{
 "license": "CC-BY-4.0",
 "attribution": "Fide AI, Agentic Cyber Explorer",
 "url": "https://agentic-cyber-explorer.pages.dev/events/struq-structured-queries-2024/",
 "asOf": "2026-09-26",
 "id": "struq-structured-queries-2024",
 "date": "2024-02-09",
 "datePrecision": "day",
 "title": "StruQ proposes separating prompts and data channels to defend against prompt injection",
 "lane": "defense",
 "kind": "paper",
 "summary": "Chen, Piet, Sitawarin and Wagner propose structured queries, in which a secure front-end separates the trusted prompt from untrusted data and a model fine-tuned to ignore instructions appearing in the data portion. The paper reports much better resistance to prompt injection with little utility loss; it appeared at USENIX Security 2025.",
 "whyItMatters": "It is a training-based instruction and data separation defense that later adaptive-attack work targets.",
 "actors": [
  "uc-berkeley"
 ],
 "topics": [
  "prompt-injection"
 ],
 "atlas": [
  "model",
  "untrusted-content"
 ],
 "artifacts": [
  "struq"
 ],
 "sources": [
  {
   "url": "https://arxiv.org/abs/2402.06363",
   "publisher": "arXiv",
   "title": "StruQ: Defending Against Prompt Injection with Structured Queries",
   "date": "2024-02-09",
   "type": "primary",
   "accessed": "2026-09-25"
  }
 ],
 "keyFacts": [
  {
   "fact": "The authors report significantly improved resistance to prompt injection with little or no utility impact from structured instruction tuning.",
   "locator": "Abstract"
  }
 ],
 "significance": 3,
 "fideQuestions": [],
 "methods": [
  "indirect-prompt-injection",
  "instruction-priority-training"
 ],
 "review": "assistant-drafted",
 "addedOn": "2026-09-25"
}