V6 Prompt Injection Shield
---
id: V6
title: Prompt Injection Shield
type: pattern
category: Reliability
summary: "Treat every byte of externally-sourced text as adversarial; sanitise it on entry, mark it as data not instruction, and bound what the agent can do with it — so that a prompt smuggled inside untrusted content cannot redirect the agent's behaviour."
when_to_use: Structural and positional defences against prompt injection
also_known_as: [Input Sanitisation, Injection Defense, Anti-Hijacking, Spotlighting]
related: [V4, V5, S9]
composes_with: [V3, V8, V14, V17, V13, S5, V7]
mechanism_refs: [3, 12]
canonical: patterns/V6-Prompt-Injection-Shield.md
derived: true
---
## Description
Treat every byte of externally-sourced text as adversarial; sanitise it on entry, mark it as data not instruction, and bound what the agent can do with it — so that a prompt smuggled inside untrusted content cannot redirect the agent's behaviour. Composes with V3, V8, V14, V17, V13, S5, V7. This is a condensed digest; the canonical file (`patterns/V6-Prompt-Injection-Shield.md`) carries the full decision criteria, failure modes, and implementation.
## Key points
- the agent processes any externally-sourced text — web pages, emails, user uploads, RAG retrievals, external API responses, MCP-tool outputs;
- the agent has tools, especially any that produce side effects or external communication;
- the agent operates in a multi-agent system where one agent passes content to another (the A14 Trust Handoff anti-pattern);
- the threat model includes adversarial users *or* adversarial third parties whose content the user pulls in.
Related: [[V3-Rule-of-Two]] · [[V8-Tool-Sandboxing]] · [[V14-Trajectory-Logging]] · [[V17-Online-Eval]] · [[V13-Tool-Budget]] · [[S5-Constraint-Framing]] · [[V7-AgentSpec]] · [[V4-Dual-LLM]] · [[V5-Guardrail-Layering]] · [[S9-Constitutional-Framing]]when to use it
Community prompt sourced from the open-source GitHub repo jlldavies/go4-llm-design-patterns (MIT). A "V6 Prompt Injection Shield" style prompt — adapt the placeholders and specifics to your task. Imported as-is and not independently retested here, so check the output before relying on it.
tags
productivitycommunitydeveloper
source
jlldavies/go4-llm-design-patterns · MIT
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