  Smart contract transactions are increasingly interleaved by cross-contract
calls. While many tools have been developed to identify a common set of
vulnerabilities, the cross-contract vulnerability is overlooked by existing
tools. Cross-contract vulnerabilities are exploitable bugs that manifest in the
presence of more than two interacting contracts. Existing methods are however
limited to analyze a maximum of two contracts at the same time. Detecting
cross-contract vulnerabilities is highly non-trivial. With multiple interacting
contracts, the search space is much larger than that of a single contract. To
address this problem, we present xFuzz, a machine learning guided smart
contract fuzzing framework. The machine learning models are trained with novel
features (e.g., word vectors and instructions) and are used to filter likely
benign program paths. Comparing with existing static tools, machine learning
model is proven to be more robust, avoiding directly adopting manually-defined
rules in specific tools. We compare xFuzz with three state-of-the-art tools on
7,391 contracts. xFuzz detects 18 exploitable cross-contract vulnerabilities,
of which 15 vulnerabilities are exposed for the first time. Furthermore, our
approach is shown to be efficient in detecting non-cross-contract
vulnerabilities as well -- using less than 20% time as that of other fuzzing
tools, xFuzz detects twice as many vulnerabilities.
