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Tue 10 Nov 2020 08:00 - 08:02 at Virtual room 1 - Fuzzing

In this paper, we take the fundamental perspective of fuzzing as a learning process. Suppose before fuzzing, we know nothing about the behaviors of a program $\mathcal{P}$: What does it do? Executing the first test input, we learn how $\mathcal{P}$ behaves for this input. Executing the next input, we either observe the same or discover a new behavior. As such, each execution reveals "some amount" of information about $\mathcal{P}$'s behaviors. A classic measure of information is Shannon's entropy. Measuring entropy allows us to quantify how much is learned from each generated test input about the behaviors of the program. Within a probabilistic model of fuzzing, we show how entropy also measures fuzzer efficiency. Specifically, it measures the general \emph{rate} at which the fuzzer discovers new behaviors. Intuitively, \emph{efficient fuzzers maximize information}.

From this information theoretic perspective, we develop Entropic, an entropy-based power schedule for greybox fuzzing which assigns more energy to seeds that maximize information. We implemented Entropic into the popular greybox fuzzer LibFuzzer. Our experiments with more than 250 open-source programs (60 \emph{million} LoC) demonstrate a substantially improved efficiency and \emph{confirm} our hypothesis that an efficient fuzzer maximizes information. Entropic has been independently evaluated and invited for integration into main-line LibFuzzer. Entropic now runs on more than 25,000 machines fuzzing hundreds of security-critical software systems simultaneously and continuously.

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