skill-security-framing

Validate URLs and sanitize fetched content before AI analysis.

Updated Jun 16, 2026

Search Tags:#security #web security #url validation #prompt injection #ai safety #content sanitization

Ready to run

npx skills add https://github.com/burgebj/claudeoctopus --skill skill-security-framing-burgebj

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: skill-security-framing
Source: https://github.com/burgebj/claudeoctopus/tree/main/.claude/skills/skill-security-framing
Command: npx skills add https://github.com/burgebj/claudeoctopus --skill skill-security-framing-burgebj

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill prevents security risks when analyzing untrusted external content by applying URL validation, content sanitization, and safe handling patterns before AI processing.

Core Features & Use Cases

URL Security Validation: Checks protocols, hostnames, private network targets, malicious patterns, and unsafe URLs before external content retrieval.
Content Sanitization Framework: Wraps fetched content as untrusted data and prevents embedded instructions from influencing analysis workflows.
Use Case: Use this Skill when an AI agent needs to analyze articles, documents, links, or API responses while reducing prompt injection and data exposure risks.

Quick Start

Apply the security framing skill when analyzing content fetched from an external URL.

Frequently Asked Questions about skill-security-framing

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

FAQPage Schema

How do I prevent prompt injection when an AI agent analyzes external web content?▼

To prevent prompt injection during web content analysis, you must sanitize fetched data by wrapping it as untrusted data and applying protocol checks, hostname filtering, and size limits before processing.

What is URL validation for AI workflows and why do I need it?▼

URL validation for AI workflows is the process of checking protocols, hostnames, and private network targets before retrieving external content, preventing unsafe processing of malicious links and blocking data exposure risks.

How to securely process untrusted API responses and webhook payloads in AI workflows?▼

Securely process untrusted API responses by applying content sanitization frameworks that enforce size limits and prompt injection defenses, isolating webhook payloads as untrusted data before AI analysis begins.

Does this content sanitization approach block private network targets during URL retrieval?▼

Yes, secure content framing includes hostname filtering that actively blocks private network targets and malicious patterns, preventing external documents from triggering unsafe internal network requests.

When should I apply security framing to external documents and fetched articles?▼

Apply security framing whenever an AI agent retrieves and analyzes external articles, documents, or API responses, ensuring all fetched content is treated as untrusted data to reduce data exposure risks.