---
name: prompt-injection-detector-f3ff6727f363
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


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering
Roles: Data Scientist, ML / AI Engineer, Security Engineer
Specialties: API
Tools: Transformers
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 3e5d18004702b73d64dc16b964806b1956fb10909a6e13899e26ebf0c2426284