prompt-injection-detector
Prompt injection detection and prevention for secure LLM applications
Best use case
prompt-injection-detector is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Prompt injection detection and prevention for secure LLM applications
Teams using prompt-injection-detector should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/prompt-injection-detector/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How prompt-injection-detector Compares
| Feature / Agent | prompt-injection-detector | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Prompt injection detection and prevention for secure LLM applications
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
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