mohitmishra786/fuzzing

Fuzzing

Purpose

Guide agents through setting up and running coverage-guided fuzz testing: libFuzzer (in-process) and AFL++ (fork-based), with sanitizer integration and CI pipeline setup.

Triggers

• "How do I fuzz-test my parser/deserializer?"
• "What is a fuzz target / how do I write one?"
• "How do I set up libFuzzer?"
• "How do I use AFL++ on my program?"
• "How do I run fuzzing in CI?"
• "Fuzzer found a crash — how do I reproduce it?"

Workflow

1. Write a fuzz target (libFuzzer)

A fuzz target is a function that accepts arbitrary bytes and exercises the code under test.

// fuzz_parser.c
#include <stdint.h>
#include <stddef.h>
#include "myparser.h"

// Entry point called by libFuzzer with random data
int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
    // Must not abort/exit on invalid input (that's expected)
    // Must not read outside [data, data+size)

    MyParser *p = parser_create();
    if (p) {
        parser_feed(p, (const char *)data, size);
        parser_destroy(p);
    }
    return 0;  // Always return 0 (non-zero means discard input)
}

Key rules:

• Never call abort(), exit(), or use global state that persists across calls
• Handle all inputs gracefully (crash = bug found)
• Keep the target fast: the fuzzer calls it millions of times

2. Build with libFuzzer

# Clang (libFuzzer is built into Clang)
clang -fsanitize=fuzzer,address -g -O1 \
    fuzz_parser.c myparser.c -o fuzz_parser

# With UBSan too
clang -fsanitize=fuzzer,address,undefined -g -O1 \
    fuzz_parser.c myparser.c -o fuzz_parser

-fsanitize=fuzzer links libFuzzer and provides main(). Do not provide your own main() in the fuzz target.

3. Run libFuzzer

# Create corpus directory
mkdir -p corpus

# Seed with known-good inputs (greatly accelerates coverage)
cp tests/inputs/* corpus/

# Run the fuzzer
./fuzz_parser corpus/ -max_len=65536 -timeout=10

# Run for a time limit
./fuzz_parser corpus/ -max_total_time=3600

# Run with specific number of jobs (parallel)
./fuzz_parser corpus/ -jobs=4 -workers=4

# Minimise a corpus (remove redundant inputs)
./fuzz_parser -merge=1 corpus_min/ corpus/

Common flags:

| Flag | Default | Effect | |------|---------|--------| | -max_len=N | 4096 | Max input size in bytes | | -timeout=N | 1200 | Kill if single run takes > N seconds | | -max_total_time=N | 0 (forever) | Total fuzzing time | | -runs=N | -1 (infinite) | Total number of runs | | -dict=file | none | Dictionary of interesting tokens | | -jobs=N | 1 | Parallel jobs (each writes its own log) | | -merge=1 | off | Merge mode: minimise corpus |

4. Reproduce a crash

libFuzzer writes crash inputs to files named crash-<hash>, oom-<hash>, timeout-<hash>.

# Reproduce
./fuzz_parser crash-abc123

# Debug with GDB
gdb ./fuzz_parser
(gdb) run crash-abc123

5. AFL++ setup

AFL++ is a fork-based fuzzer that works on arbitrary programs (not just those with a fuzz entry point).

# Install
apt install afl++     # or build from source

# Instrument the target
CC=afl-clang-fast CXX=afl-clang-fast++ \
  cmake -S . -B build-afl -DCMAKE_BUILD_TYPE=Debug
cmake --build build-afl

# Or compile directly
afl-clang-fast -g -O1 -o prog_afl main.c myparser.c

# Create input corpus
mkdir -p afl-input afl-output
echo "hello" > afl-input/seed1

# Run
afl-fuzz -i afl-input -o afl-output -- ./prog_afl @@
# @@ is replaced with the input file path
# For stdin-based programs: remove @@
afl-fuzz -i afl-input -o afl-output -- ./prog_afl

6. AFL++ with persistent mode (faster)

Persistent mode avoids fork() per input — much faster for library fuzzing:

// In your harness:
#include "myparser.h"

int main(int argc, char **argv) {
    while (__AFL_LOOP(1000)) {
        // Read input
        unsigned char *buf = NULL;
        ssize_t len = read(0, &buf, MAX_SIZE);  // or use afl_custom_mutator
        parser_feed((char*)buf, len);
        free(buf);
    }
    return 0;
}

7. Corpus management

# AFL++ corpus minimisation
afl-cmin -i afl-output/default/queue -o corpus_min -- ./prog_afl @@

# Merge libFuzzer corpora from multiple runs
./fuzz_parser -merge=1 merged_corpus/ run1_corpus/ run2_corpus/

# Show coverage (libFuzzer)
./fuzz_parser corpus/ -runs=0 -print_coverage=1

8. CI integration

# GitHub Actions example
- name: Build fuzz targets
  run: |
    clang -fsanitize=fuzzer,address,undefined -g -O1 \
      fuzz_parser.c myparser.c -o fuzz_parser

- name: Short fuzz run (regression check)
  run: |
    ./fuzz_parser corpus/ -max_total_time=60 -error_exitcode=1
    # Also run known crash inputs if any:
    ls known_crashes/ 2>/dev/null | xargs -I{} ./fuzz_parser known_crashes/{}

For long-duration fuzzing, use OSS-Fuzz or ClusterFuzz infrastructure.

9. Structure-aware fuzzing (libFuzzer)

// Custom mutator hook — preserve format invariants
size_t LLVMFuzzerCustomMutator(uint8_t *Data, size_t Size,
                               size_t MaxSize, unsigned int Seed) {
    // Delegate to default mutator then fix up structure
    Size = LLVMFuzzerMutate(Data, Size, MaxSize);
    if (Size >= 4)
        fix_checksum(Data, Size);
    return Size;
}

// Register custom crossover for structured inputs
extern "C" size_t LLVMFuzzerCustomCrossOver(
    const uint8_t *Data1, size_t Size1,
    const uint8_t *Data2, size_t Size2,
    uint8_t *Out, size_t MaxOutSize, unsigned int Seed);

Use when naive bit-flipping breaks checksums/headers before reaching deep code paths.

10. Atheris (Python fuzzing)

import atheris
import sys

with atheris.instrument_imports():
    import myparser

def TestOneInput(data: bytes) -> None:
    fdp = atheris.FuzzedDataProvider(data)
    try:
        myparser.parse(fdp.ConsumeString(sys.maxsize))
    except myparser.ParseError:
        pass

if __name__ == "__main__":
    atheris.Setup(sys.argv, TestOneInput)
    atheris.Fuzz()

pip install atheris
python fuzz_myparser.py corpus/ -max_total_time=300

11. Dataflow tracing

# Track tainted bytes through execution (LLVM dataflow sanitizer + libFuzzer)
clang -fsanitize=fuzzer,dataflow -g -O1 fuzz.c target.c -o fuzz
LIBFUZZER_DATAFLOW_TRACE=1 ./fuzz corpus/

Produces traces showing which input bytes influenced branches — guides dictionary and structure-aware mutators.

12. OSS-Fuzz integration

OSS-Fuzz workflow
├── Add project/ in google/oss-fuzz repo (Dockerfile + build.sh)
├── Fuzz targets linked with -fsanitize=fuzzer,address
├── ClusterFuzz runs continuously on GCE
└── Crash reproducers uploaded to issue tracker

# project/Dockerfile (minimal)
FROM gcr.io/oss-fuzz-base/base-builder
RUN git clone --depth 1 https://github.com/you/yourproject
WORKDIR yourproject
COPY build.sh $SRC/

# Local OSS-Fuzz repro
python infra/helper.py build_image yourproject
python infra/helper.py build_fuzzers yourproject
python infra/helper.py run_fuzzer yourproject fuzz_target

13. Zig fuzz testing

# Zig 0.11+ built-in fuzzing
zig build test --fuzz

# fuzz target in build.zig
# .root_module.fuzz_tests = &.{
#     .{ .name = "parser", .path = "src/fuzz/parser.zig" },
# };

Zig fuzz integrates with zig test and sanitizer builds for native targets.

14. Dictionary files

Dictionaries contain interesting tokens to guide mutation:

# parser.dict
kw1="<"
kw2=">"
kw3="</"
kw4='="'
kw5="\x00"
kw6="\xff\xfe"

./fuzz_parser corpus/ -dict=parser.dict

References

For fuzz target templates, corpus seed examples, and OSS-Fuzz integration guidance, see references/targets.md.

Related skills

• Use skills/runtimes/sanitizers to add ASan/UBSan to fuzz builds
• Use skills/compilers/clang for Clang-specific libFuzzer flags
• Use skills/debuggers/gdb to debug crash inputs found by the fuzzer
• Use skills/zig/zig-testing for Zig build test --fuzz workflows
• Use skills/security/kernel-security for kernel fuzzing with syzkaller