aeondave/honggfuzz
offensive-tools/fuzzing/honggfuzz/SKILL.md
Feedback-driven, high-speed fuzzer with multi-process/thread execution and persistent fuzzing. Use for local binary fuzzing, instrumentation-guided campaigns, sanitizer-assisted triage, and corpus minimization workflows.
$ install --global
skillsauthnpx skillsauth add aeondave/malskill honggfuzzInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: May 19, 2026, 4:43 AM102.4s1 file scanned
SKILL.md
- name:
- honggfuzz
- description:
- Feedback-driven, high-speed fuzzer with multi-process/thread execution and persistent fuzzing. Use for local binary fuzzing, instrumentation-guided campaigns, sanitizer-assisted triage, and corpus minimization workflows.
- license:
- Apache-2.0
- compatibility:
- Linux, macOS, Android, BSD, Windows via Cygwin.
- author:
- GitHub Copilot
- version:
- 1.1
honggfuzz
General-purpose evolutionary fuzzer with software/hardware coverage options and strong persistent-mode performance.
Quick Start
# Compile with wrappers
./hfuzz_cc/hfuzz-clang -o target target.c
# Run with file placeholder
./honggfuzz -i seeds -- ./target ___FILE___
# Persistent mode
./honggfuzz -P -i seeds -- ./target
Operator Flow
- Start with a stable target invocation (
___FILE___or-sstdin mode). - Use compile-time instrumentation first; move to hardware counters when appropriate.
- Switch to persistent mode (
-P) as soon as target design allows. - Run corpus minimization (
-M) periodically. - Verify/replay findings with sanitizer builds and deterministic launch args.
Core Flags
| Flag | Description |
|------|-------------|
| -i <dir> | Input corpus directory |
| --output <dir> | Output directory for dynamic/minimized corpus |
| -P | Persistent mode |
| -n <threads> | Number of fuzzing threads |
| -M | Corpus minimization mode |
| -x | Disable instrumentation feedback (static mode) |
| -s | Feed input via stdin |
| --sanitizers | Enable sanitizer-aware handling (build-dependent) |
Practical Tricks
- Use
-dictfor highly structured input grammars. - Tune timeout (
-t) and mutation count per run (-r) before scaling threads. - For constrained targets, use runtime limits (
--rlimit_*) to prevent campaign collapse. - In black-box cases, compare noinst (
-x) baseline vs coverage-driven modes to validate benefit.
Common Pitfalls
- Can start from empty corpus, but structured seeds improve depth.
- Persistent mode is usually the first optimization to unlock high exec/sec.
- Use with ASAN/UBSAN/MSAN builds for reliable crash triage.
- Ignoring output naming/signature patterns slows bucketing.
- Mixing unstable target startup logic with persistent mode can hide root causes.
Triage Pattern
- Keep crash files plus command/args snapshot.
- Reproduce under sanitizer build of same target path.
- Cluster by signal + PC/stack signature, then minimize and bisect input deltas.
Resources
- https://github.com/google/honggfuzz
- https://honggfuzz.dev/
- https://github.com/google/honggfuzz/blob/master/docs/USAGE.md
- https://google.github.io/oss-fuzz/
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