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Prompt Injection Scanner

Checked
jorgealves·vlatest·security·GPL-3.0

This scanner analyzes prompts for potential vulnerabilities like prompt injection attacks, safeguarding your AI applications from malicious manipulation.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add jorgealves/agent_skills --skill prompt-injection-scanner -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

jorgealves

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned jorgealves/agent_skills at 85014ad37d.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
85014ad37d
Manifest
prompt-injection-scanner/SKILL.md
Scanned
2 files, 80 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

The prompt-injection-scanner skill analyzes input data to detect and flag potential prompt injection attempts before they reach an AI model. It helps secure agent interactions by identifying malicious patterns designed to bypass safety filters or alter intended behavior.

When to use it

  • User-generated content: Scan comments, forum posts, or chat logs before processing them with an LLM.
  • Dynamic data ingestion: Filter untrusted external APIs or database entries that might contain hidden instructions.
  • Public-facing agents: Protect customer support bots or public assistants from adversarial attacks.
  • Pre-deployment testing: Validate new prompt templates against known injection vectors to ensure robustness.

Key capabilities

  • Identifies structural anomalies in input strings typical of injection attacks.
  • Flags suspicious keywords and formatting tricks used to override system instructions.
  • Provides clear alerts when potentially harmful payloads are detected in user inputs.

Example prompts

  • "Scan this batch of customer reviews for any prompt injection attempts before I feed them into the sentiment analysis model."
  • "Check if these dynamically generated API responses contain hidden commands trying to manipulate my agent's logic."
  • "Analyze this forum thread to detect users attempting to bypass safety filters through indirect instruction embedding."

Tips & gotchas

Ensure you have a baseline of legitimate input patterns to distinguish between false positives and actual threats. This skill is most effective when integrated early in the data pipeline, before any generative processing occurs.