{
  "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": "2610.19498",
  "title": "What You Sign Is Not What You Run: The Verifiability Gap in Quantized LLM Supply Chains",
  "summary": "Replaces trusting a downloaded GGUF solely via its full-precision weight signature with a protocol that re-derives the quantized artifact bit-for-bit from disclosed inputs, requiring the builder to record and ship the floating-point-contraction setting, per-tensor quantization recipes, and a hash-bound calibration imatrix. It costs negligible model quality (+0.06% perplexity from the unique-output compiler setting), adds build-provenance bookkeeping, and requires the verifier to rerun quantization locally on a 24 GB GPU, which is feasible for GGUF-class models. It fails when providers omit build-environment coordinates (currently universal: IVR 0/198), against targeted embedding edits that evade divergence statistics, and against adversarial-fp16 attacks, which lie outside the protocol.",
  "rationale": "Engineers deploying third-party quantized models on constrained hardware currently have no way to verify the artifact they run matches the signed weights, and this paper supplies a concrete, low-cost re-derivation checklist they can demand from providers and apply in their own release pipelines.",
  "technique": "Bit-exact re-derivation auditing of quantized models",
  "family": "seguranca_e_guardrails",
  "recommendation": "adotar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.19498",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Replaces trusting a downloaded GGUF solely via its full-precision weight signature with a protocol that re-derives the quantized artifact bit-for-bit from disclosed inputs, requiring the builder to record and ship the floating-point-contraction setting, per-tensor quantization recipes, and a hash-bound calibration imatrix. It costs negligible model quality (+0.06% perplexity from the unique-output compiler setting), adds build-provenance bookkeeping, and requires the verifier to rerun quantization locally on a 24 GB GPU, which is feasible for GGUF-class models. It fails when providers omit build-environment coordinates (currently universal: IVR 0/198), against targeted embedding edits that evade divergence statistics, and against adversarial-fp16 attacks, which lie outside the protocol.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.19498",
      "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": ""
}