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Tue 10 Nov 2020 08:07 - 08:08 at Virtual room 1 - Fuzzing

We present counterintuitive results for the scalability of fuzzing. Given the same non-deterministic fuzzer, finding the \emph{same bugs} linearly faster requires linearly more machines. For instance, with twice the machines, we can find \emph{all known bugs} in half the time. Yet, finding linearly \emph{more bugs} in the same time requires exponentially more machines. For instance, for every \emph{new bug} we want to find in 24 hours, we might need twice more machines. Similarly for coverage. With exponentially more machines, we can cover the \emph{same code} exponentially faster, but \emph{uncovered code} only linearly faster. In other words, re-discovering the same vulnerabilities is cheap but finding new vulnerabilities is expensive. This holds even under the simplifying assumption of \emph{no} parallelization overhead.

We derive these observations from over four CPU years worth of fuzzing campaigns involving almost three hundred open source programs, two state-of-the-art greybox fuzzers, four measures of code coverage, and two measures of vulnerability discovery. We provide a probabilistic analysis and conduct simulation experiments to explain this phenomenon.

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

08:00 - 08:02
Talk
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
DOI
08:03 - 08:04
Talk
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
DOI
08:05 - 08:06
Talk
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
Talk
Fuzzing: On the Exponential Cost of Vulnerability Discovery
Research Papers
Marcel BöhmeMonash University, Australia, Brandon FalkGamozo Labs, n.n.
DOI
08:09 - 08:10
Talk
Harvey: A Greybox Fuzzer for Smart Contracts
Industry Papers
DOI Pre-print
08:11 - 08:12
Talk
Intelligent REST API Data Fuzzing
Research Papers
Patrice GodefroidMicrosoft Research, USA, Bo-Yuan HuangPrinceton University, USA, Marina PolishchukMicrosoft Research, USA
DOI
08:13 - 08:14
Talk
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
Talk
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