Smart contracts are executable programs that enable the building of a
programmable trust mechanism between multiple entities without the need of a
trusted third-party. Researchers have developed several security scanners in
the past couple of years. However, many of these analyzers either do not scale
well, or if they do, produce many false positives. This issue is exacerbated
when bugs are triggered only after a series of interactions with the functions
of the contract-under-test. A depth-n vulnerability, refers to a vulnerability
that requires invoking a specific sequence of n functions to trigger. Depth-n
vulnerabilities are time-consuming to detect by existing automated analyzers,
because of the combinatorial explosion of sequences of functions that could be
executed on smart contracts.
In this paper, we present a technique to analyze depth-n vulnerabilities in
an efficient and scalable way by combining symbolic execution and data
dependency analysis. A significant advantage of combining symbolic with static
analysis is that it scales much better than symbolic alone and does not have
the problem of false positive that static analysis tools typically have. We
have implemented our technique in a tool called MPro, a scalable and automated
smart contract analyzer based on the existing symbolic analysis tool
Mythril-Classic and the static analysis tool Slither. We analyzed 100 randomly
chosen smart contracts on MPro and our evaluation shows that MPro is about
n-times faster than Mythril-Classic for detecting depth-n vulnerabilities,
while preserving all the detection capabilities of Mythril-Classic