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Engines and platforms for automated vulnerability discovery through fuzzing.
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OSS-Fuzz is a distributed, containerized platform for continuous fuzzing and memory safety analysis. It functions as a bug hunting infrastructure that identifies security vulnerabilities and stability bugs through automated, coverage-guided fuzz testing across a scalable cluster of containers. The system provides a continuous security testing pipeline that manages the entire lifecycle of vulnerability discovery, from bootstrapping project templates and compiling targets to executing long-running batch tests. It specifically focuses on memory safety, utilizing sanitizers to detect buffer overf
Orchestrates the bootstrapping and execution of fuzzing targets within isolated container environments.
Hypothesis is a Python property-based testing library and data generation engine. It enables the discovery of edge cases and bugs by generating a wide range of randomized inputs based on defined strategies and shrinking complex failing examples to their smallest possible form. It also functions as a state machine testing framework to verify system behavior across sequences of interdependent operations. The project features a fuzzing integration layer that converts raw byte buffers from coverage-guided fuzzers into structured test cases. It includes a persistence mechanism to store and synchro
Combines coverage-guided fuzzing with structured data generation to uncover vulnerabilities and crashes.
AFL++ is a coverage-guided fuzzing framework that discovers crashes and hangs in software by mutating inputs while tracking which code paths are exercised. It functions as both a fuzzing engine and a campaign manager, supporting targets with or without source code through compile-time instrumentation, dynamic binary instrumentation, and emulation. The framework includes tools for crash triage and analysis, test case minimization, and campaign deployment across local or distributed environments. The framework distinguishes itself through its breadth of instrumentation backends, allowing users
Deploys, monitors, and manages multiple fuzzer instances across local or distributed environments.
Distributes fuzzing campaigns across multiple CPU cores or machines to accelerate vulnerability discovery.
ClusterFuzz is an automated platform that runs coverage-guided fuzzers at scale to find security and stability bugs in software. It orchestrates libFuzzer and AFL++ across distributed clusters of worker bots, collecting coverage feedback to guide input mutation and discover crashes. The platform provides a web-based dashboard for configuring fuzzing jobs, monitoring progress, and inspecting crash reports, with role-based access control to restrict sensitive features. The system automates the full fuzzing lifecycle, from build pipeline integration and corpus management to crash triage and bug
Manages a cluster of worker bots that execute fuzzing tasks across thousands of machines.
AFL is a coverage-guided fuzzer and security vulnerability scanner used to identify software bugs and memory corruption by feeding programs mutated data. It functions as a binary instrumentation tool and a test case minimizer to locate crashes and isolate the smallest set of bytes causing a fault. The project distinguishes itself through its ability to operate as a parallel fuzzing orchestrator, distributing workloads across multiple CPU cores or networked machines. It utilizes dictionary-based mutation for complex file formats and performs input sensitivity analysis to identify critical sect
Spreads fuzzing workloads across multiple CPU cores or networked machines to accelerate discovery.
A self-hosted Fuzzing-As-A-Service platform
Self-hosted platform for Fuzzing-as-a-Service.