2,410 research outputs found
Towards Smart Hybrid Fuzzing for Smart Contracts
Smart contracts are Turing-complete programs that are executed across a
blockchain network. Unlike traditional programs, once deployed they cannot be
modified. As smart contracts become more popular and carry more value, they
become more of an interesting target for attackers. In recent years, smart
contracts suffered major exploits, costing millions of dollars, due to
programming errors. As a result, a variety of tools for detecting bugs has been
proposed. However, majority of these tools often yield many false positives due
to over-approximation or poor code coverage due to complex path constraints.
Fuzzing or fuzz testing is a popular and effective software testing technique.
However, traditional fuzzers tend to be more effective towards finding shallow
bugs and less effective in finding bugs that lie deeper in the execution. In
this work, we present CONFUZZIUS, a hybrid fuzzer that combines evolutionary
fuzzing with constraint solving in order to execute more code and find more
bugs in smart contracts. Evolutionary fuzzing is used to exercise shallow parts
of a smart contract, while constraint solving is used to generate inputs which
satisfy complex conditions that prevent the evolutionary fuzzing from exploring
deeper paths. Moreover, we use data dependency analysis to efficiently generate
sequences of transactions, that create specific contract states in which bugs
may be hidden. We evaluate the effectiveness of our fuzzing strategy, by
comparing CONFUZZIUS with state-of-the-art symbolic execution tools and
fuzzers. Our evaluation shows that our hybrid fuzzing approach produces
significantly better results than state-of-the-art symbolic execution tools and
fuzzers
Retrofitting Security in COTS Software with Binary Rewriting
We present a practical tool for inserting security features against low-level software attacks into third-party, proprietary or otherwise binary-only software. We are motivated by the inability of software users to select and use low-overhead protection schemes when source code is unavailable to them, by the lack of information as to what (if any) security mechanisms software producers have used in their toolchains, and the high overhead and inaccuracy of solutions that treat software as a black box. Our approach is based on SecondWrite, an advanced binary rewriter that operates without need for debugging information or other assist. Using SecondWrite, we insert a variety of defenses into program binaries. Although the defenses are generally well known, they have not generally been used together because they are implemented by different (non-integrated) tools. We are also the first to demonstrate the use of such mechanisms in the absence of source code availability. We experimentally evaluate the effectiveness and performance impact of our approach. We show that it stops all variants of low-level software attacks at a very low performance overhead, without impacting original program functionality
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