arXiv subjects

Cristian Daniele

Publications and source records attributed to Cristian Daniele.

5 recordsLinked to original sources

arXiv2025

WuppieFuzz: Coverage-Guided, Stateful REST API Fuzzing

Many business processes currently depend on web services, often using REST APIs for communication. REST APIs expose web service functionality through endpoints, allowing easy client interaction over the Internet. To reduce the security risk resulting from exposed endpoints, thorough testing is desired. Due to the generally vast number of endpoints, automated testing techniques, like fuzzing, are of interest. This paper introduces WuppieFuzz, an open-source REST API fuzzer built on LibAFL, supporting white-box, grey-box and black-box fuzzing. Using an OpenAPI specification, it can generate an initial input corpus consisting of sequences of requests. These are mutated with REST-specific and LibAFL-provided mutators to explore different code paths in the software under test. Guided by the measured coverage, WuppieFuzz then selects which request sequences to send next to reach complex states in the software under test. In this process, it automates harness creation to reduce manual efforts often required in fuzzing. Different kinds of reporting are provided by the fuzzer to help fixing bugs. We evaluated our tool on the Petstore API to assess the robustness of the white-box approach and the effectiveness of different power schedules. We further monitored endpoint and code coverage over time to measure the efficacy of the approach.

cs.SE↗

arXiv2024

Is Stateful Fuzzing Really Challenging?

Fuzzing has been proven extremely effective in finding vulnerabilities in software. When it comes to fuzz stateless systems, analysts have no doubts about the choice to make. In fact, among the plethora of stateless fuzzers devised in the last 20 years, AFL (with its descendants AFL++ and LibAFL) stood up for its effectiveness, speed and ability to find bugs. On the other hand, when dealing with stateful systems, it is not clear what is the best tool to use. In fact, the research community struggles to devise (and benchmark) effective and generic stateful fuzzers. In this short paper, we discuss the reasons that make stateful fuzzers difficult to devise and benchmark.

cs.SE↗

arXiv2024

Uses of Active and Passive Learning in Stateful Fuzzing

This paper explores the use of active and passive learning, i.e.\ active and passive techniques to infer state machine models of systems, for fuzzing. Fuzzing has become a very popular and successful technique to improve the robustness of software over the past decade, but stateful systems are still difficult to fuzz. Passive and active techniques can help in a variety of ways: to compare and benchmark different fuzzers, to discover differences between various implementations of the same protocol, and to improve fuzzers.

cs.SE↗

arXiv2023

Evaluating the Fork-Awareness of Coverage-Guided Fuzzers

Fuzz testing (or fuzzing) is an effective technique used to find security vulnerabilities. It consists of feeding a software under test with malformed inputs, waiting for a weird system behaviour (often a crash of the system). Over the years, different approaches have been developed, and among the most popular lies the coverage-based one. It relies on the instrumentation of the system to generate inputs able to cover as much code as possible. The success of this approach is also due to its usability as fuzzing techniques research approaches that do not require (or only partial require) human interactions. Despite the efforts, devising a fully-automated fuzzer still seems to be a challenging task. Target systems may be very complex; they may integrate cryptographic primitives, compute and verify check-sums and employ forks to enhance the system security, achieve better performances or manage different connections at the same time. This paper introduces the fork-awareness property to express the fuzzer ability to manage systems using forks. This property is leveraged to evaluate 14 of the most widely coverage-guided fuzzers and highlight how current fuzzers are ineffective against systems using forks.

cs.CR↗

arXiv2023

Fuzzers for stateful systems: Survey and Research Directions

Fuzzing is a security testing methodology effective in finding bugs. In a nutshell, a fuzzer sends multiple slightly malformed messages to the software under test, hoping for crashes or weird system behaviour. The methodology is relatively simple, although applications that keep internal states are challenging to fuzz. The research community has responded to this challenge by developing fuzzers tailored to stateful systems, but a clear understanding of the variety of strategies is still missing. In this paper, we present the first taxonomy of fuzzers for stateful systems and provide a systematic comparison and classification of these fuzzers.

cs.CR↗