Function signature recovery is important for many binary analysis tasks such
as control-flow integrity enforcement, clone detection, and bug finding.
Existing works try to substitute learning-based methods with rule-based methods
to reduce human effort.They made considerable efforts to enhance the system's
performance, which also bring the side effect of higher resource consumption.
However, recovering the function signature is more about providing information
for subsequent tasks, and both efficiency and performance are significant.
In this paper, we first propose a method called Nimbus for efficient function
signature recovery that furthest reduces the whole-process resource consumption
without performance loss. Thanks to information bias and task relation (i.e.,
the relation between parameter count and parameter type recovery), we utilize
selective inputs and introduce multi-task learning (MTL) structure for function
signature recovery to reduce computational resource consumption, and fully
leverage mutual information. Our experimental results show that, with only
about the one-eighth processing time of the state-of-the-art method, we even
achieve about 1% more prediction accuracy over all function signature recovery
tasks