{
  "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": "2609.24446",
  "title": "ActGov: Governing LLM Agent Actions via Policy-Constrained Validation",
  "summary": "ActGov replaces static, predefined execution plans and fixed allowlists with a runtime enforcement layer that validates every proposed tool call against an iteratively constructed, SMT-verified policy set before external effects occur. It costs the engineering effort of building and maintaining tool specifications and policy records, plus per-action validation latency (including SMT counterexample checks during policy construction) added to each step of the agent loop. It can fail where tool specifications are incomplete or policies under-constrain authorization, where the policy-construction pipeline misses failure modes not present in observed traces, and where teams lack the verification tooling and expertise to maintain the SMT-backed policy lifecycle.",
  "rationale": "For an engineer deploying tool-calling agents against prompt injection, per-action runtime validation is directly relevant, but the abstract provides no quantified overhead or attack-reduction figures, and the SMT-based policy construction pipeline demands validation of its operational burden on the reader's specific workflow before production adoption.",
  "technique": "Runtime policy validation of LLM agent tool actions",
  "family": "seguranca_e_guardrails",
  "recommendation": "testar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24446",
  "independent_implementations": 4,
  "total_implementations": 4,
  "stars": 3,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "ActGov replaces static, predefined execution plans and fixed allowlists with a runtime enforcement layer that validates every proposed tool call against an iteratively constructed, SMT-verified policy set before external effects occur. It costs the engineering effort of building and maintaining tool specifications and policy records, plus per-action validation latency (including SMT counterexample checks during policy construction) added to each step of the agent loop. It can fail where tool specifications are incomplete or policies under-constrain authorization, where the policy-construction pipeline misses failure modes not present in observed traces, and where teams lack the verification tooling and expertise to maintain the SMT-backed policy lifecycle.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.24446",
      "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": ""
}