攻击性模糊测试

SKILL运维和基础设施
运维和基础设施 未标注权限 许可证不明确
安装这个技能

自动化攻击性模糊测试流程,帮助AI Agent进行攻击性模糊测试工作。

一句话选型结论

按资料字段整理

攻击性模糊测试是运维和基础设施类的 Agent Skill,安装方式未标注,权限级别未标注,许可证不明确,有可直接复制的安装命令。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类运维和基础设施 安装方式未标注 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
GitHub 星标 未标注 仓库 未标注
下载量 未标注 技能市场下载数
权限级别 未标注权限 安装前请确认风险范围
许可证 许可证不明确 未说明

安装

未标注
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
skillhub install user_3c6cb52e/offensive-fuzzing-hcws
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    执行上面的命令安装;也可以把仓库里的 SKILL.md 手动放进客户端的 skills 目录。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

运维和基础设施
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方user_3c6cb52e仓库 未标注 · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式未标注安装包 community
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验未标注上游最后核验时间 · 数据更新 1791622460
正文体量11.8 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
查服务与日志

按现象定位服务状态与日志线索。

写部署与巡检脚本

把重复的部署、巡检写成脚本。

故障排查

按步骤排查网络、依赖与配置。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 有可直接复制的安装命令,不用自己翻文档找入口。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在运维和基础设施场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
攻击性模糊测试当前 未标注权限 未标注 未标注 未标注 有
Azure 部署 调用外部服务 GitHub 仓库 664.2 万 Codex、Claude、Cursor 有
Azure diagnostics 问题管理 调用外部服务 GitHub 仓库 657.6 万 Codex、Claude、Cursor 有
Azure aigateway 部署 调用外部服务 GitHub 仓库 655.3 万 Codex、Claude、Cursor 有
Azure AI 搜索 调用外部服务 GitHub 仓库 651.4 万 Codex、Claude、Cursor 有
Azure Cloud migrate 部署 调用外部服务 GitHub 仓库 650.7 万 Codex、Claude、Cursor 有

SKILL.md 全文

11.8 KB
模型实际读到的说明书原文,来自技能仓库

Offensive Fuzzing

Fuzzer Types

Type Coverage Speed Tools
BlackBox Poor Fast Peach, Boofuzz
GreyBox Good Fast AFL++, Honggfuzz, libFuzzer, WinAFL
Snapshot Good Fastest Nyx, wtf, Snapchange
WhiteBox Best Slow KLEE, QSYM, SymSan
Ensemble Best Fast AFL++ + Honggfuzz + libFuzzer

GreyBox sub-variants: Directed (AFLGo, UAFuzz), Grammar (AFLSmart, Tlspuffin), Concolic (QSYM, Driller), Kernel (syzkaller, kAFL, wtf).

Core Workflow

Research target → Choose analyses → Build harness → Seed corpus → Instrument → Fuzz → Triage crashes → Report

1. Research Target

  • Map all input surfaces (files, network, IPC, syscalls, IOCTL)
  • Identify high-value areas: previously patched code, complex parsers, newly added code, input ingestion points
  • For kernel modules: look beyond copy_from_user — DMA-BUF ops, page fault handlers, VM operation structs, allocation callbacks

2. Instrument and Build

# AFL++ (preferred for GreyBox)
CC=afl-clang-fast CXX=afl-clang-fast++ cmake -DCMAKE_BUILD_TYPE=Release .. && make -j

# libFuzzer + ASan/UBSan (C/C++)
cmake -DCMAKE_CXX_FLAGS="-fsanitize=fuzzer,address,undefined -O1 -g" ..

# CmpLog build for hard compares
AFL_LLVM_CMPLOG=1 CC=afl-clang-fast CXX=afl-clang-fast++ make clean all

Windows (MSVC): Project Properties → C/C++ → Address Sanitizer: Yes (/fsanitize=address)

3. Write Harness

libFuzzer (C++):

#include <cstdint>
#include <cstddef>
extern "C" int LLVMFuzzerTestOneInput(const uint8_t* data, size_t size) {
    parse_or_process(data, size);
    return 0;
}

Honggfuzz HF_ITER (persistent mode — preferred for large targets):

#include "honggfuzz.h"
int main(int argc, char** argv) {
    initialize_target(); // runs once
    for (;;) {
        size_t len; uint8_t *buf;
        HF_ITER(&buf, &len);
        FILE* s = fmemopen(buf, len, "r");
        target_function(s);
        fclose(s);
        reset_target_state();
    }
}

AFL++ persistent mode (__AFL_LOOP):

while (__AFL_LOOP(10000)) {
    // re-read input and process
}

macOS IPC (Mach message fuzzing):

void *lib_handle = dlopen("libexample.dylib", RTLD_LAZY);
pFunction = dlsym(lib_handle, "DesiredFunction");

4. Build Seed Corpus

  • Pull from target's test suite, bug reports, and real-world samples
  • Web-crawl (Common Crawl) for file formats; filter by MIME type
  • Minimize: afl-cmin -i raw_corpus -o seeds -- ./target @@
  • Trim inputs: afl-tmin -i crash -o crash.min -- ./target @@

5. Launch Fuzzing

AFL++ parallel (primary + secondary with cmplog):

afl-fuzz -M f1 -i seeds -o findings -x dict.txt -- ./target @@
afl-fuzz -S s1 -i seeds -o findings -c 0 -- ./target @@

libFuzzer:

./target_libfuzzer corpus/ -max_total_time=3600 -workers=4

Binary-only (QEMU):

afl-fuzz -Q -i seeds -o findings -- target.exe @@

Snapshot (AFL++ Nyx):

NYX_MODE=1 AFL_MAP_SIZE=1048576 afl-fuzz -i seeds -o findings -- ./target_nyx @@

Ensemble (AFL++ + Honggfuzz sharing corpus):

# Terminal 1
afl-fuzz -M fuzzer1 -i seeds -o sync_dir -- ./target @@
# Terminal 2
../honggfuzz/honggfuzz -i sync_dir/fuzzer1/queue -W sync_dir/hfuzz \
  --linux_perf_ipt_block -t 10 -- ./target ___FILE___

6. Monitor and Unstick

If progress stalls:
- Enable CmpLog: -c 0 on AFL++ secondaries
- Add dictionary: -x dict.txt or AFL_TOKEN_FILE
- Switch to directed fuzzing (AFLGo) targeting specific BBs/functions
- Use concolic assistance (QSYM, Driller) on hard branches
- Snapshot the target to increase exec/s
- AFL_MAP_SIZE=1048576, -L 0 for MOpt scheduler

7. Triage Crashes

# 1. Minimize
afl-tmin -i crash -o crash.min -- ./target @@
# 2. Symbolize
ASAN_OPTIONS=abort_on_error=1:symbolize=1 ./target crash.min 2>asan.log
# 3. Hash + bucket
./cov-tool --bbids ./target crash.min > cov.hash
./bucket.py --key "$(cat cov.hash)" --log asan.log --out triage/

Sanitizer env quick reference:

ASAN_OPTIONS=abort_on_error=1:symbolize=1:detect_stack_use_after_return=1
UBSAN_OPTIONS=print_stacktrace=1:halt_on_error=1
TSAN_OPTIONS=halt_on_error=1:history_size=7
MSAN_OPTIONS=poison_in_dtor=1:track_origins=2

Oracle Selection

Bug Class Oracle
Memory safety ASan, HWASan (AArch64, lower overhead)
Uninitialized reads MSan
Concurrency TSan
Undefined behavior UBSan
Type safety TypeSan
Heap hardening Scudo Hardened Allocator
Logic bugs Differential / idempotency oracles
Kernel memory KASAN, KMSAN, KCSAN
Kernel UB KUBSan (CONFIG_UBSAN_TRAP=y)
CFI KCFI (-fsanitize=kcfi, Clang 18)
Binary-only QASAN (QEMU+ASan), DynamoRIO

Property oracle patterns:
- Idempotency: f(x) == f(f(x))
- Differential: compare two impls, bucket on output mismatch
- Invariants: monotonic lengths, checksum equality, schema validation post-parse

Specialized Targets

Kernel (Linux) — syzkaller

{
  "target": "linux/arm64",
  "http": ":56700",
  "workdir": "/path/to/workdir",
  "kernel_obj": "/path/to/kernel",
  "image": "/path/to/rootfs.ext3",
  "sshkey": "/path/to/id_rsa",
  "procs": 8,
  "enable_syscalls": ["openat$module_name", "ioctl$IOCTL_CMD", "mmap"],
  "type": "qemu",
  "vm": { "count": 4, "cpu": 2, "mem": 2048 }
}
  • Limit enable_syscalls to deepen coverage on specific subsystems
  • Use syz-extract to pull constants for custom modules
  • Enable CONFIG_KASAN=y, CONFIG_KCFI=y, CONFIG_DEBUG_INFO_BTF=y
  • Use kcov filters and syz_cover_filter to direct coverage
  • Network fuzzing: inject via TUN/TAP + pseudo-syscalls (syz_emit_ethernet)
  • Crash decode: ./scripts/decode_stacktrace.sh vmlinux ... < dmesg.log

syzkaller repro:

syz-execprog -repeat=0 -procs=1 -cover=0 -debug target.repro

EDR / Windows Scanning Engines

WTF snapshot harness skeleton (mpengine.dll / mini-filter):

g_Backend->SetBreakpoint("nt!KeBugCheck2", [](Backend_t *Backend) {
    const uint64_t BCode = Backend->GetArg(0);
    Backend->Stop(Crash_t(fmt::format("crash-{:#x}", BCode)));
});

FilterConnectionPort fuzzing:

HANDLE hPort;
FilterConnectCommunicationPort(L"\\PortName", 0, NULL, 0, NULL, &hPort);
FilterSendMessage(hPort, fuzzData, sizeof(fuzzData), NULL, 0, &bytesReturned);

IOCTL fuzzing pattern:

HANDLE hDev = CreateFile(L"\\\\.\\DeviceName", GENERIC_READ|GENERIC_WRITE, ...);
DeviceIoControl(hDev, ioctlCode, inputBuf, inputLen, outBuf, outLen, &ret, NULL);
  • Take snapshots after initialization, right before parse/dispatch loop
  • Use IDA Lighthouse for coverage visualization
  • Monitor: DRIVER_VERIFIER_DETECTED_VIOLATION (0xc4), IRQL_NOT_LESS_OR_EQUAL (0xa)
  • WinDbg: .symfix; !analyze -v; k; !heap -p -a @rax

Cross-platform mpengine.dll on Linux (loadlibrary + HF_ITER + Intel PT):

// Bypass Lua VM to avoid stability issues
insert_function_redirect((void*)luaV_execute_address, my_lua_exec, HOOK_REPLACE_FUNCTION);
for (;;) {
    HF_ITER(&buf, &len);
    ScanDescriptor.UserPtr = fmemopen(buf, len, "r");
    __rsignal(&KernelHandle, RSIG_SCAN_STREAMBUFFER, &ScanParams, sizeof ScanParams);
}

Rust

# Full Rust fuzzing pipeline
cargo test                                         # 1. property tests
cargo +nightly miri test                           # 2. UB via interpreter
cargo +nightly careful test                        # 3. runtime bounds checks
cargo fuzz run fuzz_target_1 -- -max_total_time=3600  # 4. libFuzzer crashes
RUSTFLAGS="--cfg loom" cargo test --release        # 5. concurrency (if needed)
cargo fuzz coverage fuzz_target_1                  # 6. coverage report

Focus unsafe blocks on: Vec::from_raw_parts, unchecked indexing, transmute size mismatches, pointer arithmetic, FFI integer truncation.

Embedded / Binary-Only

  • LibAFL: Modular Rust framework; Unicorn engine, snapshot module, LBRFeedback (zero-instrumentation on Intel), SAND decoupled sanitization
  • Retrowrite / QASAN: Binary rewriting for coverage + ASan without source
  • Nautilus: Grammar-based fuzzing for structured formats

Language Ecosystems

  • Go 1.18+: go test -fuzz=Fuzz -run=^$ ./...
  • Python: Atheris (CPython native extension fuzzing)
  • Rust: cargo-fuzz or honggfuzz-rs
  • JS engines: Fuzzilli with extended instrumentation (__builtin_return_address(0) for PC tracking)
  • Wasm runtimes: wasmtime-fuzz, wafl for differential fuzzing across V8/Wasmer/Wasmtime
  • Smart contracts: Echidna, Foundry-fuzz (Solidity); Move-Fuzz (Aptos/Sui)

CI/CD Integration

- name: Build with afl-clang-fast
  run: CC=afl-clang-fast make -j
- name: Fuzz (smoke, 15 min)
  run: timeout 15m afl-fuzz -i seeds -o findings -- ./target @@ || true
- name: Upload crashes
  if: always()
  uses: actions/upload-artifact@v4
  with:
    path: findings/**/crashes/*

Use ClusterFuzzLite for persistent continuous fuzzing; cache corpora between runs.

Crash Analysis Quick Reference

Linux:

ulimit -c unlimited && sysctl -w kernel.core_pattern=core.%e.%p
gdb -q ./target core.* -ex 'bt' -ex 'info reg' -ex q
addr2line -e ./target 0xDEADBEEF

Windows:

# Enable local dumps
New-Item 'HKLM:\SOFTWARE\Microsoft\Windows\Windows Error Reporting\LocalDumps' -Force
# PageHeap
gflags /p /enable target.exe /full

Kernel KASAN/KMSAN:

dmesg -T | egrep -i 'kasan|kmsan' -A 60
./scripts/decode_stacktrace.sh vmlinux /lib/modules/$(uname -r)/build < dmesg.log

Reproducibility: pin CPU governor, disable ASLR only where safe, fix RNG seeds, save input sequences in persistent mode, record binary hashes and sanitizer options with every crash.

Tool Index

Tool Use Case
AFL++ General GreyBox, CmpLog, MOpt, Nyx
Honggfuzz Intel PT, crash detection, HF_ITER
libFuzzer In-process, source available
syzkaller Linux/Windows kernel syscall fuzzing
wtf Snapshot fuzzing, Windows targets
Nyx AFL++ snapshot mode (Intel PT)
Snapchange AWS snapshot fuzzing
LibAFL Custom Rust fuzzing framework
AFLGo Directed fuzzing to target BB/function
kAFL Kernel + OS fuzzing
Jackalope Binary coverage-guided (Windows/macOS)
cargo-fuzz Rust libFuzzer integration
Atheris Python fuzzing
Nautilus Grammar-based fuzzing
AFLTriage Automated crash triage
afl-cov Coverage analysis for AFL++
ClusterFuzz Distributed fuzzing infrastructure

常见问题

5 问
答案由本站依据条目字段整理
攻击性模糊测试怎么安装?

在支持 Skills 的客户端里执行:skillhub install user_3c6cb52e/offensive-fuzzing-hcws。安装方式为未标注。装完刷新技能列表或重开客户端。

攻击性模糊测试支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

攻击性模糊测试权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

攻击性模糊测试收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

攻击性模糊测试用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

热门专题

按场景挑技能
全部专题 →

技能内容以作者仓库为准;分类、权限级别与许可证状态的中文说明为中文整理,最近核验 未标注。