{
  "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": "2610.00430",
  "title": "Memetic Trojans: Social Contagions as Carriers of Adversarial Payloads in Agent Networks",
  "summary": "The paper defines an attack class, not a deployable defense: it replaces the assumption that prompt-injection detection and compromise prevention suffice for multi-agent security, showing payloads propagate through agents' voluntary retransmission of viral content. It costs the reader nothing to adopt as a threat model, but operationalizing defenses would require network-level monitoring of agent-to-agent content flow, recommendation-mechanism auditing, and topology analysis that most small deployments lack. What can fail is transferability: virality results come from one platform (Moltbook) and Monte Carlo simulation, so propagation rates and the up-to-3.19x exposure amplification may not hold in the reader's specific agent topology.",
  "rationale": "This is a threat characterization without an implementable small-scale defense, so a constrained team should incorporate it into threat modeling and monitor for practical network-level mitigations rather than build countermeasures now.",
  "technique": "Memetic trojans via endogenous agent retransmission",
  "family": "seguranca_e_guardrails",
  "recommendation": "observar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.00430",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 15,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The paper defines an attack class, not a deployable defense: it replaces the assumption that prompt-injection detection and compromise prevention suffice for multi-agent security, showing payloads propagate through agents' voluntary retransmission of viral content. It costs the reader nothing to adopt as a threat model, but operationalizing defenses would require network-level monitoring of agent-to-agent content flow, recommendation-mechanism auditing, and topology analysis that most small deployments lack. What can fail is transferability: virality results come from one platform (Moltbook) and Monte Carlo simulation, so propagation rates and the up-to-3.19x exposure amplification may not hold in the reader's specific agent topology.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.00430",
      "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": ""
}