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safety-scan

safety-scan

github.com/ruvnetSecurity80.0k743 installsUpdated today
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About safety-scan

safety-scan is a security skill for checking untrusted content for prompt injection, jailbreaks, and other unsafe patterns. It offers quick checks, detailed threat analysis, comprehensive scans, and defense-learning operations.

Key features

  1. 01Boolean safe-or-unsafe checks
  2. 02Threat classification and confidence scores
  3. 03Multi-layer content scanning
  4. 04Learning from confirmed threats
  5. 05Detection and false-positive statistics

Use cases

  1. 01Checking user submissions before processing
  2. 02Reviewing API payloads and webhook data
  3. 03Investigating suspected instruction overrides

MCP support

Uses Ruflo MCP tools to check safety, analyze and scan threats, learn from confirmed threats, and retrieve detection statistics.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/ruvnet/ruflo --skill safety-scan

After installing, type/safety-scanin Claude Code to confirm it loaded.

Details

From the project README

Safety Scan

Scan content for prompt injection, jailbreak attempts, and unsafe patterns.

When to use

Before processing untrusted input (user submissions, API payloads, webhook data), scan it to detect prompt injection, adversarial content, or policy violations.

Steps

  1. Quick safety check — call mcp__plugin_ruflo-core_ruflo__aidefence_is_safe with the input text for a boolean safe/unsafe result
  2. Deep analysis — call mcp__plugin_ruflo-core_ruflo__aidefence_analyze for detailed threat classification and confidence scores
  3. Full scan — call mcp__plugin_ruflo-core_ruflo__aidefence_scan for comprehensive multi-layer scanning
  4. Train defenses — call mcp__plugin_ruflo-core_ruflo__aidefence_learn with confirmed threats to improve detection
  5. View stats — call mcp__plugin_ruflo-core_ruflo__aidefence_stats for detection rates and false positive metrics

Threat categories

  • Prompt injection (direct and indirect)
  • Jailbreak attempts
  • Data exfiltration patterns
  • Instruction override attacks
  • Social engineering prompts

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