233 research outputs found
Towards Vulnerability Discovery Using Staged Program Analysis
Eliminating vulnerabilities from low-level code is vital for securing
software. Static analysis is a promising approach for discovering
vulnerabilities since it can provide developers early feedback on the code they
write. But, it presents multiple challenges not the least of which is
understanding what makes a bug exploitable and conveying this information to
the developer. In this paper, we present the design and implementation of a
practical vulnerability assessment framework, called Melange. Melange performs
data and control flow analysis to diagnose potential security bugs, and outputs
well-formatted bug reports that help developers understand and fix security
bugs. Based on the intuition that real-world vulnerabilities manifest
themselves across multiple parts of a program, Melange performs both local and
global analyses. To scale up to large programs, global analysis is
demand-driven. Our prototype detects multiple vulnerability classes in C and
C++ code including type confusion, and garbage memory reads. We have evaluated
Melange extensively. Our case studies show that Melange scales up to large
codebases such as Chromium, is easy-to-use, and most importantly, capable of
discovering vulnerabilities in real-world code. Our findings indicate that
static analysis is a viable reinforcement to the software testing tool set.Comment: A revised version to appear in the proceedings of the 13th conference
on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA),
July 201
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
A Survey of Symbolic Execution Techniques
Many security and software testing applications require checking whether
certain properties of a program hold for any possible usage scenario. For
instance, a tool for identifying software vulnerabilities may need to rule out
the existence of any backdoor to bypass a program's authentication. One
approach would be to test the program using different, possibly random inputs.
As the backdoor may only be hit for very specific program workloads, automated
exploration of the space of possible inputs is of the essence. Symbolic
execution provides an elegant solution to the problem, by systematically
exploring many possible execution paths at the same time without necessarily
requiring concrete inputs. Rather than taking on fully specified input values,
the technique abstractly represents them as symbols, resorting to constraint
solvers to construct actual instances that would cause property violations.
Symbolic execution has been incubated in dozens of tools developed over the
last four decades, leading to major practical breakthroughs in a number of
prominent software reliability applications. The goal of this survey is to
provide an overview of the main ideas, challenges, and solutions developed in
the area, distilling them for a broad audience.
The present survey has been accepted for publication at ACM Computing
Surveys. If you are considering citing this survey, we would appreciate if you
could use the following BibTeX entry: http://goo.gl/Hf5FvcComment: This is the authors pre-print copy. If you are considering citing
this survey, we would appreciate if you could use the following BibTeX entry:
http://goo.gl/Hf5Fv
Specognitor: Identifying Spectre Vulnerabilities via Prediction-Aware Symbolic Execution
Spectre attacks exploit speculative execution to leak sensitive information.
In the last few years, a number of static side-channel detectors have been
proposed to detect cache leakage in the presence of speculative execution.
However, these techniques either ignore branch prediction mechanism, detect
static pre-defined patterns which is not suitable for detecting new patterns,
or lead to false negatives.
In this paper, we illustrate the weakness of prediction-agnostic
state-of-the-art approaches. We propose Specognitor, a novel prediction-aware
symbolic execution engine to soundly explore program paths and detect subtle
spectre variant 1 and variant 2 vulnerabilities. We propose a dynamic pattern
detection mechanism to account for both existing and future vulnerabilities.
Our experimental results show the effectiveness and efficiency of Specognitor
in analyzing real-world cryptographic programs w.r.t. different processor
families
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