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Prompt Injection Detector

DevOps & Security
prompt-injection-detector

Prompt Injection Detector is an Agent Skill for devops & security published in the a5c-ai/babysitter repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Prompt injection detection and prevention for secure LLM applications

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/ai-agents-conversational/skills/prompt-injection-detector/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/prompt-injection-detector, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

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