A Call for Mandatory Input Validation and Fuzz Testing

Learn&Fuzz: machine learning for input fuzzing

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 show how to automate the generation of an input grammar suitable for input fuzzing using sample inputs and neural-network-based statistical machine-learning techniques. We present a detailed case study with a complex in...

Automated Whitebox Fuzz Testing

Fuzz testing is an effective technique for finding security vulnerabilities in software. Traditionally, fuzz testing tools apply random mutations to well-formed inputs and test the program on the resulting values. We present an alternative whitebox fuzz testing approach inspired by recent advances in symbolic execution and dynamic test generation. Our approach records an actual run of a program...

A call for mandatory flu vaccinations.

Automatic and lightweight grammar generation for fuzz testing

Blackbox fuzz testing can only test a small portion of code when rigorously checking the well-formedness of input values. To overcome this problem, blackbox fuzz testing is performed using a grammar that delineates the format information of input values. However, it is almost impossible to manually construct a grammar if the input specifications are not known. We propose an alternative techniqu...

Instrumented Fuzz Testing Using AIR Integers

Integers represent a growing and underestimated source of vulnerabilities in C and C++ programs. In this paper, we present the as-if infinitely ranged (AIR) integer model, which provides a largely automated mechanism for eliminating integer overflow, truncation, and other integral exceptional conditions. The AIR integer model either produces a value equivalent to one that would have been obtain...