Agent skill
implementing-fuzz-testing-in-cicd-with-aflplusplus
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.
Install this agent skill to your Project
npx add-skill https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-fuzz-testing-in-cicd-with-aflplusplus
SKILL.md
Implementing Fuzz Testing in CI/CD with AFL++
Overview
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.
When to Use
- When deploying or configuring implementing fuzz testing in cicd with aflplusplus capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation
Prerequisites
- Linux-based CI runners (AFL++ does not support Windows natively)
- GCC or Clang compiler toolchain
- AFL++ installed (
apt install aflplusplusor built from source) - Target application with harness functions isolating input processing
- Seed corpus of valid input samples
Core Concepts
Coverage-Guided Fuzzing
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.
Instrumentation Modes
| 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
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;
}
Workflow
Step 1 --- Build the Fuzzing Harness
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;
}
Step 2 --- Compile with AFL++ Instrumentation
# 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
$CC -o fuzz_harness_cmplog fuzz_harness.c -ltarget_parser \
-fsanitize=address -DCMPLOG
Step 3 --- Prepare Seed Corpus
mkdir -p corpus/
# Add valid input samples
cp test_inputs/* corpus/
# Minimize the corpus
afl-cmin -i corpus/ -o corpus_min/ -- ./fuzz_harness @@
# Further minimize individual inputs
mkdir -p corpus_tmin/
for f in corpus_min/*; do
afl-tmin -i "$f" -o "corpus_tmin/$(basename $f)" -- ./fuzz_harness @@
done
Step 4 --- Configure CI/CD Integration
GitHub Actions:
name: Fuzz Testing
on:
push:
branches: [main]
schedule:
- cron: '0 2 * * *' # Nightly fuzzing
jobs:
fuzz:
runs-on: ubuntu-latest
timeout-minutes: 120
steps:
- uses: actions/checkout@v4
- name: Install AFL++
run: |
sudo apt-get update
sudo apt-get install -y aflplusplus
- name: Restore corpus cache
uses: actions/cache@v4
with:
path: corpus/
key: fuzz-corpus-${{ 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 @@
Step 5 --- Parallel Fuzzing for Nightly Runs
# 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 @@
Step 6 --- Crash Triage
# 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 @@
CI/CD Best Practices for AFL++
| 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 |
Monitoring Fuzzing Campaigns
# 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)
References
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
mapping-mitre-attack-techniques
Maps observed adversary behaviors, security alerts, and detection rules to MITRE ATT&CK techniques and sub-techniques to quantify detection coverage and guide control prioritization. Use when building an ATT&CK-based coverage heatmap, tagging SIEM alerts with technique IDs, aligning security controls to adversary playbooks, or reporting threat exposure to executives. Activates for requests involving ATT&CK Navigator, Sigma rules, MITRE D3FEND, or coverage gap analysis.
hunting-for-spearphishing-indicators
Hunt for spearphishing campaign indicators across email logs, endpoint telemetry, and network data to detect targeted email attacks.
analyzing-malicious-url-with-urlscan
URLScan.io is a free service for scanning and analyzing suspicious URLs. It captures screenshots, DOM content, HTTP transactions, JavaScript behavior, and network connections of web pages in an isolat
implementing-zero-standing-privilege-with-cyberark
Deploy CyberArk Secure Cloud Access to eliminate standing privileges in hybrid and multi-cloud environments using just-in-time access with time, entitlement, and approval controls.
implementing-pam-for-database-access
Deploy privileged access management for database systems including Oracle, SQL Server, PostgreSQL, and MySQL. Covers session proxy configuration, credential vaulting, query auditing, dynamic credentia
detecting-t1003-credential-dumping-with-edr
Detect OS credential dumping techniques targeting LSASS memory, SAM database, NTDS.dit, and cached credentials using EDR telemetry, Sysmon process access monitoring, and Windows security event correlation.
Didn't find tool you were looking for?