insecure-output-handling

Sanitize LLM output to prevent XSS and injection attacks.

20|Updated Feb 27, 2026

Search Tags:#xss #sanitization #command-injection #parameterized-queries #llm-security #insecure-output-handling #secure-rendering

Ready to run

npx skills add https://github.com/thejefflarson/soundcheck --skill insecure-output-handling

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: insecure-output-handling
Source: https://github.com/thejefflarson/soundcheck/tree/main/.claude/skills/insecure-output-handling
Command: npx skills add https://github.com/thejefflarson/soundcheck --skill insecure-output-handling

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Protects applications from XSS, command injection, and second-order injections by treating LLM output as untrusted across UI rendering, code execution, and downstream integrations.

Core Features & Use Cases

Safe rendering: Enforces DOM-safe sinks and escaping for all LLM-derived content.
Input sanitization: Applies universal sanitation rules to LLM outputs before consumption.
Usage scenarios: Applies to web apps, dashboards, and automation pipelines that render or execute LLM outputs.

Quick Start

Apply the insecure output guidelines to your UI rendering and downstream integrations to sanitize LLM responses and prevent unsafe execution.

Frequently Asked Questions about insecure-output-handling

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

FAQPage Schema

How do I prevent XSS when rendering LLM outputs in a web UI?▼

To prevent XSS when rendering LLM outputs, treat all generated content as untrusted input by enforcing DOM-safe sinks and applying HTML sanitization before injecting any LLM-derived text into the UI.

What is second-order injection in LLM automation pipelines?▼

Second-order injection in LLM pipelines occurs when generated outputs are passed unsanitized to downstream APIs or databases, prevented by applying parameterized SQL usage and strict allowlists for shell and database interactions.

How do I sanitize LLM responses for safe code execution?▼

Sanitizing LLM responses for code execution requires applying universal sanitation rules to the output before consumption, ensuring parameterized queries and shell allowlists are used to meet security best practices.

Do I need parameterized queries for LLM-generated SQL in dashboards?▼

Yes, you need parameterized queries for LLM-generated SQL in dashboards to prevent command and second-order injection, treating LLM output as untrusted input to ensure safe downstream database interactions.

What's the best way to secure LLM outputs across web apps and automation pipelines?▼

The best way to secure LLM outputs is treating them as untrusted input across all surfaces, implementing HTML sanitization for UI rendering, safe DOM sinks, and parameterized queries for automation pipelines.

Why does treating LLM output as untrusted input prevent command injection?▼

Treating LLM output as untrusted input prevents command injection by forcing the application to apply universal sanitation rules and shell allowlists before executing any generated commands in downstream integrations.