{
  "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": "2609.30657",
  "title": "Prompt Injection Detection for Email Agents Through Attack Chain Modeling",
  "summary": "The framework replaces single-stage binary malicious-text classification with a multi-component pipeline: a text detector, per-stage verifiers, rule-based risk signals, user-intent/action consistency checks, and a logistic decision policy. It costs training data with derived attack-chain labels (including hard benign examples), integration of several verification stages into the agent's tool-use path, and added latency per screened email. It can fail under distribution shift (the paper shows random splits substantially overestimate robustness), early attack stages are less predictable than tool-argument stages, and the absolute F1 of 0.406 under the strict policy leaves both missed attacks and false alarms in deployment.",
  "rationale": "The staged consistency checks and hard-benign training are implementable on constrained infrastructure and address a real production risk for email agents, but the modest absolute F1 and demonstrated transfer degradation require workload-specific validation before adoption.",
  "technique": "Attack-chain modeling for prompt injection detection",
  "family": "seguranca_e_guardrails",
  "recommendation": "testar",
  "published": "2026-09-25",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.30657",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 11,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The framework replaces single-stage binary malicious-text classification with a multi-component pipeline: a text detector, per-stage verifiers, rule-based risk signals, user-intent/action consistency checks, and a logistic decision policy. It costs training data with derived attack-chain labels (including hard benign examples), integration of several verification stages into the agent's tool-use path, and added latency per screened email. It can fail under distribution shift (the paper shows random splits substantially overestimate robustness), early attack stages are less predictable than tool-argument stages, and the absolute F1 of 0.406 under the strict policy leaves both missed attacks and false alarms in deployment.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.30657",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Mean F1 of 0.406 across five binary benchmarks under a strict threshold policy, versus 0.216 for the strongest of five pretrained detectors evaluated without additional training.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.30657",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compute",
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      "finding": "",
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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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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "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": ""
}