external/anthropic-cybersecurity-skills/skills/implementing-fuzz-testing-in-cicd-with-aflplusplus/SKILL.md
Integrate AFL++ coverage-guided fuzz testing into CI/CD pipelines to discover memory corruption, input handling, and logic vulnerabilities in C/C++ and compiled applications.
npx skillsauth add seikaikyo/dash-skills implementing-fuzz-testing-in-cicd-with-aflplusplusInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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AFL++ (American Fuzzy Lop Plus Plus) is a community-maintained fork of AFL that provides state-of-the-art coverage-guided fuzz testing for discovering vulnerabilities in compiled applications. AFL++ uses genetic algorithms to mutate inputs, tracking code coverage to find new execution paths that trigger crashes, hangs, and undefined behavior. In CI/CD environments, AFL++ can be integrated to continuously test parsers, protocol handlers, file format processors, and any code that handles untrusted input. AFL++ supports persistent mode for high-speed fuzzing (up to 100,000+ executions per second), custom mutators, QEMU mode for binary-only fuzzing, and CmpLog/RedQueen for automatic dictionary extraction.
apt install aflplusplus or built from source)AFL++ instruments the target binary at compile time (or via QEMU/Frida for binary-only targets) to track which code paths each input exercises. When a mutated input triggers a new code path, it is saved to the corpus for further mutation. This feedback loop enables AFL++ to systematically explore program state space.
| Mode | Use Case | Performance |
|------|----------|-------------|
| afl-clang-fast (LTO) | Source available, best performance | Highest |
| afl-clang-fast | Source available, standard | High |
| afl-gcc-fast | GCC-based projects | High |
| QEMU mode | Binary-only, no source | Medium |
| Frida mode | Binary-only, cross-platform | Medium |
| Unicorn mode | Firmware, embedded | Low |
Persistent mode avoids fork overhead by fuzzing within a loop:
#include <unistd.h>
__AFL_FUZZ_INIT();
int main() {
__AFL_INIT();
unsigned char *buf = __AFL_FUZZ_TESTCASE_BUF;
while (__AFL_LOOP(10000)) {
int len = __AFL_FUZZ_TESTCASE_LEN;
// Process buf[0..len-1]
parse_input(buf, len);
}
return 0;
}
Create a harness that feeds AFL++ input to the target function:
// fuzz_harness.c
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "target_parser.h"
__AFL_FUZZ_INIT();
int main() {
__AFL_INIT();
unsigned char *buf = __AFL_FUZZ_TESTCASE_BUF;
while (__AFL_LOOP(10000)) {
int len = __AFL_FUZZ_TESTCASE_LEN;
if (len < 4) continue;
// Reset state between iterations
parser_context_t ctx;
parser_init(&ctx);
parser_process(&ctx, buf, len);
parser_cleanup(&ctx);
}
return 0;
}
# Standard instrumentation
export CC=afl-clang-fast
export CXX=afl-clang-fast++
# Enable AddressSanitizer for better crash detection
export AFL_USE_ASAN=1
# Build the target with instrumentation
$CC -o fuzz_harness fuzz_harness.c -ltarget_parser -fsanitize=address
# Build a CmpLog binary for better coverage
$CC -o fuzz_harness_cmplog fuzz_harness.c -ltarget_parser \
-fsanitize=address -DCMPLOG
mkdir -p corpus/
# Add valid input samples
cp test_inputs/* corpus/
# Minimize the corpus
afl-cmin -i corpus/ -o corpus_min/ -- ./fuzz_harness @@
# Further minimize individual inputs
mkdir -p corpus_tmin/
for f in corpus_min/*; do
afl-tmin -i "$f" -o "corpus_tmin/$(basename $f)" -- ./fuzz_harness @@
done
GitHub Actions:
name: Fuzz Testing
on:
push:
branches: [main]
schedule:
- cron: '0 2 * * *' # Nightly fuzzing
jobs:
fuzz:
runs-on: ubuntu-latest
timeout-minutes: 120
steps:
- uses: actions/checkout@v4
- name: Install AFL++
run: |
sudo apt-get update
sudo apt-get install -y aflplusplus
- name: Restore corpus cache
uses: actions/cache@v4
with:
path: corpus/
key: fuzz-corpus-${{ github.sha }}
restore-keys: fuzz-corpus-
- name: Build fuzzing harness
run: |
export CC=afl-clang-fast
export AFL_USE_ASAN=1
make fuzz_harness
- name: Run AFL++ fuzzing (CI mode)
env:
AFL_CMPLOG_ONLY_NEW: 1
AFL_FAST_CAL: 1
AFL_NO_STARTUP_CALIBRATION: 1
run: |
mkdir -p findings/
timeout 7200 afl-fuzz \
-S ci_fuzzer \
-i corpus/ \
-o findings/ \
-t 5000 \
-- ./fuzz_harness @@ || true
- name: Check for crashes
run: |
CRASHES=$(find findings/ -path "*/crashes/*" -not -name "README.txt" | wc -l)
echo "Found $CRASHES unique crashes"
if [ "$CRASHES" -gt 0 ]; then
echo "::error::AFL++ found $CRASHES crashes"
for crash in findings/*/crashes/*; do
[ -f "$crash" ] && echo "Crash: $crash ($(wc -c < $crash) bytes)"
done
exit 1
fi
- name: Update corpus cache
if: always()
run: |
afl-cmin -i findings/ci_fuzzer/queue/ -o corpus/ -- ./fuzz_harness @@
# Launch multiple secondary instances for better coverage
for i in $(seq 1 $(nproc)); do
afl-fuzz -S fuzzer_$i \
-i corpus/ \
-o findings/ \
-- ./fuzz_harness @@ &
done
# Wait for all fuzzers
wait
# Merge and minimize corpus
afl-cmin -i findings/*/queue/ -o corpus_merged/ -- ./fuzz_harness @@
# Reproduce and categorize crashes
for crash in findings/*/crashes/*; do
echo "=== Testing: $crash ==="
timeout 5 ./fuzz_harness_asan "$crash" 2>&1 | head -20
echo "---"
done
# Deduplicate crashes by stack trace
afl-collect findings/ crashes_deduped/ -- ./fuzz_harness @@
| Setting | CI Short Run | Nightly Long Run |
|---------|-------------|-----------------|
| Duration | 30-60 min | 4-24 hours |
| Mode | -S (secondary only) | -S (no -M for CI) |
| AFL_CMPLOG_ONLY_NEW | 1 | 1 |
| AFL_FAST_CAL | 1 | 0 |
| AFL_NO_STARTUP_CALIBRATION | 1 | 0 |
| Corpus caching | Required | Required |
| Parallel instances | 1-2 | nproc |
# View fuzzing statistics
afl-whatsup findings/
# Key metrics to track:
# - Total paths found (code coverage indicator)
# - Unique crashes / unique hangs
# - Stability percentage (should be >90%)
# - Exec speed (execs/sec)
# - Cycles done (full corpus cycles completed)
development
拋棄式 HTML mockup 比稿:產出 2 到 3 個設計立場不同的變體(密度 / 版式 / 強調軸,不是換色),各附取捨說明,最後給有立場的對比結論。適用:「畫個草圖」「比較 A 版 B 版」「先看方向再做」「給我看幾種做法」。要 production 元件或設計已定案時不適用。
tools
需求不明時的意圖萃取訪談:一次一題、每題附上自己的猜測、聽出「真正想要 vs 覺得應該要」,直到能預測使用者反應(約 95% 信心)才動工。適用:需求缺少對象 / 動機 / 成功標準 / 約束,或使用者點名「訪談我」「先確認一下」「我們確定嗎」。明確自足的指示、純資訊查詢、機械性操作不適用。
development
對非平凡決策啟動新鮮 context 對抗審查(找碴不背書),在修正還便宜的時候抓出錯誤方向。適用:高風險改動(production、資安敏感邏輯、不可逆操作)、不熟的程式碼、要宣稱「這樣是安全的 / 可行的」之前。機械性操作與一行修改不適用。
testing
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