Fuzzilli vs Fuzzinator

Fuzzilli is a free, open-source, coverage-guided fuzzer for dynamic language interpreters. It represents test programs in FuzzIL, mutates them, and translates them to JavaScript for execution. Its mutators can change data flow or operation parameters, generate or splice code, and combine corpus programs. Components include a mutation fuzzer, script runner, corpus, environment, minimizer, evaluator, and lifter. A target engine runs repeated scripts in a read-eval-print-reset loop, resetting between cases; multiple instances can synchronize within a process or across machines over TCP. Listed target directories include JavaScriptCore, JerryScript, QuickJS, QtJS, Serenity, SpiderMonkey, V8, XS, Duktape, and njs. Setup requires compiling a supported engine with coverage instrumentation using clang 4.0 or later, then building Fuzzilli with Swift Package Manager. Docker and Google Compute Engine tooling are provided. The project supports Linux, macOS, and self-hosted use and identifies its license as Apache-2.0. Its repository says it is not an officially supported Google product. Contributions require a Contributor License Agreement and are reviewed through GitHub pull requests.

Fuzzinator is a free, open-source framework for automating fuzzing jobs against software under test. It runs test generators, stores distinct issues, reduces failing test cases, updates targets and schedules jobs across targets and generators without overloading the workstation. Configuration files and Python snippets let users customize workflows; built-in components handle common scenarios.

Its interfaces include an interactive Web UI, an interactive Text UI for Linux and Mac OS X, and a line-based dump mode for consoles on every platform. Issues and execution statistics are stored in MongoDB, with the documented default URI set to mongodb://localhost/fuzzinator. Reporting to trackers such as Bugzilla or GitHub is optional and must be configured. The framework includes only an example random content generator, so users may need external generators or custom components. Documented requirements are Python 3.10 or later and MongoDB 3.6 or later; Java SE 7 is optional for the Picireny reducer. The documentation lists Linux, macOS and Windows among tested platforms.