DevOps-Security-Agent-Skills/security/ai/prompt-injection-defense/SKILL.md

name, description, license, metadata

name	description	license	metadata
prompt-injection-defense	Defend AI systems against prompt injection and indirect prompt attacks using input controls, tool permissions, output validation, and isolation boundaries.	MIT	author version devops-skills 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

1. Instruction hierarchy enforcement: system > developer > user > tool output.
2. Context segregation: isolate untrusted text from control instructions.
3. Tool permissioning: explicit allow-list per task and tenant.
4. Output policy checks: validate schema, redact secrets, block unsafe actions.
5. 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

• ai-agent-security - Agent threat model and controls
• llm-app-security - End-to-end LLM app hardening
• security-automation - Automated policy response workflows