{
  "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": "2609.19705",
  "title": "SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemes",
  "summary": "FARSIGHT replaces ad-hoc or absent red-teaming of financial LLM trading agents with a scheme-level evaluation harness covering robustness under market turbulence and three attack classes: compromised information sources, direct attacks on agents, and agent-as-attacker behaviors. The cost is integration effort: adapting one's agent to the harness, running turbulence and adversarial scenarios, and remediating findings, with no claim that it reduces inference cost or latency. It can fail by producing false assurance, since passing its scenarios does not guarantee safety against novel adversaries or live-market conditions outside the tested distributions, and the paper itself is an evaluation study rather than a deployable defense.",
  "rationale": "The work is an evaluation framework and SoK specific to financial trading agents, not an efficiency or capability technique the reader can adopt for a quantified gain, so it is worth monitoring only if the reader deploys agents with real execution authority.",
  "technique": "FARSIGHT: security and robustness evaluation of financial LLM trading agents",
  "family": "seguranca_e_guardrails",
  "recommendation": "observar",
  "published": "2026-09-17",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.19705",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 7,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "FARSIGHT replaces ad-hoc or absent red-teaming of financial LLM trading agents with a scheme-level evaluation harness covering robustness under market turbulence and three attack classes: compromised information sources, direct attacks on agents, and agent-as-attacker behaviors. The cost is integration effort: adapting one's agent to the harness, running turbulence and adversarial scenarios, and remediating findings, with no claim that it reduces inference cost or latency. It can fail by producing false assurance, since passing its scenarios does not guarantee safety against novel adversaries or live-market conditions outside the tested distributions, and the paper itself is an evaluation study rather than a deployable defense.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.19705",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
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      "finding": "",
      "interpretation": "",
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      "next_check": "",
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    },
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    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
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      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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      "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": ""
}