  Memory-safety bugs introduce critical software-security issues. Rust provides
memory-safe mechanisms to avoid memory-safety bugs in programming, while still
allowing unsafe escape hatches via unsafe code. However, the unsafe code that
enhances the usability of Rust provides clear spots for finding memory-safety
bugs in Rust source code. In this paper, we claim that these unsafe spots can
still be identifiable in Rust binary code via machine learning and be leveraged
for finding memory-safety bugs. To support our claim, we propose the tool
textttrustspot, that enables reverse engineering to learn an unsafe classifier
that proposes a list of functions in Rust binaries for downstream analysis. We
empirically show that the function proposals by textttrustspot can recall
$92.92\%$ of memory-safety bugs, while it covers only $16.79\%$ of the entire
binary code. As an application, we demonstrate that the function proposals are
used in targeted fuzzing on Rust packages, which contribute to reducing the
fuzzing time compared to non-targeted fuzzing.
