insecure-plugin-design

Enforce input validation, per-tool authorization, and audit logging in LLM tool definitions.

20|Updated Feb 27, 2026
One-click install
npx skills add https://github.com/thejefflarson/soundcheck --skill insecure-plugin-design
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: insecure-plugin-design
Source: https://github.com/thejefflarson/soundcheck/tree/main/.claude/skills/insecure-plugin-design
Command: npx skills add https://github.com/thejefflarson/soundcheck --skill insecure-plugin-design

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Prevents unsafe plugin and tool design patterns from being deployed by LLMs, addressing prompt-injection risks and misconfigurations in tool schemas and handlers.

Core Features & Use Cases

  • Enforces strict input validation at the tool boundary using explicit schemas and runtime checks to ensure only safe values reach handlers.
  • Verifies internal authorization inside the tool handler and enforces narrow tool capabilities by separating read, write, and delete operations.
  • Provides guidelines for safe plugin design across function schemas, middleware, and tool integrations to reduce attack surfaces in LLM-powered plugins.

Quick Start

Inspect your existing tool definitions and apply inline validation, per-tool authorization, and audit logging to prevent prompt-based exploits.

Frequently Asked Questions about insecure-plugin-design

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I secure LLM tool definitions against prompt injection?

To secure LLM tool definitions against prompt injection, enforce explicit input validation at the tool boundary using strict schemas and runtime checks to ensure only safe values reach handlers.

What is insecure plugin design in LLM-powered applications?

Insecure plugin design in LLM-powered applications involves unsafe patterns in tool schemas and handlers that expose attack surfaces, allowing prompt injection to manipulate tool actions or bypass authorization.

How do I implement per-tool authorization for LLM plugins?

Implement per-tool authorization for LLM plugins by verifying permissions inside the tool handler and enforcing narrow capabilities, separating read, write, and delete operations to limit potential exploit impact.

Does input validation at the tool boundary prevent prompt injection exploits?

Input validation at the tool boundary prevents prompt injection exploits by applying explicit schemas and runtime checks, ensuring that untrusted LLM outputs cannot pass malicious commands or unsafe values to internal handlers.

What are the best practices for safe plugin design in LLM tool use?

Best practices for safe plugin design in LLM tool use include applying explicit input validation, internal per-tool authorization, canonicalized paths, and audit logging to reduce attack surfaces across middleware and function schemas.

Why do I need audit logging for LLM tool handlers?

Audit logging for LLM tool handlers is needed to track executed actions, record validated inputs, and monitor for prompt-based exploits, providing an essential security trail for detecting and mitigating misconfigurations.