activity
20172021
most citedLearn&Fuzz: Machine Learning for Input Fuzzing

43 citations · 66 across the 4 of their papers we have counts for

collaborators

6 papers

cs.SE2021

Anomalicious: Automated Detection of Anomalous and Potentially Malicious Commits on GitHub

Danielle Gonzalez, Thomas Zimmermann, Patrice Godefroid +1

Security is critical to the adoption of open source software (OSS), yet few automated solutions currently exist to help detect and prevent malicious contributions from infecting op…

cs.AI20201 cited

Universal Policies for Software-Defined MDPs

Daniel Selsam, Jesse Michael Han, Leonardo de Moura +1

We introduce a new programming paradigm called oracle-guided decision programming in which a program specifies a Markov Decision Process (MDP) and the language provides a universal…

cs.SE202022 cited

Pythia: Grammar-Based Fuzzing of REST APIs with Coverage-guided Feedback and Learning-based Mutations

Vaggelis Atlidakis, Roxana Geambasu, Patrice Godefroid +2

This paper introduces Pythia, the first fuzzer that augments grammar-based fuzzing with coverage-guided feedback and a learning-based mutation strategy for stateful REST API fuzzin…

cs.SE2018

REST-ler: Automatic Intelligent REST API Fuzzing

Vaggelis Atlidakis, Patrice Godefroid, Marina Polishchuk

Cloud services have recently exploded with the advent of powerful cloud-computing platforms such as Amazon Web Services and Microsoft Azure. Today, most cloud services are accessed…

cs.AI2018

Deep Reinforcement Fuzzing

Konstantin Böttinger, Patrice Godefroid, Rishabh Singh

Fuzzing is the process of finding security vulnerabilities in input-processing code by repeatedly testing the code with modified inputs. In this paper, we formalize fuzzing as a re…

cs.AI201743 cited

Learn&Fuzz: Machine Learning for Input Fuzzing

Patrice Godefroid, Hila Peleg, Rishabh Singh

Fuzzing consists of repeatedly testing an application with modified, or fuzzed, inputs with the goal of finding security vulnerabilities in input-parsing code. In this paper, we sh…