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/prompt-injection-detector

Prompt injection detection and prevention for secure LLM applications

From plugin
babysitter
1.8k200 skills3 agents21 commands1 MCP
Install
$ npx -y skills add a5c-ai/babysitter --skill prompt-injection-detector --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/prompt-injection-detector

Context preview

The summary Claude sees to decide when to auto-load this skill.

Prompt injection detection and prevention for secure LLM applications

SKILL.md

prompt-injection-detector.SKILL.md
name: prompt-injection-detector
description: Prompt injection detection and prevention for secure LLM applications
allowed-tools:
  - Read
  - Write
  - Edit
  - Bash
  - Glob
  - Grep
graph:
  domains: [domain:software-engineering]
  specializations: [specialization:ai-agents-conversational]
  skillAreas: [skill-area:hallucination-mitigation-fact-checking, skill-area:safety-redteaming]
  roles: [role:ml-engineer, role:backend-engineer]
  workflows: [workflow:feature-development, workflow:ml-model-lifecycle]

Prompt Injection Detector Skill

Capabilities

  • Detect prompt injection attempts
  • Implement input sanitization
  • Configure detection classifiers
  • Design defense layers
  • Implement canary token detection
  • Create injection logging and alerting

Target Processes

  • prompt-injection-defense
  • tool-safety-validation

Implementation Details

Detection Methods

1. **Pattern Matching**: Known injection patterns 2. **ML Classifiers**: Trained injection detectors 3. **Canary Tokens**: Detect instruction override 4. **LLM-Based**: Use LLM to detect manipulation 5. **Perplexity Analysis**: Unusual input patterns

Defense Strategies

  • Input preprocessing
  • Prompt structure design
  • Output validation
  • Sandboxed execution
  • Multi-layer defense

Configuration Options

  • Detection threshold
  • Pattern rules
  • Classifier model
  • Action policies
  • Alerting settings

Best Practices

  • Defense in depth
  • Regular pattern updates
  • Monitor false positives
  • Test with red-team inputs

Dependencies

  • rebuff (optional)
  • transformers
  • Custom classifiers
Read more
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Enforce obedience on agentic workforces. Manage extremely complex workflows through deterministic, hallucination-free self-orchestration.

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