7,360 research outputs found
Combining Static and Dynamic Analysis for Vulnerability Detection
In this paper, we present a hybrid approach for buffer overflow detection in
C code. The approach makes use of static and dynamic analysis of the
application under investigation. The static part consists in calculating taint
dependency sequences (TDS) between user controlled inputs and vulnerable
statements. This process is akin to program slice of interest to calculate
tainted data- and control-flow path which exhibits the dependence between
tainted program inputs and vulnerable statements in the code. The dynamic part
consists of executing the program along TDSs to trigger the vulnerability by
generating suitable inputs. We use genetic algorithm to generate inputs. We
propose a fitness function that approximates the program behavior (control
flow) based on the frequencies of the statements along TDSs. This runtime
aspect makes the approach faster and accurate. We provide experimental results
on the Verisec benchmark to validate our approach.Comment: There are 15 pages with 1 figur
Harvey: A Greybox Fuzzer for Smart Contracts
We present Harvey, an industrial greybox fuzzer for smart contracts, which
are programs managing accounts on a blockchain. Greybox fuzzing is a
lightweight test-generation approach that effectively detects bugs and security
vulnerabilities. However, greybox fuzzers randomly mutate program inputs to
exercise new paths; this makes it challenging to cover code that is guarded by
narrow checks, which are satisfied by no more than a few input values.
Moreover, most real-world smart contracts transition through many different
states during their lifetime, e.g., for every bid in an auction. To explore
these states and thereby detect deep vulnerabilities, a greybox fuzzer would
need to generate sequences of contract transactions, e.g., by creating bids
from multiple users, while at the same time keeping the search space and test
suite tractable. In this experience paper, we explain how Harvey alleviates
both challenges with two key fuzzing techniques and distill the main lessons
learned. First, Harvey extends standard greybox fuzzing with a method for
predicting new inputs that are more likely to cover new paths or reveal
vulnerabilities in smart contracts. Second, it fuzzes transaction sequences in
a targeted and demand-driven way. We have evaluated our approach on 27
real-world contracts. Our experiments show that the underlying techniques
significantly increase Harvey's effectiveness in achieving high coverage and
detecting vulnerabilities, in most cases orders-of-magnitude faster; they also
reveal new insights about contract code.Comment: arXiv admin note: substantial text overlap with arXiv:1807.0787
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
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