{
  "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": "2610.31372",
  "title": "RIDAR: Safeguarding Research Integrity in Autonomous Research Agents via Automated Detection and Runtime Intervention",
  "summary": "RIDAR replaces manual or absent auditing of autonomous research-agent trajectories with a taxonomy-driven post-hoc detector (14 violation types) and a runtime monitor that intervenes mid-execution. It costs an additional LLM-based detection layer over every agent trajectory, which increases token spend and latency and requires intervention logic wired into the agent loop; the benchmark is small (60 annotated trajectories, 300 wild trajectories from a niche domain). After adoption, the detector can fail through false positives that interrupt valid agent behavior, false negatives on violation types underrepresented in the taxonomy, and distribution shift if the reader's agents differ from the research-agent setting studied.",
  "rationale": "The framework targets integrity auditing of autonomous scientific-research agents, a specialized deployment context, and its evidence base is a small benchmark, so it merits monitoring rather than direct production adoption by a general practitioner.",
  "technique": "Runtime integrity violation detection for research agents",
  "family": "seguranca_e_guardrails",
  "recommendation": "observar",
  "published": "2026-10-22",
  "as_of": "2026-10-23",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.31372",
  "independent_implementations": 3,
  "total_implementations": 4,
  "stars": 136,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "RIDAR replaces manual or absent auditing of autonomous research-agent trajectories with a taxonomy-driven post-hoc detector (14 violation types) and a runtime monitor that intervenes mid-execution. It costs an additional LLM-based detection layer over every agent trajectory, which increases token spend and latency and requires intervention logic wired into the agent loop; the benchmark is small (60 annotated trajectories, 300 wild trajectories from a niche domain). After adoption, the detector can fail through false positives that interrupt valid agent behavior, false negatives on violation types underrepresented in the taxonomy, and distribution shift if the reader's agents differ from the research-agent setting studied.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.31372",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "85.2% F1 on the RIV-Gold detection benchmark (60 expert-annotated trajectories); RIDAR-runtime reduces research integrity violations remaining at task completion by 36.8%.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.31372",
      "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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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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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": ""
}