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Tue 10 Nov 2020 08:03 - 08:04 at Virtual room 1 - Fuzzing

Fuzz testing has been proved its effectiveness in discovering
software vulnerabilities. Empowered its randomness nature along
with a coverage-guiding feature, fuzzing has been identified a vast
number of vulnerabilities in real-world programs. This paper begins
with an observation that the design of the current state-of-the-art
fuzzers is not well suited for a particular (but yet important) set
of software programs. Specifically, current fuzzers have limitations
in fuzzing programs serving multiple purposes, where each purpose is
controlled by extra options.

This paper proposes CrFuzz, which overcomes this limitation.
CrFuzz designs a clustering analysis to automatically predict if
a newly given input would be accepted or not by a target program.
Exploiting this prediction capability, CrFuzz is designed to
efficiently explore the programs with multiple purposes. We employed
CrFuzz for three state-of-the-art fuzzers, AFL, QSYM, and MOpt,
and CrFuzz-augmented versions have shown 19.3% and 5.68% better path
and edge coverage on average. More importantly, during two weeks of
long-running experiments, CrFuzz discovered 277 previously unknown
vulnerabilities where 212 of those are already confirmed and fixed
by the respected vendors. We would like to emphasize that many of
these vulnerabilities were discoverd from FFMpeg, ImageMagick, and
Graphicsmagick, all of which are targets of Google's OSS-Fuzz project
and thus heavily fuzzed for last three years by far. Nevertheless,
CrFuzz identified a remarkable number of vulnerabilities, demonstrating
its effectiveness of vulnerability finding capability.

Tue 10 Nov
Times are displayed in time zone: (UTC) Coordinated Universal Time change

08:00 - 08:02
Boosting Fuzzer Efficiency: An Information Theoretic PerspectiveACM SIGSOFT Distinguished Paper Award
Research Papers
Marcel BöhmeMonash University, Australia, Valentin ManèsKAIST, South Korea, Sang Kil ChaKAIST, South Korea
08:03 - 08:04
CrFuzz: Fuzzing Multi-purpose Programs through Input Validation
Research Papers
Suhwan SongSeoul National University, South Korea, Chengyu SongUniversity of California at Riverside, USA, Yeongjin JangOregon State University, USA, Byoungyoung LeeSeoul National University, South Korea
08:05 - 08:06
Detecting Critical Bugs in SMT Solvers Using Blackbox Mutational Fuzzing
Research Papers
Muhammad Numair MansurMPI-SWS, Germany, Maria ChristakisMPI-SWS, Valentin WüstholzConsenSys, Fuyuan ZhangMPI-SWS, Germany
DOI Pre-print
08:07 - 08:08
Fuzzing: On the Exponential Cost of Vulnerability Discovery
Research Papers
Marcel BöhmeMonash University, Australia, Brandon FalkGamozo Labs, n.n.
08:09 - 08:10
Harvey: A Greybox Fuzzer for Smart Contracts
Industry Papers
DOI Pre-print
08:11 - 08:12
Intelligent REST API Data Fuzzing
Research Papers
Patrice GodefroidMicrosoft Research, USA, Bo-Yuan HuangPrinceton University, USA, Marina PolishchukMicrosoft Research, USA
08:13 - 08:14
MTFuzz: Fuzzing with a Multi-task Neural Network
Research Papers
Dongdong SheColumbia University, USA, Rahul KrishnaColumbia University, USA, Lu YanShanghai Jiao Tong University, China, Suman JanaColumbia University, USA, Baishakhi RayColumbia University, USA
DOI Pre-print
08:15 - 08:30
Conversations on Fuzzing
Research Papers
Dongdong SheColumbia University, USA, Muhammad Numair MansurMPI-SWS, Germany, Marcel BöhmeMonash University, Australia, Suhwan SongSeoul National University, South Korea, Valentin WüstholzConsenSys, M: Mike PapadakisUniversity of Luxembourg, Luxembourg