{
  "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": "2609.26529",
  "title": "From Approval to Execution: Reconstruction-Aware Repair Analysis for LLM-Agent Software",
  "summary": "It replaces field-flow and check-coverage analyses that verify an approval record or inspected representation but cannot prove the checked object version is the one consumed at the execution sink. The cost is analysis and engineering effort: modeling representation transitions, sink dependencies, consumed versions, and grant scope, plus an object-flow, dominance, and interference setup comparable to the CodeQL composite, with analyst judgment (19/20 sink-dependency agreement). It can fail where the grant contract or sink dependencies are mis-specified, in codebases whose reconstruction paths (reload, transcript projection, argument rebinding, durable-state lookup) fall outside the modeled transitions, and its evidence base is small (28 controlled outcomes, four matched pairs, five repairs).",
  "rationale": "It addresses a real authorization-bypass class in LLM-agent systems and runs as static analysis without GPU or training cost, but adoption requires workload-specific modeling of sink dependencies and grant contracts before its sufficiency judgments can be trusted in a given codebase.",
  "technique": "Reconstruction-aware authorization repair analysis (ReSA/APAS-Finder)",
  "family": "seguranca_e_guardrails",
  "recommendation": "testar",
  "published": "2026-09-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.26529",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "It replaces field-flow and check-coverage analyses that verify an approval record or inspected representation but cannot prove the checked object version is the one consumed at the execution sink. The cost is analysis and engineering effort: modeling representation transitions, sink dependencies, consumed versions, and grant scope, plus an object-flow, dominance, and interference setup comparable to the CodeQL composite, with analyst judgment (19/20 sink-dependency agreement). It can fail where the grant contract or sink dependencies are mis-specified, in codebases whose reconstruction paths (reload, transcript projection, argument rebinding, durable-state lookup) fall outside the modeled transitions, and its evidence base is small (28 controlled outcomes, four matched pairs, five repairs).",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.26529",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Predictions agree with all 28 controlled outcomes; five repairs on released consumers prevent 60/60 tested out-of-scope effects, while three mechanism contrasts expose predicted residual bypasses.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.26529",
      "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": ""
}