Prompt Injection Defense Auditor

Audit LLM prompts for injection risks and generate defensive scaffolding.

9|1|Updated Mar 15, 2026

Search Tags:#risk-assessment #guardrails #system-prompt #prompt-injection #llm-security #prompt-audit #defensive-scaffolding

Ready to run

npx skills add https://github.com/Notysoty/openagentskills --skill prompt-injection-defense-auditor

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Prompt Injection Defense Auditor
Source: https://github.com/Notysoty/openagentskills/tree/main/skills/prompt-injection-auditor
Command: npx skills add https://github.com/Notysoty/openagentskills --skill prompt-injection-defense-auditor

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Audits LLM applications and prompts to detect direct and indirect prompt injection vulnerabilities, then generates defensive scaffolding to mitigate risks.

Core Features & Use Cases

Audits system prompts and user input handling for boundary clarity, override resistance, and privilege escalation risks.
Analyzes retrieved content to detect indirect prompt injection and enforces isolation and safe handling of external data.
Produces defensive scaffolding and guardrails compatible with Claude Code, Cline, Cursor, Codex, and similar agent environments, enabling quick remediation and safer deployments.
Use Case: A team deploying an LLM-powered chatbot can run this auditor to identify injection vectors in the prompt workflow and implement a defensive template.

Quick Start

Copy this SKILL.md into your project root and run the audit against your LLM prompts to identify and defensively scaffold prompt injection protections.

Frequently Asked Questions about Prompt Injection Defense Auditor

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

FAQPage Schema

How do I detect prompt injection vulnerabilities in my LLM application?▼

You can detect prompt injection vulnerabilities by auditing system prompts and user inputs for boundary clarity, override resistance, and privilege escalation risks. This process also analyzes retrieved content to detect indirect injection and enforces safe isolation of external data.

What is indirect prompt injection and how does it affect LLM security?▼

Indirect prompt injection is a security risk where malicious instructions are hidden in retrieved content. It affects LLM security by manipulating agent behavior through external data, requiring content isolation and defensive scaffolding to prevent unauthorized actions.

Can I use this prompt auditor with Claude Code, Cline, Cursor, or Codex?▼

Yes, you can use this prompt auditor with Claude Code, Cline, Cursor, and Codex. It generates defensive scaffolding and guardrails compatible with these agent environments, enabling quick remediation and safer LLM deployments.

How to generate defensive scaffolding for system prompts?▼

To generate defensive scaffolding, run an audit against your LLM prompts to identify injection vectors in the prompt workflow. The auditor then produces defensive templates and guardrails that establish instruction boundaries and override resistance.

What is the best way to prevent data exfiltration in LLM agents?▼

The best way to prevent data exfiltration is implementing safety guardrails and instruction boundaries within system prompts. Auditing for injection flaws identifies exfiltration vectors, allowing you to defensively scaffold prompt protections against unauthorized data access.