self-recognition in ten of the most capable open- and
closed-source LMs currently publicly available. Our extensive experiments found
no empirical evidence of general or consistent self-recognition in any examined
LM. Instead, our results suggest that given a set of alternatives, LMs seek to
pick the "best" answer, regardless of its origin. Moreover, we find indications
that preferences about which models produce the best answers are consistent
across LMs. We additionally uncover novel insights on position bias
considerations for LMs in multiple-choice settings.
