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Aflpp

Checked
trailofbits·vlatest·writing·CC-BY-SA-4.0

Aflpp converts Markdown to a format suitable for generating images, streamlining visual content creation from text-based sources.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add trailofbits/skills --skill aflpp -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

trailofbits

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Only contacts known or declared hosts: raw.githubusercontent.com.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    raw.githubusercontent.comsource host

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned trailofbits/skills at 82fe822625.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
82fe822625
Manifest
plugins/testing-handbook-skills/skills/aflpp/SKILL.md
Scanned
2 files, 574 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill, aflpp, likely provides capabilities related to American Fuzzy Lop (AFL), a powerful fuzzer. It can be used to automatically generate test cases for software and systems in order to discover bugs and vulnerabilities. The tool automates the process of mutating inputs and monitoring program behavior to identify crashes or unexpected outputs.

When to use it

  • Security Auditing: To proactively find security flaws in a closed-source application by fuzzing its input formats.
  • Bug Hunting: To uncover hidden bugs in open-source projects, particularly those with complex parsing logic.
  • Software Testing: As part of a broader testing strategy to improve software reliability and robustness.
  • Reverse Engineering: To understand the behavior of an unknown program by observing its responses to various inputs.

Key capabilities

  • Automated test case generation (fuzzing)
  • Mutation-based input creation
  • Crash detection and reporting

Example prompts

  • "Fuzz the image_parser binary with AFL."
  • "Generate 10,000 fuzzing inputs for the network protocol handler."
  • "Run AFL on this program and report any crashes to my email address."

Tips & gotchas

The effectiveness of AFL depends heavily on having a good understanding of the target program's input formats. You may need to provide hints or dictionaries to guide the fuzzer towards interesting code paths.