{
  "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": "2610.06122",
  "title": "Benchmarking Jailbreak Guardrails for Embodied Agents",
  "summary": "The paper replaces ad hoc, per-model safety evaluation with a pluggable framework that fixes the embodied agent backend and benchmarks six guardrails intervening at the perception, planning, or control stage under identical attacks. It contributes evaluation methodology rather than a new defense, so adopting it costs integration effort against a simulator plus the runtime latency of whichever guardrail is selected, and the central finding is a trade-off: no guardrail dominates across defense effectiveness, false-positive rate, and latency. The findings can fail to transfer because results are simulator-based and specific to the six tested methods, so bypass and hazard rates may shift against stronger adaptive attacks or on real hardware.",
  "rationale": "Unless the reader deploys LLM/VLM-driven embodied agents, this benchmark offers only indirect design guidance, and its conclusions are qualitative trade-offs rather than a deployable component, so it merits monitoring rather than adoption or hands-on testing.",
  "technique": "Benchmark of jailbreak guardrails for embodied agents",
  "family": "seguranca_e_guardrails",
  "recommendation": "observar",
  "published": "2026-10-05",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.06122",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 345,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The paper replaces ad hoc, per-model safety evaluation with a pluggable framework that fixes the embodied agent backend and benchmarks six guardrails intervening at the perception, planning, or control stage under identical attacks. It contributes evaluation methodology rather than a new defense, so adopting it costs integration effort against a simulator plus the runtime latency of whichever guardrail is selected, and the central finding is a trade-off: no guardrail dominates across defense effectiveness, false-positive rate, and latency. The findings can fail to transfer because results are simulator-based and specific to the six tested methods, so bypass and hazard rates may shift against stronger adaptive attacks or on real hardware.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.06122",
      "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",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "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": ""
}