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Prompts AFL++ Coverage-Guided Fuzzing Procedure

agent security skill risk: medium

AFL++ Coverage-Guided Fuzzing Procedure

The prompt provides an overview, prerequisites, and numbered steps for instrumenting binaries and running AFL++ to discover crashes and new execution paths.

  • Policy sensitive
  • Human review
  • External action: low

SKILL 4 files · 2 folders

SKILL.md
---
name: performing-fuzzing-with-aflplusplus
description: "Perform coverage-guided fuzzing of compiled binaries using AFL++ (American Fuzzy Lop Plus Plus) to discover"
---
# Performing Fuzzing with AFL++

## Overview

AFL++ is a community-maintained fork of American Fuzzy Lop (AFL) that provides coverage-guided
fuzzing for compiled binaries. It instruments targets at compile time or via QEMU/Unicorn mode
for binary-only fuzzing, then mutates input corpora to discover new code paths. AFL++ includes
advanced scheduling (MOpt, rare), custom mutators, CMPLOG for input-to-state comparison solving,
and persistent mode for high-throughput fuzzing.


## When to Use

- When conducting security assessments that involve performing fuzzing with aflplusplus
- When following incident response procedures for related security events
- When performing scheduled security testing or auditing activities
- When validating security controls through hands-on testing

## Prerequisites

- AFL++ installed (`apt install afl++` or build from source)
- Target binary source code (for compile-time instrumentation) or QEMU mode for binary-only
- Initial seed corpus of valid inputs for the target format
- Linux system with /proc/sys/kernel/core_pattern configured

## Steps

1. Instrument the target binary with `afl-cc` or `afl-clang-fast`
2. Prepare seed corpus directory with minimal valid inputs
3. Minimize corpus with `afl-cmin` to remove redundant seeds
4. Run `afl-fuzz` with appropriate flags (-i input -o output)
5. Monitor fuzzing progress via afl-whatsup and UI stats
6. Triage crashes with `afl-tmin` minimization and CASR/GDB analysis
7. Report unique crashes with reproduction steps

## Expected Output

```
+++ Findings +++
  unique crashes: 12
  unique hangs: 3
  last crash: 00:02:15 ago
+++ Coverage +++
  map density: 4.23% / 8.41%
  paths found: 1847
  exec speed: 2145/sec
```

REQUIRED CONTEXT

  • target binary
  • seed corpus
  • AFL++ installation

EXPECTED OUTPUT

Format
markdown
Schema
text_block · Findings, Coverage
Constraints
  • include overview, when-to-use, prerequisites, numbered steps, and sample findings output

EXAMPLES

Includes one example of expected fuzzing output showing findings and coverage statistics.

QUALITY

OVERALL
0.72
CLARITY
0.90
SPECIFICITY
0.75
REUSABILITY
0.55
COMPLETENESS
0.80

USAGE

Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.

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