Skip to content
TrustedSkillsbeta
Back to all skills

Llm App Security

Checked
bagelhole·vlatest·ai-ml·MIT

Helps with LLMs, security as part of building AI and machine learning applications workflows.

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 bagelhole/devops-security-agent-skills --skill llm-app-security -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

bagelhole

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 bagelhole/devops-security-agent-skills at 0365f57a07.

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

Commit
0365f57a07
Manifest
security/ai/llm-app-security/SKILL.md
Scanned
1 file, 954 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 analyze and improve the security of applications that utilize Large Language Models (LLMs). It identifies potential vulnerabilities related to prompt injection, data leakage, and insecure API usage. The tool provides actionable recommendations for mitigating these risks and strengthening overall application resilience.

When to use it

  • Security Audits: Integrate this skill into automated workflows to regularly assess LLM-powered applications for security flaws.
  • Prompt Engineering Review: Evaluate newly designed prompts or prompt templates to proactively identify potential vulnerabilities before deployment.
  • API Integration Checks: Analyze how your application interacts with external LLM APIs, ensuring secure configurations and data handling practices.
  • Post-Incident Analysis: Investigate security incidents involving LLMs to determine root causes and prevent future occurrences.

Key capabilities

  • Prompt Injection Detection
  • Data Leakage Prevention
  • Insecure API Usage Identification
  • Vulnerability Remediation Recommendations

Example prompts

  • "Analyze this prompt for potential injection vulnerabilities: 'Translate the following text into French: [user input]'"
  • "Review my application's code and identify any insecure practices when interacting with the OpenAI API."
  • "What are the common data leakage risks associated with using LLMs in a customer service chatbot?"

Tips & gotchas

This skill requires a solid understanding of basic security principles. While it provides recommendations, implementing them effectively may necessitate adjustments to your application's code and architecture.