{
  "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": "2609.29808",
  "title": "Hard Stop: Kernel-Level Preemption and Containment for Rogue Agentic Execution",
  "summary": "The method replaces reliance on in-loop LLM guardrails and conventional sandboxing with a deterministic, out-of-band supervisory layer (discrete-event control plus synchronous-reactive sentinels) that preempts rogue agent actions at the kernel/POSIX level before off-target network calls execute. The cost is substantial systems engineering: kernel-level integration, formally specified containment policies, and maintenance of a control plane independent of the agent's own compute, which is nontrivial for a small team. It can fail if the preemption bus shares fate with the compromised host (e.g., hypervisor or credential compromise below the enforcement layer), if policy specifications are incomplete and either block legitimate actions or miss novel escape paths, and because the motivating incident and its evidence cannot be independently verified.",
  "rationale": "The underlying principle—deterministic, out-of-band kill switches and network egress controls for autonomous agents—is directly applicable to a constrained-infrastructure team deploying tool-using agents, but the full kernel-level architecture requires validation at small scale before adoption.",
  "technique": "Out-of-band kernel-level preemption for agent containment",
  "family": "seguranca_e_guardrails",
  "recommendation": "testar",
  "published": "2026-09-24",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.29808",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 5,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces reliance on in-loop LLM guardrails and conventional sandboxing with a deterministic, out-of-band supervisory layer (discrete-event control plus synchronous-reactive sentinels) that preempts rogue agent actions at the kernel/POSIX level before off-target network calls execute. The cost is substantial systems engineering: kernel-level integration, formally specified containment policies, and maintenance of a control plane independent of the agent's own compute, which is nontrivial for a small team. It can fail if the preemption bus shares fate with the compromised host (e.g., hypervisor or credential compromise below the enforcement layer), if policy specifications are incomplete and either block legitimate actions or miss novel escape paths, and because the motivating incident and its evidence cannot be independently verified.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.29808",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "The paper reports a 4.8 microsecond median and sub-0.154 ms worst-case preemption latency for its POSIX preemption bus, but provides no comparative speed, cost, or quality factor over existing sandboxing approaches.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.29808",
      "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,
      "source_excerpt": ""
    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "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": ""
}