# google/fuzzing

Tutorials, examples, discussions, research proposals, and other resources related to fuzzing

Repository: https://github.com/google/fuzzing
Canonical: https://ross.abutalabs.com/products/fuzzing
Language: C++
License: Apache-2.0
License Family: permissive
Archived: true
Last push: 2024-10-24T14:45:24+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2696, "days_push": 678, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3782, forks 448 (observed 2026-08-28T04:08:18.520649+00:00)

## What it is
A Google-hosted collection of tutorials, examples, documentation, and research proposals about fuzz testing, primarily aimed at C/C++ developers using tools like libFuzzer and AFL. It is an educational resource rather than a runnable tool, linking to related projects like OSS-Fuzz and ClusterFuzz.

## Use cases
- learn how to write fuzz targets for C++ code
- understand structure-aware fuzzing with libFuzzer
- find documentation on building fuzz targets
- compare AFL-based fuzzers
- research fuzzing techniques and glossary terms
- get started with fuzz testing for security

## When to choose
- you are learning fuzz testing from scratch
- you need reference docs on writing good fuzz targets
- you want curated links to the fuzzing ecosystem (OSS-Fuzz, ClusterFuzz, FuzzBench)

## When to avoid
- you need a runnable fuzzer tool itself
- you need continuous fuzzing infrastructure (use OSS-Fuzz or ClusterFuzz)
- you need fuzzer benchmarking (use FuzzBench)

## Facets
- artifact type: learning-resource
- maturity: active
- function: fuzzing, testing, security, developer-tools
- domain: security, testing, developer-tools, tutorials
- platform: cross-platform, cpp
- tags: fuzz-testing, libfuzzer, afl, tutorials, documentation, research

## Member repositories
- google/fuzzing (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:18.520649+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:27:43.579387+00:00, confidence not recorded.
  - readme: https://github.com/google/fuzzing (fetched 2026-08-28T04:08:18.520649+00:00, sha 0a2fc869bda5)
- Data as of 2026-08-30T08:39:29.467469+00:00.