prompt-injection-detector
Prompt injection detection and prevention for secure LLM applications
npx skills add a5c-ai/babysitter --skill prompt-injection-detector --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Capabilities
- Target Processes
- Implementation Details
- Detection Methods
- Defense Strategies
- Configuration Options
- Best Practices
- Dependencies
What does the prompt-injection-detector skill do?
Prompt injection detection and prevention for secure LLM applications
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill prompt-injection-detector --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.