{
  "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": "2609.26316",
  "title": "Design and Evaluation of a Controlled Post-Alert Incident Orchestration and Response Subsystem Using a Rule Engine and a Local Large Language Model",
  "summary": "The method replaces free-form or monolithic LLM-driven incident response with a pipeline where a deterministic rule engine handles severity classification and playbook selection, and a local LLM only supplies advisory content behind validator, guardrail, sanitizer, and safe-fallback controls. The cost is substantial engineering complexity (rule engine, durable queue, static RAG, multiple control layers, contention limiting) plus roughly 33 seconds of mean post-alert processing latency, with no measured accuracy or quality gain over alternatives. What can fail in production: the rule matrix can misroute alerts outside the 30 tested boundary cases, the advisory model's outputs are validated only in a simulated laboratory scope, and the single-request concurrency limit becomes a bottleneck under real alert volumes.",
  "rationale": "The deterministic-routing-plus-guarded-LLM pattern is sound and feasible on a single GPU, but the paper offers only laboratory functional-correctness results with no comparative performance evidence, so the reader should validate it on their own alert workload before production adoption.",
  "technique": "Rule engine with guarded local LLM for incident response",
  "family": "seguranca_e_guardrails",
  "recommendation": "testar",
  "published": "2026-09-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.26316",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces free-form or monolithic LLM-driven incident response with a pipeline where a deterministic rule engine handles severity classification and playbook selection, and a local LLM only supplies advisory content behind validator, guardrail, sanitizer, and safe-fallback controls. The cost is substantial engineering complexity (rule engine, durable queue, static RAG, multiple control layers, contention limiting) plus roughly 33 seconds of mean post-alert processing latency, with no measured accuracy or quality gain over alternatives. What can fail in production: the rule matrix can misroute alerts outside the 30 tested boundary cases, the advisory model's outputs are validated only in a simulated laboratory scope, and the single-request concurrency limit becomes a bottleneck under real alert volumes.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.26316",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
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    },
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      "dimension": "compute",
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    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "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": "",
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      "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": ""
}