| name | prompt-injection-defense |
| description | Defend AI systems against prompt injection and indirect prompt attacks using input controls, tool permissions, output validation, and isolation boundaries. |
| license | MIT |
| metadata | {"author":"devops-skills","version":"1.0"} |
Prompt Injection Defense
Mitigate direct and indirect prompt injection across chat apps, agentic workflows, and RAG pipelines.
Attack Surface
- User input attempting to override system instructions
- Untrusted documents/web pages in retrieval context
- Tool output that smuggles malicious instructions
- Cross-tenant leakage via shared context windows
Defense-in-Depth Pattern
- Instruction hierarchy enforcement: system > developer > user > tool output.
- Context segregation: isolate untrusted text from control instructions.
- Tool permissioning: explicit allow-list per task and tenant.
- Output policy checks: validate schema, redact secrets, block unsafe actions.
- Human approval: required for high-impact operations.
Implementation Controls
- Strip or label untrusted content blocks before generation.
- Disable autonomous tool chaining for sensitive workflows.
- Use deterministic parsers (JSON schema) before tool execution.
- Reject requests containing high-risk exfiltration patterns.
- Add canary tokens to detect data exfil attempts.
Red-Team Test Cases
- "Ignore previous instructions" style direct override
- Retrieval payload containing hidden policy bypass text
- Tool output instructing follow-up privileged command
- Prompt that asks for secrets from memory or env vars
Security Metrics
- Prompt injection detection rate
- Unsafe tool invocation prevention rate
- Time-to-containment for injection attempts
- False positive rate on blocked safe prompts
Related Skills
