{ "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": "2609.31358", "title": "A Safety-Bounded SDC-to-MCP Gateway for Medical AI Agents", "summary": "The method replaces ad-hoc or generic exposure of medical device state and actions to language-model agents with a gateway that serves device metrics and alarms as read-only MCP resources and exposes actions only as policy-validated dry-run tools that never dispatch device operations. The cost is integration and maintenance of a domain-specific gateway, policy definitions, and semantic metadata layers, plus dependence on the IEEE 11073 SDC ecosystem, with no inference-speed or memory benefit. After adoption it can fail through structured-output non-compliance (plausible narrative answers that violate required machine-readable schemas), and the evidence comes from a Python prototype with simulated fault and lifecycle experiments, so behavior against real device fleets and regulatory review remains unvalidated.", "rationale": "The work is a safety architecture prototype specific to IEEE 11073 medical device integration with no quantified efficiency gain, so it is relevant to track for agent tool-exposure design but not yet an adoptable production component for a constrained-infrastructure team.", "technique": "Safety-bounded SDC-to-MCP gateway for medical agents", "family": "seguranca_e_guardrails", "recommendation": "observar", "published": "2026-09-25", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2609.31358", "independent_implementations": 0, "total_implementations": 1, "stars": 10, "citations": null, "claims": [ { "claim_id": "claim-01", "statement": "The method replaces ad-hoc or generic exposure of medical device state and actions to language-model agents with a gateway that serves device metrics and alarms as read-only MCP resources and exposes actions only as policy-validated dry-run tools that never dispatch device operations. 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