{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent reads external documents or tool responses that may contain malicious instructions.",
    "change": "Replay attacks and legitimate lookalikes in an isolated copy of your agent workflow.",
    "baseline": "Your existing checks with the same tasks, permissions, and tool responses.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Successful attacks missed, legitimate actions blocked, detection delay, and review cost.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.20776",
  "title": "INFUSE: A Unified Surface Benchmark for Indirect Prompt Injection in Agentic Systems",
  "summary": "INFUSE replaces ad hoc or single-turn prompt-injection tests with a 40-task, 56-attack benchmark that evaluates injection across full long-horizon agent trajectories including shell, code, and remote-service surfaces. The cost is evaluation compute (thousands of trajectories) and, if the prompt-hardening mitigation is adopted, a model-dependent security-utility trade-off that can degrade benign task performance. It can fail to transfer if the team's agent tools, surfaces, or task domains differ from the benchmark's 40 tasks, and heuristic versus LLM-based judgment (15.4% vs 19.6% ASR) shows measurement itself is unstable.",
  "rationale": "The reader running production agents gains actionable evidence that cheap prompt hardening is the most accessible effective defense, but should validate it on their own workflows rather than trusting benchmark numbers wholesale.",
  "technique": "End-to-end indirect prompt injection benchmark (INFUSE)",
  "family": "seguranca_e_guardrails",
  "recommendation": "testar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20776",
  "independent_implementations": 3,
  "total_implementations": 3,
  "stars": 1547,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "INFUSE replaces ad hoc or single-turn prompt-injection tests with a 40-task, 56-attack benchmark that evaluates injection across full long-horizon agent trajectories including shell, code, and remote-service surfaces. The cost is evaluation compute (thousands of trajectories) and, if the prompt-hardening mitigation is adopted, a model-dependent security-utility trade-off that can degrade benign task performance. It can fail to transfer if the team's agent tools, surfaces, or task domains differ from the benchmark's 40 tasks, and heuristic versus LLM-based judgment (15.4% vs 19.6% ASR) shows measurement itself is unstable.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.20776",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Prompt-level instructional hardening reduces mean attack success from 58.2% to 14.3% (about 4x reduction in ASR), while guard models offer limited protection; baseline mean ASR is 19.6% across 19 models.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.20776",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compute",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}