  As the capabilities of large machine learning models continue to grow, and as
the autonomy afforded to such models continues to expand, the spectre of a new
adversary looms: the models themselves. The threat that a model might behave in
a seemingly reasonable manner, while secretly and subtly modifying its behavior
for ulterior reasons is often referred to as deceptive alignment in the AI
Safety & Alignment communities. Consequently, we call this new direction
Deceptive Alignment Monitoring. In this work, we identify emerging directions
in diverse machine learning subfields that we believe will become increasingly
important and intertwined in the near future for deceptive alignment
monitoring, and we argue that advances in these fields present both long-term
challenges and new research opportunities. We conclude by advocating for
greater involvement by the adversarial machine learning community in these
emerging directions.
