  Each machine learning model deployed into production has a risk of
adversarial attack. Quantifying the contributing factors and uncertainties
using empirical measures could assist the industry with assessing the risk of
downloading and deploying common machine learning model types. The Drake
Equation is famously used for parameterizing uncertainties and estimating the
number of radio-capable extra-terrestrial civilizations. This work proposes
modifying the traditional Drake Equation's formalism to estimate the number of
potentially successful adversarial attacks on a deployed model. While previous
work has outlined methods for discovering vulnerabilities in public model
architectures, the proposed equation seeks to provide a semi-quantitative
benchmark for evaluating the potential risk factors of adversarial attacks.
