  Cryptocurrencies are an emerging economic force, but there are concerns about
their security. This is due, in part, to complex collusion cases and new threat
vectors that could be missed by conventional security assessment strategies. To
address these issues, we propose ABC, an Asset-Based Cryptocurrency-focused
threat modeling framework capable of identifying such risks. ABC's key
innovation is the use of collusion matrices. A collusion matrix forces a threat
model to cover a large space of threat cases while simultaneously manages this
process to prevent it from being overly complex. Moreover, ABC derives a
system-specific threat categories that account for the financial aspects and
the new asset types that cryptocurrencies introduce. We demonstrate that ABC is
effective by conducting a user study and by presenting real-world use cases.
The user study showed that around 71$\%$ of those who used ABC were able to
identify financial security threats, as compared to only 13$\%$ of participants
who used the popular framework STRIDE. The use cases further attest to the
usefulness of ABC's tools for both cryptocurrency-based systems, as well as a
cloud native security technology. This shows the potential of ABC as an
effective security assessment technique for various types of large-scale
distributed systems.
