# prompt-injection

> Claude/Codex skill by [@thejefflarson](https://omgskills.com/library/thejefflarson/index.md) · 20 stars · Updated 2026-07-31

Detects LLM prompts built from user input or retrieved documents that let attackers hijack the model's instructions. Use when writing code that constructs LLM prompts from user input, builds system prompts, implements RAG pipelines, or processes external documents fed to a model. Also invoke when external data can influence LLM context.

Tags: llms, security, skills

## Install

```sh
git clone https://github.com/thejefflarson/soundcheck /tmp/soundcheck && ln -s /tmp/soundcheck/.claude/skills/prompt-injection ~/.claude/skills/prompt-injection
```

## From README

Prompt Injection Security Check (OWASP LLM01:2025) What this checks Protects against attacker-controlled text that hijacks LLM instructions. Direct injection arrives through user input; indirect injection arrives through retrieved documents, emails, or tool outputs. Both can cause the model to exfiltrate data, bypass guardrails, or execute unintended actions. Vulnerable patterns User input interpolated directly into the system-role message — user text lands in the instruction tier. Retrieved documents concatenated raw into the prompt with no delimiter or trust label. Email bodies, fetched web pages, or other external content passed into the prompt with no boundary markers separating data from instructions. No structural separation between developer instructions and untrusted data — everything is one string. Raw model response returned to the caller, rendered, logged, or used to trigger a downstream action with no validation step in between.

## Links

- HTML page: https://omgskills.com/library/thejefflarson/soundcheck/dot-claude/skills/prompt-injection/
- GitHub: https://github.com/thejefflarson/soundcheck
- Author: [@thejefflarson](https://omgskills.com/library/thejefflarson/index.md)

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