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ossfuzz

Enrolls a project in OSS-Fuzz, Google's free continuous fuzzing service for open source, and drives it locally. Covers project.yaml, Dockerfile and build.sh setup, the helper scripts, reproducing OSS-Fuzz crash reports, and the acceptance criteria. Use when setting up continuous fuzzing for an open-source project, reproducing an OSS-Fuzz bug report, or testing an OSS-Fuzz build before submitting it.

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
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name ossfuzz type technique description Enrolls a project in OSS-Fuzz, Google's free continuous fuzzing service for open source, and drives it locally. Covers project.yaml, Dockerfile and build.sh setup, the helper scripts, reproducing OSS-Fuzz crash reports, and the acceptance criteria. Use when setting up continuous fuzzing for an open-source project, reproducing an OSS-Fuzz bug report, or testing an OSS-Fuzz build before submitting it. OSS-Fuzz OSS-Fuzz is an open-source project developed by Google that provides free distributed infrastructure for continuous fuzz testing. It streamlines the fuzzing process and facilitates simpler modifications. While only select projects are accepted into OSS-Fuzz, the project's core is open-source, allowing anyone to host their own instance for private projects. Overview OSS-Fuzz provides a simple CLI framework for building and starting harnesses or calculating their coverage. Additionally, OSS-Fuzz can be used as a service that hosts static web pages generated from fuzzing outputs such as coverage information. Key Concepts Concept Description helper.py CLI script for building images, building fuzzers, and running harnesses locally Base Images Hierarchical Docker images providing build dependencies and compilers project.yaml Configuration file defining project metadata for OSS-Fuzz enrollment Dockerfile Project-specific image with build dependencies build.sh Script that builds fuzzing harnesses for your project Criticality Score Metric used by OSS-Fuzz team to evaluate project acceptance When to Apply Apply this technique when: Setting up continuous fuzzing for an open-source project Need distributed fuzzing infrastructure without managing servers Want coverage reports and bug tracking integrated with fuzzing Testing existing OSS-Fuzz harnesses locally Reproducing crashes from OSS-Fuzz bug reports Skip this technique when: Project is closed-source (unless hosting your own OSS-Fuzz instance) Project doesn't meet OSS-Fuzz's criticality score threshold Need proprietary or specialized fuzzing infrastructure Fuzzing simple scripts that don't warrant infrastructure Quick Reference Task Command Clone OSS-Fuzz git clone https://github.com/google/oss-fuzz Build project image uv run --no-project python infra/helper.py build_image --pull <project> Build fuzzers with ASan uv run --no-project python infra/helper.py build_fuzzers --sanitizer=address <project> Run specific harness uv run --no-project python infra/helper.py run_fuzzer <project> <harness> Generate coverage report uv run --no-project python infra/helper.py coverage <project> Check helper.py options uv run --no-project python infra/helper.py --help OSS-Fuzz Project Components OSS-Fuzz provides several publicly available tools and web interfaces: Bug Tracker The bug tracker allows you to: Check bugs from specific projects (initially visible only to maintainers, later made public ) Create new issues and comment on existing ones Search for similar bugs across all projects to understand issues Build Status System The build status system helps track: Build statuses of all included projects Date of last successful build Build failures and their duration Fuzz Introspector Fuzz Introspector displays: Coverage data for projects enrolled in OSS-Fuzz Hit frequency for covered code Performance analysis and blocker identification Read this case study for examples and explanations. Step-by-Step: Running a Single Harness You don't need to host the whole OSS-Fuzz platform to use it. The helper script makes it easy to run individual harnesses locally. Step 1: Clone OSS-Fuzz git clone https://github.com/google/oss-fuzz cd oss-fuzz uv run --no-project python infra/helper.py -- help Step 2: Build Project Image uv run --no-project python infra/helper.py build_image --pull <project-name> This downloads and builds the base Docker image for the project. Step 3: Build Fuzzers with Sanitizers uv run --no-project python infra/helper.py build_fuzzers --sanitizer=address <project-name> Sanitizer options: --sanitizer=address for AddressSanitizer with LeakSanitizer Other sanitizers available (language support varies) Note: Fuzzers are built to /build/out/<project-name>/ containing the harness executables, dictionaries, corpus, and crash files. Step 4: Run the Fuzzer uv run --no-project python infra/helper.py run_fuzzer <project-name> <harness-name> [<fuzzer-args>] The helper script automatically runs any missed steps if you skip them. Step 5: Coverage Analysis (Optional) First, install gsutil (skip gcloud initialization). uv run --no-project python infra/helper.py build_fuzzers --sanitizer=coverage <project-name> uv run --no-project python infra/helper.py coverage <project-name> Use --no-corpus-download to use only local corpus. The command generates and hosts a coverage report locally. See official OSS-Fuzz documentation for details. Common Patterns Pattern: Running irssi Example Use Case: Testing OSS-Fuzz setup with a simple enrolled project # Clone and navigate to OSS-Fuzz git clone https://github.com/google/oss-fuzz cd oss-fuzz # Build and run irssi fuzzer uv run --no-project python infra/helper.py build_image --pull irssi uv run --no-project python infra/helper.py build_fuzzers --sanitizer=address irssi uv run --no-project python infra/helper.py run_fuzzer irssi irssi-fuzz Expected Output: INFO:__main__:Running: docker run --rm --privileged --shm-size=2g --platform linux/amd64 -i -e FUZZING_ENGINE=libfuzzer -e SANITIZER=address -e RUN_FUZZER_MODE=interactive -e HELPER=True -v /private/tmp/oss-fuzz/build/out/irssi:/out -t gcr.io/oss-fuzz-base/base-runner run_fuzzer irssi-fuzz. Using seed corpus: irssi-fuzz_seed_corpus.zip /out/irssi-fuzz -rss_limit_mb=2560 -timeout=25 /tmp/irssi-fuzz_corpus -max_len=2048 < /dev/null INFO: Running with entropic power schedule (0xFF, 100). INFO: Seed: 1531341664 INFO: Loaded 1 modules (95687 inline 8-bit counters): 95687 [0x1096c80, 0x10ae247), INFO: Loaded 1 PC tables (95687 PCs): 95687 [0x10ae248,0x1223eb8), INFO: 719 files found in /tmp/irssi-fuzz_corpus INFO: seed corpus: files: 719 min: 1b max: 170106b total: 367969b rss: 48Mb #720 INITED cov: 409 ft: 1738 corp: 640/163Kb exec/s: 0 rss: 62Mb #762 REDUCE cov: 409 ft: 1738 corp: 640/163Kb lim: 2048 exec/s: 0 rss: 63Mb L: 236/2048 MS: 2 ShuffleBytes-EraseBytes- Pattern: Enrolling a New Project Use Case: Adding your project to OSS-Fuzz (or private instance) Create three files in projects/<your-project>/ : 1. project.yaml - Project metadata: homepage: "https://github.com/yourorg/yourproject" language: c++ primary_contact: "your-email@example.com" main_repo: "https://github.com/yourorg/yourproject" fuzzing_engines: - libfuzzer sanitizers: - address - undefined 2. Dockerfile - Build dependencies: FROM gcr.io/oss-fuzz-base/base-builder RUN apt-get update && apt-get install -y \ autoconf \ automake \ libtool \ pkg-config RUN git clone --depth 1 https://github.com/yourorg/yourproject WORKDIR yourproject COPY build.sh $SRC/ 3. build.sh - Build harnesses: #!/bin/bash -eu ./autogen.sh ./configure --disable-shared make -j$( nproc ) # Build harnesses $CXX $CXXFLAGS -std=c++11 -I. \ $SRC /yourproject/fuzz/harness.cc -o $OUT /harness \ $LIB_FUZZING_ENGINE ./libyourproject.a # Copy corpus and dictionary if available cp $SRC /yourproject/fuzz/corpus.zip $OUT /harness_seed_corpus.zip cp $SRC /yourproject/fuzz/dictionary.dict $OUT /harness.dict Docker Images in OSS-Fuzz Harnesses are built and executed in Docker containers. All projects share a runner image, but each project has its own build image. Image Hierarchy Images build on each other in this sequence: base_image - Specific Ubuntu version base_clang - Clang compiler; based on base_image base_builder - Build dependencies; based on base_clang Language-specific variants: base_builder_go , etc. See /oss-fuzz/infra/base-images/ for full list Your project Docker image - Project-specific dependencies; based on base_builder or language variant Runner Images (Used Separately) base_runner - Executes harnesses; based on base_clang base_runner_debug - With debug tools; based on base_runner Advanced Usage Tips and Tricks Tip Why It Helps Don't manually copy source code Project Dockerfile likely already pulls latest version Check existing projects Browse oss-fuzz/projects for examples Keep harnesses in separate repo Like curl-fuzzer - cleaner organization Use specific compiler versions Base images provide consistent build environment Install dependencies in Dockerfile May require approval for OSS-Fuzz enrollment Criticality Score OSS-Fuzz uses a criticality score to evaluate project acceptance. See this example for how scoring works. Projects with lower scores may still be added to private OSS-Fuzz instances. Hosting Your Own Instance Since OSS-Fuzz is open-source, you can host your own instance for: Private projects not eligible for public OSS-Fuzz Projects with lower criticality scores Custom fuzzing infrastructure needs Anti-Patterns Anti-Pattern Problem Correct Approach Manually pulling source in build.sh Doesn't use latest version Let Dockerfile handle git clone Copying code to OSS-Fuzz repo Hard to maintain, violates separation Reference external harness repo Ignoring base image versions Build inconsistencies Use provided base images and compilers Skipping local testing Wastes CI resources Use helper.py locally before PR Not checking build status Unnoticed build failures Monitor build status page regularly Tool-Specific Guidance libFuzzer OSS-Fuzz primarily uses libFuzzer as the fuzzing engine for C/C++ projects. Harness signature: extern "C" int LLVMFuzzerTestOneInput ( const uint8_t *data, size_t size) { // Your fuzzing logic return 0 ; } Build in build.sh: $CXX $CXXFLAGS -std=c++11 -I. \ harness.cc -o $OUT /harness \ $LIB_FUZZING_ENGINE ./libproject.a Integration tips: Use $LIB_FUZZING_ENGINE variable provided by OSS-Fuzz Include -fsanitize=fuzzer is handled automatically Link against static libraries when possible AFL++ OSS-Fuzz supports AFL++ as an alternative fuzzing engine. Enable in project.yaml: fuzzing_engines: - afl - libfuzzer Integration tips: AFL++ harnesses work alongside libFuzzer harnesses Use persistent mode for better performance OSS-Fuzz handles engine-specific compilation flags Atheris (Python) For Python projects with C extensions. Example from cbor2 integration : Harness: import atheris import sys import cbor2 @atheris.instrument_func def TestOneInput ( data ): fdp = atheris.FuzzedDataProvider(data) try : cbor2.loads(data) except (cbor2.CBORDecodeError, ValueError): pass def main (): atheris.Setup(sys.argv, TestOneInput) atheris.Fuzz() if __name__ == "__main__" : main() Build in build.sh: # allow-legacy-python: build.sh runs inside the oss-fuzz container, where the shims are absent. pip3 install .
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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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