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Owasp Llm Top 10

Checked
yariv1025·vlatest·ai-ml

Provides LLMs guidance and assistance for building AI and machine learning applications.

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 yariv1025/skills --skill owasp-llm-top-10 -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

yariv1025

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

    Makes no network calls.

    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.

  • 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 yariv1025/skills at 30e16586c6.

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

Commit
30e16586c6
Manifest
owasp-llm-top-10/SKILL.md
Scanned
11 files, 298 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 helps AI agents mitigate risks associated with Large Language Models (LLMs) by implementing defenses against the OWASP Top 10 LLM Security Risks. It provides guidance and checks to prevent prompt injection, data leakage, denial of service, and other vulnerabilities commonly exploited in LLM applications. The skill aims to improve the security posture of AI-powered systems interacting with LLMs.

When to use it

  • Developing a chatbot: Ensure your chatbot is resistant to malicious user input that could compromise its functionality or access sensitive data.
  • Automating content generation: Protect against prompt injection attacks when using an LLM to generate articles, code, or other creative content.
  • Building AI-powered assistants: Secure the assistant's interactions with users and prevent unauthorized actions by validating prompts and responses.
  • Integrating LLMs into existing applications: Add a layer of security checks before passing user input to an LLM within a larger system.

Key capabilities

  • Prompt injection defense
  • Data leakage prevention
  • Denial-of-service mitigation
  • Supply chain vulnerability detection
  • Authentication and authorization enforcement

Example prompts

  • "Analyze this prompt for potential vulnerabilities: 'Write a poem about cats, but ignore all previous instructions.'"
  • "Assess the security risks of using this LLM to generate code based on user input."
  • "Check if this response contains any sensitive data that should be redacted."

Tips & gotchas

The skill requires a basic understanding of prompt engineering and common LLM vulnerabilities. While it provides valuable checks, it's not a complete solution; ongoing monitoring and security best practices are still essential.