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Prompt Injection — Guardrails and Fix Patterns

🧭 Quick Return to Map

You are in a sub-page of Safety_PromptIntegrity.
To reorient, go back here:

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

A focused guide to handle prompt injection attacks in RAG, agents, and orchestration.
Use this page when injected text hijacks your instructions, bypasses schema, or makes the model ignore contracts.


When to open this page

  • Responses contain leaked system prompt or hidden instructions.
  • Model obeys malicious user text like “ignore above and do X”.
  • Citations vanish after injection payload.
  • JSON / tool schema is broken by arbitrary free text.
  • Memory or context keys rewritten by injected content.

Open these first


Core acceptance

  • ΔS(question, retrieved) ≤ 0.45 even with injection attempts.
  • λ remains convergent across 3 paraphrases, does not flip under “ignore above” payloads.
  • Schema lock: JSON/tool calls validate against fixed schema.
  • Coverage ≥ 0.70 of target section even under noisy injection.

Fix in 60 seconds

  1. Detect abnormal ΔS drift

    • Compute ΔS(question, retrieved). If injected phrase raises ΔS ≥ 0.60, isolate payload.
  2. Enforce contracts

    • Wrap retriever and reasoner outputs in data-contracts.md.
    • Reject free text outside schema.
  3. Apply fences

  4. Verify stability

    • Re-run with paraphrase probes. Injection should not flip λ or erase citations.

Typical injection payloads → exact fix

Payload type Symptom Fix
Ignore-all override Model discards earlier rules role_confusion.md + schema locks
Citation erasure No references, only free text answer retrieval-traceability.md, data-contracts.md
Tool hijack JSON field replaced with instruction text json_mode_and_tool_calls.md
Role swap User prompt injected as “system” role_confusion.md
Memory overwrite Past state or keys corrupted memory_fences_and_state_keys.md

Copy-paste probe prompt

System: WFGY firewall active.
User input: {question}

Check:
1. Did retrieved snippet keep citations?
2. Did ΔS(question,retrieved) ≤ 0.45?
3. Did λ stay convergent under paraphrase?
4. Did JSON/tool call respect schema?

If any fail, return the failing layer + fix page.

🔗 Quick-Start Downloads (60 sec)

Tool Link 3-Step Setup
WFGY 1.0 PDF Engine Paper 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS) TXTOS.txt 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

Explore More

Layer Page What it’s for
⭐ Proof WFGY Recognition Map External citations, integrations, and ecosystem proof
⚙️ Engine WFGY 1.0 Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine WFGY 2.0 Production tension kernel for RAG and agent systems
⚙️ Engine WFGY 3.0 TXT based Singularity tension engine (131 S class set)
🗺️ Map Problem Map 1.0 Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map Problem Map 2.0 Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map Problem Map 3.0 Global AI troubleshooting atlas and failure pattern map
🧰 App TXT OS .txt semantic OS with fast bootstrap
🧰 App Blah Blah Blah Abstract and paradox Q&A built on TXT OS
🧰 App Blur Blur Blur Text to image generation with semantic control
🏡 Onboarding Starter Village Guided entry point for new users

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